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Wang B, Yan C, Lou S, Emani P, Li B, Xu M, Kong X, Meyerson W, Yang YT, Lee D, Gerstein M. Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions. Structure 2019; 27:1469-1481.e3. [PMID: 31279629 DOI: 10.1016/j.str.2019.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 02/14/2019] [Accepted: 06/03/2019] [Indexed: 11/17/2022]
Abstract
A key issue in drug design is how population variation affects drug efficacy by altering binding affinity (BA) in different individuals, an essential consideration for government regulators. Ideally, we would like to evaluate the BA perturbations of millions of single-nucleotide variants (SNVs). However, only hundreds of protein-drug complexes with SNVs have experimentally characterized BAs, constituting too small a gold standard for straightforward statistical model training. Thus, we take a hybrid approach: using physically based calculations to bootstrap the parameterization of a full model. In particular, we do 3D structure-based docking on ∼10,000 SNVs modifying known protein-drug complexes to construct a pseudo gold standard. Then we use this augmented set of BAs to train a statistical model combining structure, ligand and sequence features and illustrate how it can be applied to millions of SNVs. Finally, we show that our model has good cross-validated performance (97% AUROC) and can also be validated by orthogonal ligand-binding data.
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Affiliation(s)
- Bo Wang
- Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Prashant Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - William Meyerson
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Yale School of Medicine, Yale University, New Haven, CT 06520, USA
| | - Yucheng T Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Donghoon Lee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA.
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Chen J, Wang B, Regan L, Gerstein M. Intensification: A Resource for Amplifying Population-Genetic Signals with Protein Repeats. J Mol Biol 2016; 429:435-445. [PMID: 27939289 DOI: 10.1016/j.jmb.2016.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 11/16/2016] [Accepted: 12/03/2016] [Indexed: 11/16/2022]
Abstract
Large-scale genome sequencing holds great promise for the interpretation of protein structures through the discovery of many, rare functional variants in the human population. However, because protein-coding regions are under high selective constraints, these variants occur at low frequencies, such that there is often insufficient statistics for downstream calculations. To address this problem, we develop the Intensification approach, which uses the modular structure of repeat protein domains to amplify signals of selection from population genetics and traditional interspecies conservation. In particular, we are able to aggregate variants at the codon level to identify important positions in repeat domains that show strong conservation signals. This allows us to compare conservation over different evolutionary timescales. It also enables us to visualize population-genetic measures on protein structures. We make available the Intensification results as an online resource (http://intensification.gersteinlab.org) and illustrate the approach through a case study on the tetratricopeptide repeat.
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Affiliation(s)
- Jieming Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Bo Wang
- Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | - Lynne Regan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA.
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Kumar S, Clarke D, Gerstein M. Localized structural frustration for evaluating the impact of sequence variants. Nucleic Acids Res 2016; 44:10062-10073. [PMID: 27915290 PMCID: PMC5137452 DOI: 10.1093/nar/gkw927] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 09/30/2016] [Accepted: 10/14/2016] [Indexed: 12/13/2022] Open
Abstract
Population-scale sequencing is increasingly uncovering large numbers of rare single-nucleotide variants (SNVs) in coding regions of the genome. The rarity of these variants makes it challenging to evaluate their deleteriousness with conventional phenotype-genotype associations. Protein structures provide a way of addressing this challenge. Previous efforts have focused on globally quantifying the impact of SNVs on protein stability. However, local perturbations may severely impact protein functionality without strongly disrupting global stability (e.g. in relation to catalysis or allostery). Here, we describe a workflow in which localized frustration, quantifying unfavorable local interactions, is employed as a metric to investigate such effects. Using this workflow on the Protein Databank, we find that frustration produces many immediately intuitive results: for instance, disease-related SNVs create stronger changes in localized frustration than non-disease related variants, and rare SNVs tend to disrupt local interactions to a larger extent than common variants. Less obviously, we observe that somatic SNVs associated with oncogenes and tumor suppressor genes (TSGs) induce very different changes in frustration. In particular, those associated with TSGs change the frustration more in the core than the surface (by introducing loss-of-function events), whereas those associated with oncogenes manifest the opposite pattern, creating gain-of-function events.
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Affiliation(s)
- Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
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Xue Y, Guo H, Hillertz P. Fragment Screening of RORγt Using Cocktail Crystallography: Identification of Simultaneous Binding of Multiple Fragments. ChemMedChem 2016; 11:1881-5. [PMID: 27432277 DOI: 10.1002/cmdc.201600242] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 06/17/2016] [Indexed: 01/01/2023]
Abstract
The retinoic-acid-related orphan receptor γ t (RORγt), as a master regulator of Th17 cell pathology, has become an attractive target for small-molecule drug discovery for the treatment of Th17-cell-related autoimmune diseases. A crystallographic fragment screening was carried out for RORγt using the ligand binding domain. An overall hit rate of 5.5 % was obtained by screening 384 compounds in 96 cocktails. Five distinct hotspots were identified, and four regions of anchoring polar interactions were observed. In addition, significant induced fit was found for the binding of several fragments. Strikingly, a simultaneous binding of three fragments was revealed which presents interesting features including π-π stacking, multiple hydrogen bonds to the protein, and significant induced fit. Overall, the results offer a complete mapping of the ligand binding pocket and provide valuable inspiration in structure-based design for RORγt lead generation and optimization. The crystallographic screening also resulted in fragment hits that bind at the surface away from the ligand binding pocket. This surface site is near the plausible dimer interface by analogy with other nuclear receptor systems, which can provide initial hints to explore alternative ways to modulate RORγt through protein-protein interactions.
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Affiliation(s)
- Yafeng Xue
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.
| | - Hongwei Guo
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden
| | - Per Hillertz
- Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden.,Research and Development Information, AstraZeneca Gothenburg, Pepparedsleden 1, 431 83, Mölndal, Sweden
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Clarke D, Sethi A, Li S, Kumar S, Chang RWF, Chen J, Gerstein M. Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-species Conservation. Structure 2016; 24:826-837. [PMID: 27066750 PMCID: PMC4883016 DOI: 10.1016/j.str.2016.03.008] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 01/01/2016] [Accepted: 03/04/2016] [Indexed: 01/17/2023]
Abstract
The rapidly growing volume of data being produced by next-generation sequencing initiatives is enabling more in-depth analyses of conservation than previously possible. Deep sequencing is uncovering disease loci and regions under selective constraint, despite the fact that intuitive biophysical reasons for such constraint are sometimes absent. Allostery may often provide the missing explanatory link. We use models of protein conformational change to identify allosteric residues by finding essential surface pockets and information-flow bottlenecks, and we develop a software tool that enables users to perform this analysis on their own proteins of interest. Though fundamentally 3D-structural in nature, our analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues. We find that these tend to be conserved over diverse evolutionary time scales. Finally, we highlight examples of allosteric residues that help explain poorly understood disease-associated variants.
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Affiliation(s)
- Declan Clarke
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, CT 06520, USA
| | - Anurag Sethi
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Computer Science, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Richard W F Chang
- Yale College, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Jieming Chen
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Computer Science, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA.
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