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1.
  • Abraham, Mark James, et al. (författare)
  • Sharing Data from Molecular Simulations
  • 2019
  • Ingår i: Journal of Chemical Information and Modeling. - : AMER CHEMICAL SOC. - 1549-9596 .- 1549-960X. ; 59:10, s. 4093-4099
  • Tidskriftsartikel (refereegranskat)abstract
    • Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.
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2.
  • Aftab, Obaid, 1984-, et al. (författare)
  • NMR spectroscopy based metabolic profiling of drug induced changes in vitro can discriminate between pharmacological classes
  • 2014
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:11, s. 3251-3258
  • Tidskriftsartikel (refereegranskat)abstract
    • Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.
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3.
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4.
  • Ahlström, Marie M, et al. (författare)
  • Virtual screening and scaffold hopping based on GRID molecular interaction fields.
  • 2005
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 45:5, s. 1313-23
  • Tidskriftsartikel (refereegranskat)abstract
    • In this study, a set of strategies for structure-based design using GRID molecular interaction fields (MIFs) to derive a pharmacophoric representation of a protein is reported. Thrombin, one of the key enzymes involved in the blood coagulation cascade, was chosen as the model system since abundant published experimental data are available related to both crystal structures and structurally diverse sets of inhibitors. First, a virtual screening methodology was developed either using a pharmacophore representation of the protein based on GRID MIFs or using GRID MIFs from the 3D structure of a set of chosen thrombin inhibitors. The search was done in a 3D multiconformation version of the Available Chemical Directory (ACD) database, which had been spiked with 262 known thrombin inhibitors (multiple conformers available per compound). The model managed to find 80% of the known thrombin inhibitors among the 74,291 conformers in the ACD by only searching 5% of the database; hence, a 15-fold enrichment of the library was achieved. Second, a scaffold hopping methodology was developed using GRID MIFs, giving the scaffold interaction pattern and the shape of the scaffold, together with the distance between the anchor points. The scaffolds reported by Dolle in the Journal of Combinatorial Chemistry summaries (2000 and 2001) and scaffolds built or derived from ligands cocomplexed with the thrombin enzyme were parameterized using a new set of descriptors and saved into a searchable database. The scaffold representation from the database was then compared to a template scaffold (from a thrombin crystal structure), and the thrombin-derived scaffolds included in the database were found among the top solutions. To validate the usefulness of the methodology to replace the template scaffold, the entire molecule was built (scaffold and side chains) and the resulting compounds were docked into the active site of thrombin. The docking solutions showed the same binding pattern as the cocomplexed compound, hence, showing that this method can be a valuable tool for medicinal chemists to select interchangeable core structures (scaffolds) in an easy manner and retaining the binding properties from the original ligand.
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5.
  • Alogheli, Hiba, et al. (författare)
  • Docking of Macrocycles : Comparing Rigid and Flexible Docking in Glide
  • 2017
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 57:2, s. 190-202
  • Tidskriftsartikel (refereegranskat)abstract
    • In recent years, there has been an increased interest in using macrocyclic compounds for drug discovery and development. For docking of these commonly large and flexible compounds to be addressed, a screening and a validation set were assembled from the PDB consisting of 16 and 31 macrocycle-containing protein complexes, respectively. The macrocycles were docked in Glide by rigid docking of pregenerated conformational ensembles produced by the macrocycle conformational sampling method (MCS) in Schrödinger Release 2015-3 or by direct Glide flexible docking after performing ring-templating. The two protocols were compared to rigid docking of pregenerated conformational ensembles produced by an exhaustive Monte Carlo multiple minimum (MCMM) conformational search and a shorter MCMM conformational search (MCMM-short). The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement and the poses obtained by the docking protocols. The median RMSD values for top-scored poses of the screening set were 0.83, 0.80, 0.88, and 0.58 Å for MCMM, MCMM-short, MCS, and Glide flexible docking, respectively. There was no statistically significant difference in the performance between rigid docking of pregenerated conformations produced by the MCS and direct docking using Glide flexible docking. However, the flexible docking protocol was 2-times faster in docking the screening set compared to that of the MCS protocol. In a final study, the new Prime-MCS method was evaluated in Schrödinger Release 2016-3. This method is faster compared that of to MCS; however, the conformations generated were found to be suboptimal for rigid docking. Therefore, on the basis of timing, accuracy, and ease of set up, standard Glide flexible docking with prior ring-templating is recommended over current gold standard protocols using rigid docking of pregenerated conformational ensembles.
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6.
  • Alvarsson, Jonathan, et al. (författare)
  • Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines
  • 2014
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:11, s. 3211-3217
  • Tidskriftsartikel (refereegranskat)abstract
    • QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, ?, and signature height. C is a penalty parameter that limits overfitting, ? controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and gamma in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.
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7.
  • Alvarsson, Jonathan, et al. (författare)
  • Ligand-Based Target Prediction with Signature Fingerprints
  • 2014
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:10, s. 2647-2653
  • Tidskriftsartikel (refereegranskat)abstract
    • When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.
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8.
  • Alzghoul, Ahmad, et al. (författare)
  • Experimental and Computational Prediction of Glass Transition Temperature of Drugs
  • 2014
  • Ingår i: JOURNAL OF CHEMICAL INFORMATION AND MODELING. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:12, s. 3396-3403
  • Tidskriftsartikel (refereegranskat)abstract
    • Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.
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9.
  • Amarasinghe, Kosala N., et al. (författare)
  • Virtual Screening Expands the Non-Natural Amino Acid Palette for Peptide Optimization
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62:12, s. 2999-3007
  • Tidskriftsartikel (refereegranskat)abstract
    • Peptides are an important modality in drug discovery. While current peptide optimization focuses predominantly on the small number of natural and commercially available non-natural amino acids, the chemical spaces available for small molecule drug discovery are in the billions of molecules. In the present study, we describe the development of a large virtual library of readily synthesizable non-natural amino acids that can power the virtual screening protocols and aid in peptide optimization. To that end, we enumerated nearly 380 thousand amino acids and demonstrated their vast chemical diversity compared to the 20 natural and commercial residues. Furthermore, we selected a diverse ten thousand amino acid subset to validate our virtual screening workflow on the Keap1-Neh2 complex model system. Through in silico mutations of Neh2 peptide residues to those from the virtual library, our docking-based protocol identified a number of possible solutions with a significantly higher predicted affinity toward the Keap1 protein. This protocol demonstrates that the non-natural amino acid chemical space can be massively extended and virtually screened with a reasonable computational cost.
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10.
  • Andersson, Claes R., et al. (författare)
  • In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency
  • 2007
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 47:1, s. 239-248
  • Tidskriftsartikel (refereegranskat)abstract
    • A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.
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11.
  • Andersson, David C., 1978-, et al. (författare)
  • A multivariate approach to investigate docking parameters' effects on docking performance
  • 2007
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society Publications. - 1549-9596 .- 1549-960X. ; 47:4, s. 1673-1687
  • Tidskriftsartikel (refereegranskat)abstract
    • Increasingly powerful docking programs for analyzing and estimating the strength of protein-ligand interactions have been developed in recent decades, and they are now valuable tools in drug discovery. Software used to perform dockings relies on a number of parameters that affect various steps in the docking procedure. However, identifying the best choices of the settings for these parameters is often challenging. Therefore, the settings of the parameters are quite often left at their default values, even though scientists with long experience with a specific docking tool know that modifying certain parameters can improve the results. In the study presented here, we have used statistical experimental design and subsequent regression based on root-mean-square deviation values using partial least-square projections to latent structures (PLS) to scrutinize the effects of different parameters on the docking performance of two software packages: FRED and GOLD. Protein-ligand complexes with a high level of ligand diversity were selected from the PDBbind database for the study, using principal component analysis based on 1D and 2D descriptors, and space-filling design. The PLS models showed quantitative relationships between the docking parameters and the ability of the programs to reproduce the ligand crystallographic conformation. The PLS models also revealed which of the parameters and what parameter settings were important for the docking performance of the two programs. Furthermore, the variation in docking results obtained with specific parameter settings for different protein-ligand complexes in the diverse set examined indicates that there is great potential for optimizing the parameter settings for selected sets of proteins.
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12.
  • Andersson, Patrik L, et al. (författare)
  • A Multivariate Chemical Similarity Approach to Search for Drugs of Potential Environmental Concern
  • 2011
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society. - 1549-960X .- 1549-9596. ; 51:8, s. 1788-1794
  • Tidskriftsartikel (refereegranskat)abstract
    • A structural similarity tool was developed and aimed to search for environmentally persistent drugs. The basis for the tool was a selection of so-called anchor molecules and a multidimensional chemical map of drugs. The map was constructed using principal component analysis covering 899 drugs described by 67 diverse calculated chemical descriptors. The anchor molecules (diclofenac, trimethoprim, and carbamazepine) were selected to represent drugs of known environmental concern. In addition 12 chemicals listed by the Stockholm Convention on persistent organic pollutants were used representing typical environmental pollutants. Chemical similarity was quantified by measuring relative Euclidean distances in the five-dimensional chemical map, and more than 100 nearest neighbors (kNNs) were found within a relative distance of less than 10% from each drug anchor. The developed chemical similarity approach not only identified persistent or semipersistent drugs but also a large number of potentially persistent drugs lacking environmental fate data.
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13.
  • Arvidsson McShane, Staffan, et al. (författare)
  • Machine Learning Strategies When Transitioning between Biological Assays
  • 2021
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:7, s. 3722-3733
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and Na-v assays. We suggest to continuously monitor the validity and efficiency of models as more data is accumulated from the new assay and select a modeling strategy based on these metrics. In order to maximize the utility of data from the old assay, we propose a strategy that augments the proper training set of an inductive conformal predictor by adding data from the old assay but only having data from the new assay in the calibration set, which results in valid (well-calibrated) models with improved efficiency compared to other strategies. We study the results for varying sizes of new and old assays, allowing for discussion of different practical scenarios. We also conclude that our proposed assay transition strategy is more beneficial, and the value of data from the new assay is higher, for the harder case of regression compared to classification problems.
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14.
  • Atance, Sara Romeo, et al. (författare)
  • De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 62:20, s. 4863-4872
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to finetune graph-based deep generative models for de novo molecular design tasks. We show how our computational framework can successfully guide a pretrained generative model toward the generation of molecules with a specific property profile, even when such molecules are not present in the training set and unlikely to be generated by the pretrained model. We explored the following tasks: generating molecules of decreasing/increasing size, increasing drug-likeness, and increasing bioactivity. Using the proposed approach, we achieve a model which generates diverse compounds with predicted DRD2 activity for 95% of sampled molecules, outperforming previously reported methods on this metric.
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15.
  • Avram, S., et al. (författare)
  • Off-Patent Drug Repositioning
  • 2020
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 60:12, s. 5746-5753
  • Tidskriftsartikel (refereegranskat)abstract
    • Drug repositioning aims to reuse "old" drugs to treat diseases outside their approved indication(s). Composition-of-matter patents and FDA exclusivities can hinder the immediate availability of some drugs to be repositioned (repurposed). Here, we analyze data from the FDA Orange Book and use current on-market patent validity and exclusivities to classify drugs into on-patent (ONP), off-patent (OFP), and off-market (OFM) sets. In the absence of an unanimously accepted definition for small molecules, these sets include organic molecules and peptides with molecular weight between 100 and 1250, which resulted in 237 ONP drugs, 320 OFM, and 996 OFP drugs, respectively. We discuss the differences between the three categories in terms of primary molecular properties, chemical diversity, mechanism-of-action target classes, and therapeutic areas and comment on the enrichment of OFP drugs in the near future. Given the intellectual property landscape, and in the absence of specific property rights, we suggest that drugs should be prioritized as follows, to improve the repositioning strategy: (i) OFP, (ii) OFM, and (iii) ONP, respectively.
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16.
  • Ballante, Flavio, et al. (författare)
  • 3-D QSAutogrid/R : an alternative procedure to build 3-D QSAR models. Methodologies and applications.
  • 2012
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:6, s. 1674-85
  • Tidskriftsartikel (refereegranskat)abstract
    • Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.
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17.
  • Ballante, Flavio, et al. (författare)
  • An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design.
  • 2016
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 56:1, s. 54-72
  • Tidskriftsartikel (refereegranskat)abstract
    • Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e., by determining the docking accuracy (DA)) in order to select the most appropriate docking procedure and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different root-mean-square deviation (RMSD) formulas, scoring functions, and format results, it is both difficult and time-consuming to consistently determine and compare their predictive capabilities in order to identify the best protocol to use for the target of interest and to extrapolate the binding poses (i.e., best-docked (BD), best-cluster (BC), and best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures called Clusterizer and DockAccessor have been developed and implemented for use with some common and "free-for-academics" programs such as AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC, and BF poses as well as to perform consistent cluster and DA analyses. Clusterizer and DockAccessor (code available over the Internet) represent two novel tools to collect computationally determined poses and detect the most predictive docking approach. Herein an application to human lysine deacetylase (hKDAC) inhibitors is illustrated.
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18.
  • Ballante, Flavio, et al. (författare)
  • Hsp90 inhibitors, part 1 : definition of 3-D QSAutogrid/R models as a tool for virtual screening.
  • 2014
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:3, s. 956-69
  • Tidskriftsartikel (refereegranskat)abstract
    • The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.
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19.
  • Béquignon, Olivier J. M., et al. (författare)
  • Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells
  • 2023
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 63:17, s. 5433-5445
  • Tidskriftsartikel (refereegranskat)abstract
    • Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.
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20.
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21.
  • Bertaccini, Edward J, et al. (författare)
  • Modeling Anesthetic Binding Sites within the Glycine Alpha One Receptor Based on Prokaryotic Ion Channel Templates : The Problem with TM4
  • 2010
  • Ingår i: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 50:12, s. 2248-2255
  • Tidskriftsartikel (refereegranskat)abstract
    • Ligand-gated ion channels (LGICs) significantly modulate anesthetic effects. Their exact molecular structure remains unknown. This has led to ambiguity regarding the proper amino acid alignment within their 3D structure and, in turn, the location of any anesthetic binding sites. Current controversies suggest that such a site could be located in either an intra- or intersubunit locale within the transmembrane domain of the protein. Here, we built a model of the glycine alpha one receptor (GlyRa1) based on the open-state structures of two new high-resolution ion channel templates from the prokaryote, Gloebacter violaceus (GLIC). Sequence scoring suggests reasonable homology between GlyRa1 and GLIC. Three of the residues notable for modulating anesthetic action are on transmembrane segments 1-3 (TM1-3): (ILE229, SER 267, and ALA 288). They line an intersubunit interface, in contrast to previous models. However, residues from the fourth transmembrane domain (TM4) that are known to modulate a variety of anesthetic effects are quite distant from this putative anesthetic binding site. While this model can account for a large proportion of the physicochemical data regarding such proteins, it cannot readily account for the alterations on anesthetic effects that are due to mutations within TM4.
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22.
  • Bertaccini, Edward J., et al. (författare)
  • Normal-mode analysis of the glycine alpha1 receptor by three separate methods
  • 2007
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 47:4, s. 1572-1579
  • Tidskriftsartikel (refereegranskat)abstract
    • Predicting collective dynamics and structural changes in biological macromolecules is pivotal toward a better understanding of many biological processes. Limitations due to large system sizes and inaccessible time scales have prompted the development of alternative techniques for the calculation of such motions. In this work, we present the results of a normal-mode analysis technique based on molecular mechanics that enables the calculation of accurate force-field based vibrations of extremely large molecules and compare it with two elastic network approximate models. When applied to the glycine alpha1 receptor, all three normal-mode analysis algorithms demonstrate an "iris-like" gating motion. Such gating motions have implications for understanding the effects of anesthetic and other ligand binding sites and for the means of transducing agonist binding into ion channel opening. Unlike the more approximate methods, molecular mechanics based analyses can also reveal approximate vibrational frequencies. Such analyses may someday allow the use of protein dynamics elucidated via normal-mode calculations as additional endpoints for future drug design.
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23.
  • Bhakat, Soumendranath, et al. (författare)
  • Flap Dynamics in Pepsin-Like Aspartic Proteases : A Computational Perspective Using Plasmepsin-II and BACE-1 as Model Systems
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62:4, s. 914-926
  • Tidskriftsartikel (refereegranskat)abstract
    • The flexibility of β hairpin structure known as the flap plays a key role in catalytic activity and substrate intake in pepsin-like aspartic proteases. Most of these enzymes share structural and sequential similarity. In this study, we have used apo Plm-II and BACE-1 as model systems. In the apo form of the proteases, a conserved tyrosine residue in the flap region remains in a dynamic equilibrium between the normal and flipped states through rotation of the χ1 and χ2 angles. Independent MD simulations of Plm-II and BACE-1 remained stuck either in the normal or flipped state. Metadynamics simulations using side-chain torsion angles (χ1 and χ2 of tyrosine) as collective variables sampled the transition between the normal and flipped states. Qualitatively, the two states were predicted to be equally populated. The normal and flipped states were stabilized by H-bond interactions to a tryptophan residue and to the catalytic aspartate, respectively. Further, mutation of tyrosine to an amino-acid with smaller side-chain, such as alanine, reduced the flexibility of the flap and resulted in a flap collapse (flap loses flexibility and remains stuck in a particular state). This is in accordance with previous experimental studies, which showed that mutation to alanine resulted in loss of activity in pepsin-like aspartic proteases. Our results suggest that the ring flipping associated with the tyrosine side-chain is the key order parameter that governs flap dynamics and opening of the binding pocket in most pepsin-like aspartic proteases.
  •  
24.
  • Billod, Jean-Marc, et al. (författare)
  • Structures, Properties, and Dynamics of Intermediates in eEF2-Diphthamide Biosynthesis
  • 2016
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 56:9, s. 1776-1786
  • Tidskriftsartikel (refereegranskat)abstract
    • The eukaryotic translation Elongation Factor 2 (eEF2) is an essential enzyme in protein synthesis. eEF2 contains a unique modification of a histidine (His699 in yeast; HIS) into diphthamide (DTA), obtained via 3-amino-3-carboxypropyl (ACP) and diphthine (DTI) intermediates in the biosynthetic pathway. This essential and unique modification is also vulnerable, in that it can be efficiently targeted by NAD(+)-dependent ADP-ribosylase toxins, such as diphtheria toxin (DT). However, none of the intermediates in the biosynthesis path is equally vulnerable against the toxins. This study aims to address the different susceptibility of DTA and its precursors against bacterial toxins. We have herein undertaken a detailed in silico study of the structural features and dynamic motion of different His699 intermediates along the diphthamide synthesis pathway (HIS, ACP, DTI, DTA). The study points out that DTA forms a strong hydrogen bond with an asparagine which might explain the ADP-ribosylation mechanism caused by the diphtheria toxin (DT). Finally, in silico mutagenesis studies were performed on the DTA modified protein, in order to hamper the formation of such a hydrogen bond. The results indicate that the mutant structure might in fact be less susceptible to attack by DT and thereby behave similarly to DTI in this respect.
  •  
25.
  • Bortot, Leandro Oliveira, et al. (författare)
  • Making Soup : Preparing and Validating Models of the Bacterial Cytoplasm for Molecular Simulation
  • 2020
  • Ingår i: Journal of Chemical Information and Modeling. - : AMER CHEMICAL SOC. - 1549-9596 .- 1549-960X. ; 60:1, s. 322-331
  • Tidskriftsartikel (refereegranskat)abstract
    • Biomolecular crowding affects the biophysical and biochemical behavior of macromolecules compared with the dilute environment in experiments on isolated proteins. Computational modeling and simulation are useful tools to study how crowding affects the structural dynamics and biological properties of macromolecules. With increases in computational power, modeling and simulation of large-scale all-atom explicit-solvent models of the prokaryote cytoplasm have now become possible. In this work, we built an atomistic model of the cytoplasm of Escherichia coli composed of 1.5 million atoms and submitted it to a total of 3 mu s of molecular dynamics simulations. The model consisted of eight different proteins representing about 50% of the cytoplasmic proteins and one type of t-RNA molecule. Properties of biomolecules under crowding conditions were compared with those from simulations of the individual compounds under dilute conditions. The simulation model was found to be consistent with experimental data about the diffusion coefficient and stability of macromolecules under crowded conditions. In order to stimulate further work, we provide a Python script and a set of files to enable other researchers to build their own E. coli cytoplasm models to address questions related to crowding.
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26.
  • Brandmaier, Stefan, et al. (författare)
  • PLS-Optimal: A stepwise D-Optimal design based on latent variables
  • 2012
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:4, s. 975-983
  • Tidskriftsartikel (refereegranskat)abstract
    • Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure–activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.
  •  
27.
  • Buendia, Ruben, et al. (författare)
  • Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors
  • 2019
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 59:3, s. 1230-1237
  • Tidskriftsartikel (refereegranskat)abstract
    • Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn - ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.
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28.
  • Buonfiglio, Rosa, et al. (författare)
  • Investigating Pharmacological Similarity by Charting Chemical Space
  • 2015
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 55:11, s. 2375-2390
  • Tidskriftsartikel (refereegranskat)abstract
    • In this study, biologically relevant areas of the chemical space were analyzed using ChemGPS-NP. This application enables comparing groups of ligands within a multidimensional space based on principle components derived from physicochemical descriptors. Also, 3D visualization of the ChemGPS-NP global map can be used to conveniently evaluate bioactive compound similarity and visually distinguish between different types or groups of compounds. To further establish ChemGPS-NP as a method to accurately represent the chemical space, a comparison with structure-based fingerprint has been performed. Interesting complementarities between the two descriptions of molecules were observed. It has been shown that the accuracy of describing molecules with physicochemical descriptors like in ChemGPS-NP is similar to the accuracy of structural fingerprints in retrieving bioactive molecules. Lastly, pharmacological similarity of structurally diverse compounds has been investigated in ChemGPS-NP space. These results further strengthen the case of using ChemGPS-NP as a tool to explore and visualize chemical space.
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29.
  • Burggraaff, Lindsey, et al. (författare)
  • Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors
  • 2020
  • Ingår i: Journal of Chemical Information and Modeling. - : AMER CHEMICAL SOC. - 1549-9596 .- 1549-960X. ; 60:9, s. 4283-4295
  • Tidskriftsartikel (refereegranskat)abstract
    • Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of <60% (at a concentration of 10 mu M) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC(50) value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.
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30.
  • Caroli, Antonia, et al. (författare)
  • Hsp90 inhibitors, part 2 : combining ligand-based and structure-based approaches for virtual screening application.
  • 2014
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:3, s. 970-7
  • Tidskriftsartikel (refereegranskat)abstract
    • Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.
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31.
  • Celma Tirado, Alberto (författare)
  • Prediction of Retention Time and Collision Cross Section (CCSH plus , CCSH-, and CCSNa plus ) of Emerging Contaminants Using Multiple Adaptive Regression Splines
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62, s. 5425-5434
  • Tidskriftsartikel (refereegranskat)abstract
    • Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of +/- 2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was +/- 4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below +/- 5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below +/- 5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.
  •  
32.
  • Chen, Dan, et al. (författare)
  • Complementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A(2A) Adenosine Receptor
  • 2013
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 53:10, s. 2701-2714
  • Tidskriftsartikel (refereegranskat)abstract
    • Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophysical screening of chemical libraries with molecular docking in FBLD against the A(2A) adenosine receptor (A(2A)AR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A(2A)AR, molecular docking against a crystal structure was used to rank the same set of molecules by their predicted affinities. Molecular docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophysical screens. In addition, three fragments that were top-ranked by molecular docking, but had not been picked up by the NMR-based method, were demonstrated to be A2AAR ligands. While biophysical approaches for fragment screening are typically limited to a few thousand compounds, the docking screen was extended to include 328,000 commercially available fragments. Twenty-two top-ranked compounds were tested in radioligand binding assays, and 14 of these were A(2A)AR ligands with K-i values ranging from 2 to 240 mu M. Optimization of fragments was guided by molecular dynamics simulations and free energy calculations. The results illuminate strengths and weaknesses of molecular docking and demonstrate that this method can serve as a valuable complementary tool to biophysical screening in FBLD.
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33.
  • Chen, Yue, et al. (författare)
  • Allosteric Effect of Nanobody Binding on Ligand-Specific Active States of the beta 2 Adrenergic Receptor
  • 2021
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:12, s. 6024-6037
  • Tidskriftsartikel (refereegranskat)abstract
    • Nanobody binding stabilizes G-protein-coupled receptors (GPCR) in a fully active state and modulates their affinity for bound ligands. However, the atomic-level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like beta 2 adrenergic receptor (beta 2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes structural features of the beta 2AR with an similar to 14 angstrom outward movement of transmembrane helix 6 and a close proximity of transmembrane (TM) helices 5 and 7, and favors the fully active-like conformation of the receptor, independent of ligand binding, in contrast to the conditions under which no intracellular binding partner is bound, in which case the receptor is only stabilized in an intermediateactive state. This activation is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intracellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding triggers tighter and stronger local communication networks between the Nb80 and the ligand-binding sites, primarily involving residues around ICL2 and the intracellular end of TM3, TM5, TM6, as well as ECL2, ECL3, and the extracellular ends of TM6 and TM7. In particular, we identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in conformations modulated by a full agonist, BI167107, and a G-protein-biased partial agonist, salmeterol, involving mainly TM1 and TM2, and TM5, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which should be taken into account in structure-based drug discovery.
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34.
  • Chen, Yue, et al. (författare)
  • Computational Insight into the Allosteric Activation Mechanism ofFarnesoid X Receptor
  • 2020
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 60:3, s. 1540-1550
  • Tidskriftsartikel (refereegranskat)abstract
    • The farnesoid X receptor (FXR) is a bile acid-sensing transcription factor with indispensable roles in regulating metabolic processes. Nowadays, FXR has become a highly promising drug target for severe liver disorders, especially nonalcoholic steatohepatitis (NASH). A recent study showed that imatinib and its analogues were able to allosterically enhance agonist-induced FXR activation and its target gene expression. However, the allosteric modulation mechanism of FXR by these compounds remains unclear. In this work, the most effective imatinib analogue, P16, was used as a probe to explore this issue by computational approaches. Our results identified one potential allosteric site surrounded by residues Ile335, Phe336, Lys338, Glu339, Leu340, and Leu348, which could efficiently accommodate P16. In addition, the long-time molecular dynamics simulations indicated that the binding of P16 could significantly decrease the fluctuation of the co-activator and enhance the communications between the endogenous ligand chenodeoxycholic acid (CDCA) and FXR. By analyzing the residue interaction network, we observed two unique communication pathways connecting P16 and CDCA through three key residues, Arg331, Ser332, and Phe336. The communications of network organization in the P16-bound complex may allow the synergistic effect of the two compounds via robust signal transmission between the binding sites and global network bridges, which coordinate allosteric transitions and modulate the receptor activity. Our study offers insights into the allosteric modulation occurring in FXR and would be helpful for discovery of new allosteric modulators targeting FXR for further clinical research.
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35.
  • Conn, Jonathan G.M., et al. (författare)
  • Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
  • 2023
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 63:4, s. 1099-1113
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets.
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36.
  • Crean, Rory M., et al. (författare)
  • Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62:3, s. 577-590
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.
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37.
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38.
  • Diaz, Lucia, et al. (författare)
  • Computational Prediction of Structure-Activity Relationships for the Binding of Aminocyclitols to beta-Glucocerebrosidase
  • 2011
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 51:3, s. 601-611
  • Tidskriftsartikel (refereegranskat)abstract
    • Glucocerebrosidase (GCase, acid beta-Glucosidase) hydrolyzes the sphingolipid glucosylceramide into glucose and ceramide. Mutations in this enzyme lead to a lipid metabolism disorder known as Gaucher disease. The design of competitive inhibitors of GCase is a promising field of research for the design of pharmacological chaperones as new therapeutic agents. Using a series of recently reported molecules with experimental binding affinities for GCase in the nanomolar to micromolar range, we here report an extensive theoretical analysis of their binding mode. On the basis of molecular docking, molecular dynamics, and binding free energy calculations using the linear interaction energy method (LIE), we provide details on the molecular interactions supporting ligand binding in the different families of compounds. The applicability of other computational approaches, such as the COMBINE methodology, is also investigated. The results show the robustness of the standard parametrization of the LIE method, which reproduces the experimental affinities with a mean unsigned error of 0.7 kcal/mol. Several structure activity relationships are established using the computational models here provided, including the identification of hot spot residues in the binding site. The models derived are envisaged as important tools in ligand-design programs for GCase inhibitors.
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39.
  • Dulko-Smith, Beata, et al. (författare)
  • Mechanistic basis for a connection between the catalytic step and slow opening dynamics of adenylate kinase
  • 2023
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 63:5, s. 1556-1569
  • Tidskriftsartikel (refereegranskat)abstract
    • Escherichia coli adenylate kinase (AdK) is a small, monomeric enzyme that synchronizes the catalytic step with the enzyme’s conformational dynamics to optimize a phosphoryl transfer reaction and the subsequent release of the product. Guided by experimental measurements of low catalytic activity in seven single-point mutation AdK variants (K13Q, R36A, R88A, R123A, R156K, R167A, and D158A), we utilized classical mechanical simulations to probe mutant dynamics linked to product release, and quantum mechanical and molecular mechanical calculations to compute a free energy barrier for the catalytic event. The goal was to establish a mechanistic connection between the two activities. Our calculations of the free energy barriers in AdK variants were in line with those from experiments, and conformational dynamics consistently demonstrated an enhanced tendency toward enzyme opening. This indicates that the catalytic residues in the wild-type AdK serve a dual role in this enzyme’s function─one to lower the energy barrier for the phosphoryl transfer reaction and another to delay enzyme opening, maintaining it in a catalytically active, closed conformation for long enough to enable the subsequent chemical step. Our study also discovers that while each catalytic residue individually contributes to facilitating the catalysis, R36, R123, R156, R167, and D158 are organized in a tightly coordinated interaction network and collectively modulate AdK’s conformational transitions. Unlike the existing notion of product release being rate-limiting, our results suggest a mechanistic interconnection between the chemical step and the enzyme’s conformational dynamics acting as the bottleneck of the catalytic process. Our results also suggest that the enzyme’s active site has evolved to optimize the chemical reaction step while slowing down the overall opening dynamics of the enzyme.
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40.
  • Edberg, Anna, et al. (författare)
  • Assessing Relative Bioactivity of Chemical Substances Using Quantitative Molecular Network Topology Analysis
  • 2012
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:5, s. 1238-1249
  • Tidskriftsartikel (refereegranskat)abstract
    • Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17 beta-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.
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41.
  • Eklund, Martin, et al. (författare)
  • Choosing Feature Selection and Learning Algorithms in QSAR
  • 2014
  • Ingår i: J CHEM INF MODEL. - Washington DC : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:3, s. 837-843
  • Tidskriftsartikel (refereegranskat)abstract
    • Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.
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42.
  • Fialková, Vendy, et al. (författare)
  • LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
  • 2022
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 62:9, s. 2046-2063
  • Tidskriftsartikel (refereegranskat)abstract
    • Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.
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43.
  • Friggeri, Laura, et al. (författare)
  • Pharmacophore assessment through 3-D QSAR : evaluation of the predictive ability on new derivatives by the application on a series of antitubercular agents.
  • 2013
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 53:6, s. 1463-74
  • Tidskriftsartikel (refereegranskat)abstract
    • Pharmacophoric mapping is a useful procedure to frame, especially when crystallographic receptor structures are unavailable as in ligand-based studies, the hypothetical site of interaction. In this study, 71 pyrrole derivatives active against M. tuberculosis were used to derive through a recent new 3-D QSAR protocol, 3-D QSAutogrid/R, several predictive 3-D QSAR models on compounds aligned by a previously reported pharmacophoric application. A final multiprobe (MP) 3-D QSAR model was then obtained configuring itself as a tool to derive pharmacophoric quantitative models. To stress the applicability of the described models, an external test set of unrelated and newly synthesized series of R-4-amino-3-isoxazolidinone derivatives found to be active at micromolar level against M. tuberculosis was used, and the predicted bioactivities were in good agreement with the experimental values. The 3-D QSAutogrid/R procedure proved to be able to correlate by a single multi-informative scenario the different activity molecular profiles thus confirming its usefulness in the rational drug design approach.
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44.
  • Gahlawat, Anuj, et al. (författare)
  • Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.
  • 2020
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 60:12, s. 5781-5793
  • Tidskriftsartikel (refereegranskat)abstract
    • The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.
  •  
45.
  • Gao, Li, et al. (författare)
  • A mechanistic hypothesis for the cytochrome P450-catalyzed cis-trans isomerization of 4-hydroxytamoxifen : an unusual redox reaction
  • 2011
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 51:9, s. 2293-2301
  • Tidskriftsartikel (refereegranskat)abstract
    • We provide a detailed description of the cis-trans isomerization of 4-hydroxytamoxifen/endoxifen catalyzed by several isoforms from the cytochrome P450 (CYP) superfamily, including CYP1B1, CYP2B6, and CYP2C19. We show that the reactions mainly involve redox processes catalyzed by CYP, DFT calculation results strongly suggest that the isomerization occurs via a cationic intermediate. The cationic cis-isomer is more than 3 kcal/mol more stable than the trans form, resulting in an easier conversion from trans-to-cis than cis-to-trans. The cis-trans isomerization is a rarely reported CYP reaction and is ascribed to the lack of a second abstractable proton on the ethenyl group of the triarylvinyl class of substrates. The cationic intermediates thus formed instead of the stable dehydrogenation products allow for isomerization to occur. As a comparison, the reactions for the tamoxifen derivatives are compared to those of other substrates, 4-hydroxyacetanilide and raloxifene, for which the stable dehydrogenation products are formed.
  •  
46.
  • Gao, Li, et al. (författare)
  • Conformational enantiomerization and estrogen receptor alpha binding of anti-cancer drug tamoxifen and its derivatives
  • 2011
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 51:2, s. 306-314
  • Tidskriftsartikel (refereegranskat)abstract
    • The anticancer drug tamoxifen (TAM) displays two chiral vinyl propeller structures, which interconvert so rapidly that the process is undetectable on the NMR time scale. In the present work, the enantiomerization processes were investigated with molecular modeling techniques. The threshold mechanisms probed at the different rings were shown to be identical, i.e., involving a synchronous three-ring flip, with a correlated rotation of the rings. In order to reveal the pharmacological profiles of the two chiral forms, we performed structural studies on the ligand binding domain of estrogen receptor alpha. (ER alpha LBD) and associated ligands. The enantiomers, with opposite torsional twist, were found to be discriminated by ER alpha. For TAM and its main metabolites, the effects of the stereoselectivity of ER alpha are overcome by the low energy cost for helical inversion between the two torsional enantiomers, estimated to be similar to 3 kcal/mol.
  •  
47.
  • Gao, Ya, et al. (författare)
  • CHARMM-GUI Supports Hydrogen Mass Repartitioning and Different Protonation States of Phosphates in Lipopolysaccharides
  • 2021
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:2, s. 831-839
  • Tidskriftsartikel (refereegranskat)abstract
    • Hydrogen mass repartitioning (HMR) that permits time steps of allatom molecular dynamics simulation up to 4 fs by increasing the mass of hydrogen atoms has been used in protein and phospholipid bilayers simulations to improve conformational sampling. Molecular simulation input generation via CHARMM-GUI now supports HMR for diverse simulation programs. In addition, considering ambiguous pH at the bacterial outer membrane surface, different protonation states, either -2e or -1e, of phosphate groups in lipopolysaccharides (LPS) are also supported in CHARMM-GUI LPS Modeler. To examine the robustness of HMR and the influence of protonation states of phosphate groups on LPS bilayer properties, eight different LPS-type all-atom systems with two phosphate protonation states are modeled and simulated utilizing both OpenMM 2-fs (standard) and 4-fs (HMR) schemes. Consistency in the conformational space sampled by standard and HMR simulations shows the reliability of HMR even in LPS, one of the most complex biomolecules. For systems with different protonation states, similar conformations are sampled with a PO41- or PO(4)(-)(2)group, but different phosphate protonation states make slight impacts on lipid packing and conformational properties of LPS acyl chains. Systems with PO41- have a slightly smaller area per lipid and thus slightly more ordered lipid A acyl chains compared to those with PO42-, due to more electrostatic repulsion between PO42- even with neutralizing Ca2+ ions. HMR and different protonation states of phosphates of LPS available in CHARMM-GUI are expected to be useful for further investigations of biological systems of diverse origin.
  •  
48.
  • Gao, Ya, et al. (författare)
  • Modeling and Simulation of Bacterial Outer Membranes with Lipopolysaccharides and Capsular Polysaccharides
  • 2023
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 63:5, s. 1592-1601
  • Tidskriftsartikel (refereegranskat)abstract
    • Capsule is one of the common virulence factors in Gram-negative bacteria protecting pathogens from host defenses and consists of long-chain capsular polysaccharides (CPS) anchored in the outer membrane (OM). Elucidating structural properties of CPS is important to understand its biological functions as well as the OM properties. However, the outer leaflet of the OM in current simulation studies is represented exclusively by LPS due to the complexity and diversity of CPS. In this work, representative Escherichia coli CPS, KLPS (a lipid A-linked form) and KPG (a phosphatidylglycerol-linked form), are modeled and incorporated into various symmetric bilayers with co-existing LPS in different ratios. All-atom molecular dynamics simulations of these systems have been conducted to characterize various bilayer properties. Incorporation of KLPS makes the acyl chains of LPS more rigid and ordered, while incorporation of KPG makes them less ordered and flexible. These results are consistent with the calculated area per lipid (APL) of LPS, in which the APL of LPS becomes smaller when KLPS is incorporated, whereas it gets larger when KPG is included. Torsional analysis reveals that the influence of the CPS presence on the conformational distributions of the glycosidic linkages of LPS is small, and minor differences are also detected for the inner and outer regions of the CPS. Combined with previously modeled enterobacterial common antigens (ECAs) in the form of mixed bilayers, this work provides more realistic OM models as well as the basis for characterization of interactions between the OM and OM proteins.
  •  
49.
  • Garcia de Lomana, Marina, et al. (författare)
  • ChemBioSim : Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities
  • 2021
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:7, s. 3255-3272
  • Tidskriftsartikel (refereegranskat)abstract
    • Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.
  •  
50.
  • Ge, Yunhui, et al. (författare)
  • Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2
  • 2021
  • Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:5, s. 2353-2367
  • Tidskriftsartikel (refereegranskat)abstract
    • Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov state model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long time scales. Such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to mimic the p53 transactivation domain and competitively bind mouse double minute 2 homologue (MDM2). Disrupting the MDM2–p53 interaction is a therapeutic strategy to prevent degradation of the p53 tumor suppressor in cancer cells. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 transactivation domain (TAD) binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress toward predicting accurate off rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.
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