| 1. |
- Shamovsky, I., et al.
(författare)
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Theoretical studies of chemical reactivity of metabolically activated forms of aromatic amines towards DNA.
- 2012
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Ingår i: Chemical Research in Toxicology. - : American Chemical Society (ACS). - 0893-228X .- 1520-5010. ; 25:10, s. 2236-2252
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Tidskriftsartikel (refereegranskat)abstract
- The metabolism of aromatic and heteroaromatic amines (ArNH2) results in nitrenium ions (ArNH+) that modify nucleobases of DNA, primarily deoxyguanosine (dG), by forming dG-C8 adducts. The activated amine nitrogen in ArNH+ reacts with the C8 of dG, which gives rise to mutations in DNA. For the most mutagenic ArNH2, including the majority of known genotoxic carcinogens, the stability of ArNH+ is of intermediate magnitude. To understand the origin of this observation as well as the specificity of reactions of ArNH+ with guanines in DNA, we investigated the chemical reactivity of the metabolically activated forms of ArNH2, that is, ArNHOH and ArNHOAc, toward 9-methylguanine by DFT calculations. The chemical reactivity of these forms is determined by the rate constants of two consecutive reactions leading to cationic guanine intermediates. The formation of ArNH+ accelerates with resonance stabilization of ArNH+, whereas the formed ArNH+ reacts with guanine derivatives with the constant diffusion-limited rate until the reaction slows down when ArNH+ is about 20 kcal/mol more stable than PhNH+. At this point, ArNHOH and ArNHOAc show maximum reactivity. The lowest activation energy of the reaction of ArNH+ with 9-methylguanine corresponds to the charge-transfer π-stacked transition state (π-TS) that leads to the direct formation of the C8 intermediate. The predicted activation barriers of this reaction match the observed absolute rate constants for a number of ArNH+. We demonstrate that the mutagenic potency of ArNH2 correlates with the rate of formation and the chemical reactivity of the metabolically activated forms toward the C8 atom of dG. On the basis of geometric consideration of the π-TS complex made of genotoxic compounds with long aromatic systems, we propose that precovalent intercalation in DNA is not an essential step in the genotoxicity pathway of ArNH2. The mechanism-based reasoning suggests rational design strategies to avoid genotoxicity of ArNH2 primarily by preventing N-hydroxylation of ArNH2.
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| 2. |
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| 3. |
- Amarasinghe, Kosala N., et al.
(författare)
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Virtual Screening Expands the Non-Natural Amino Acid Palette for Peptide Optimization
- 2022
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 62:12, s. 2999-3007
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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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| 4. |
- Johansson, Simon, 1994, et al.
(författare)
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
- 2022
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 41:12
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Tidskriftsartikel (refereegranskat)abstract
- Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.
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| 5. |
- Over, Bjorn, et al.
(författare)
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Structural and conformational determinants of macrocycle cell permeability
- 2016
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Ingår i: Nature Chemical Biology. - New York : Nature Publishing Group. - 1552-4450 .- 1552-4469. ; 12:12, s. 1065-1074
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Tidskriftsartikel (refereegranskat)abstract
- Macrocycles are of increasing interest as chemical probes and drugs for intractable targets like protein-protein interactions, but the determinants of their cell permeability and oral absorption are poorly understood. To enable rational design of cell-permeable macrocycles, we generated an extensive data set under consistent experimental conditions for more than 200 nonpeptidic, de novo-designed macrocycles from the Broad Institute's diversity-oriented screening collection. This revealed how specific functional groups, substituents and molecular properties impact cell permeability. Analysis of energy-minimized structures for stereo- and regioisomeric sets provided fundamental insight into how dynamic, intramolecular interactions in the 3D conformations of macrocycles may be linked to physicochemical properties and permeability. Combined use of quantitative structure-permeability modeling and the procedure for conformational analysis now, for the first time, provides chemists with a rational approach to design cell-permeable non-peptidic macrocycles with potential for oral absorption.
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