1.
Ballante, Flavio, et al.
(författare)
Docking Finds GPCR Ligands in Dark Chemical Matter
2020
Ingår i: Journal of Medicinal Chemistry. - : American Chemical Society (ACS). - 0022-2623 .- 1520-4804. ; 63:2, s. 613-620
Tidskriftsartikel (refereegranskat) abstract
High-throughput screening has revealed dark chemical matter, a set of drug-like compounds that has never shown bioactivity despite being extensively assayed. If dark molecules are found active at a therapeutic target, their extraordinary selectivity profiles make excellent starting points for drug development. We explored if ligands of therapeutically relevant G-protein-coupled receptors could be discovered by structure-based virtual screening of the dark chemical matter. Molecular docking screens against crystal structures of the A(2A) adenosine and the D-4 dopamine receptors were carried out, and 53 top-ranked molecules were evaluated experimentally. Two ligands of each receptor were discovered, and the most potent had sub-micromolar affinities. Analysis of bioactivity data showed that the ligands lacked activity at hundreds of off-targets, including several that are associated with adverse effects. Our results demonstrate that virtual screening provides an efficient means to mine the dark chemical space, which could contribute to development of drugs with improved safety profiles.
2.
Marshall, Garland R, et al.
(författare)
Limiting Assumptions in the Design of Peptidomimetics
2017
Ingår i: Drug development research. - : Wiley. - 0272-4391 .- 1098-2299. ; 78:6, s. 245-267
Forskningsöversikt (refereegranskat) abstract
Limiting the flexibility of organic compounds to enhance their affinity and selectivity for targeting a macromolecule involved in molecular recognition has become a well-developed paradigm in medicinal chemistry. While the role of reverse-turn motifs as peptidomimetics has received the most attention, β-sheets and helices are also important motifs for protein/protein interactions. The more complicated problem of mimicking the interacting surface of noncontiguous epitopes will not be considered in this review. This limited overview focuses on efforts to use amino acid synthons as secondary-structure mimetics as well as providing examples of peptidomimetic design focused on nonpeptide synthetic chemistry in contrast. In particular, the rationale of optimal design criteria for mimicry and the many naïve violations of those criteria made in its pursuit are emphasized.
3.
Ragno, Rino, et al.
(författare)
Structure-based modeling and target-selectivity prediction
2014
Patent (populärvet., debatt m.m.) abstract
The present invention provides, inter alia, methods, models, and systems for selecting an effector having specificity for a target molecule. The methods and systems of the present invention involve several steps, including compiling a database containing structural data for a library of molecules and a population of ligands and activity data, establishing structure-based equivalence of sequence elements in the library of molecules, determining likely spatial orientations of population ligands in library molecules, calculating interaction energies for each ligand-molecule pair, generating statistical models that are predictive of sequence elements likely to contribute to a differential effect of ligands on molecules, selecting an effector that is likely to have a desired specificity for the target molecule, experimentally determining activity data for effector-library molecule pairs, and at least once repeating the steps listed above wherein the effector is a member of the population of ligands.
4.
Kennedy, Amanda, et al.
(författare)
Structural Characterization of Agonist Binding to Protease-Activated Receptor 2 through Mutagenesis and Computational Modeling
2018
Ingår i: ACS Pharmacology & Translational Science. - : American Chemical Society (ACS). - 2575-9108. ; 1:2, s. 119-133
Tidskriftsartikel (refereegranskat) abstract
Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor that is activated by proteolytic cleavage of its N-terminus. The unmasked N-terminal peptide then binds to the transmembrane bundle, leading to activation of intracellular signaling pathways associated with inflammation and cancer. Recently determined crystal structures have revealed binding sites of PAR2 antagonists, but the binding mode of the peptide agonist remains unknown. In order to generate a model of PAR2 in complex with peptide SLIGKV, corresponding to the trypsin-exposed tethered ligand, the orthosteric binding site was probed by iterative combinations of receptor mutagenesis, agonist ligand modifications and data-driven structural modeling. Flexible-receptor docking identified a conserved binding mode for agonists related to the endogenous ligand that was consistent with the experimental data and allowed synthesis of a novel peptide (1-benzyl-1H[1,2,3]triazole-4-yl-LIGKV) with higher functional potency than SLIGKV. The final model may be used to understand the structural basis of PAR2 activation and in virtual screens to identify novel PAR2 agonist and competitive antagonists. The combined experimental and computational approach to characterize agonist binding to PAR2 can be extended to study the many other G protein-coupled receptors that recognize peptides or proteins.
5.
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.
6.
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.
7.
Ballante, Flavio, et al.
(författare)
Comprehensive model of wild-type and mutant HIV-1 reverse transciptases.
2012
Ingår i: Journal of Computer-Aided Molecular Design. - : Springer Science+Business Media B.V.. - 0920-654X .- 1573-4951. ; 26:8, s. 907-19
Tidskriftsartikel (refereegranskat) abstract
An enhanced version of COMBINE that uses both ligand-based and structure-based alignment of ligands has been used to build a comprehensive 3-D QSAR model of wild-type HIV-1 reverse transcriptase and drug-resistant mutants. The COMBINEr model focused on 7 different RT enzymes complexed with just two HIV-RT inhibitors, niverapine (NVP) and efavirenz (EFV); therefore, 14 inhibitor/enzyme complexes comprised the training set. An external test set of chiral 2-(alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones (DABOs) was used to test predictability. The COMBINEr model MC4, although developed using only two inhibitors, predicted the experimental activities of the test set with an acceptable average absolute error of prediction (0.89 pK (i)). Most notably, the model was able to correctly predict the right eudismic ratio for two R/S pairs of DABO derivatives. The enhanced COMBINEr approach was developed using only software freely available to academics.
8.
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.
9.
Ballante, Flavio, 1979-
(författare)
Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection
2018
Ingår i: Rational Drug Design. - New York, NY : Humana Press. - 9781493986293 - 9781493986309 ; , s. 67-88
Bokkapitel (refereegranskat) abstract
Main goal in drug discovery is the identification of drug-like compounds capable to modulate specific biological targets. Thus, the prediction of reliable binding poses of candidate ligands, through molecular docking simulations, represents a key step to be pursued in structure-based drug design (SBDD). Since the increasing number of resolved three-dimensional ligand-protein structures, together with the expansion of computational power and software development, the comprehensive and systematic use of experimental data can be proficiently employed to validate the docking performance. This allows to select and refine the protocol to adopt when predicting the binding pose of trial compounds in a target. Given the availability of multiple docking software, a comparative docking assessment in an early research stage represents a must-use step to minimize fails in molecular modeling. This chapter describes how to perform a docking assessment, using freely available tools, in a semiautomated fashion.
10.
Ballante, Flavio, et al.
(författare)
Structural insights of SmKDAC8 inhibitors : Targeting Schistosoma epigenetics through a combined structure-based 3D QSAR, in vitro and synthesis strategy.
2017
Ingår i: Bioorganic & Medicinal Chemistry. - : Elsevier BV. - 0968-0896 .- 1464-3391. ; 25:7, s. 2105-2132
Tidskriftsartikel (refereegranskat) abstract
A predictive structure-based 3D QSAR (COMBINEr 2.0) model of the Schistosoma mansoni lysine deacetylase 8 enzyme (SmKDAC8) was developed, validated and used to perform virtual screening (VS) of the NCI Diversity Set V database (1593 compounds). Three external datasets (with congeneric structures to those experimentally resolved in complexes by X-ray and previously reported as SmKDAC8 inhibitors) were employed to compose and validate the most predictive model. Two series characterized by 104 benzodiazepine derivatives (BZDs) and 60 simplified largazole analogs (SLAs), recently reported by our group as human KDAC inhibitors, were tested for their inhibition potency against SmKDAC8 to probe the predictive capability of the quantitative models against compounds with diverse structures. The SmKDAC8 biochemical results confirmed: (1) the benzodiazepine moiety as a valuable scaffold to further investigate when pursuing SmKDAC8 inhibition; (2) the predictive capability of the COMBINEr 2.0 model towards non-congeneric series of compounds, highlighting the most influencing ligand-protein interactions and refining the structure-activity relationships. From the VS investigations, the first 40 top-ranked compounds were obtained and biologically tested for their inhibition potency against SmKDAC8 and hKDACs 1, 3, 6 and 8. Among them, a non-hydroxamic acid benzothiadiazine dioxide derivative (code NSC163639), showed interesting activity and selectivity against SmKDAC8. To further elucidate the structure-activity relationships of NSC163639, two analogs (herein reported as compounds 3 and 4) were synthesized and biologically evaluated. Results suggest the benzothiadiazine dioxide moiety as a promising scaffold to be used in a next step to derive selective SmKDAC8 inhibitors.
