1
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Sampson JM, Cannon DA, Duan J, Epstein JCK, Sergeeva AP, Katsamba PS, Mannepalli SM, Bahna FA, Adihou H, Guéret SM, Gopalakrishnan R, Geschwindner S, Rees DG, Sigurdardottir A, Wilkinson T, Dodd RB, De Maria L, Mobarec JC, Shapiro L, Honig B, Buchanan A, Friesner RA, Wang L. Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations. J Mol Biol 2024; 436:168640. [PMID: 38844044 DOI: 10.1016/j.jmb.2024.168640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 05/31/2024] [Indexed: 06/18/2024]
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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Affiliation(s)
- Jared M Sampson
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA
| | - Daniel A Cannon
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | - Jianxin Duan
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | | | - Alina P Sergeeva
- Columbia University, Department of Systems Biology, New York, NY, USA
| | | | - Seetha M Mannepalli
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA
| | - Fabiana A Bahna
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA
| | - Hélène Adihou
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stéphanie M Guéret
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Ranganath Gopalakrishnan
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stefan Geschwindner
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Gothenburg, Sweden
| | - D Gareth Rees
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | | | | | - Roger B Dodd
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | - Leonardo De Maria
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Juan Carlos Mobarec
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | - Lawrence Shapiro
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA; Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
| | - Barry Honig
- Columbia University, Department of Systems Biology, New York, NY, USA; Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA; Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA; Columbia University, Department of Medicine, New York, NY, USA
| | | | | | - Lingle Wang
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA.
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2
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Adediwura VA, Koirala K, Do HN, Wang J, Miao Y. Understanding the impact of binding free energy and kinetics calculations in modern drug discovery. Expert Opin Drug Discov 2024; 19:671-682. [PMID: 38722032 PMCID: PMC11108734 DOI: 10.1080/17460441.2024.2349149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.
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Affiliation(s)
- Victor A. Adediwura
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kushal Koirala
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hung N. Do
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
- Present address: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jinan Wang
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yinglong Miao
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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3
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Sampson JM, Cannon DA, Duan J, Epstein JCK, Sergeeva AP, Katsamba PS, Mannepalli SM, Bahna FA, Adihou H, Guéret SM, Gopalakrishnan R, Geschwindner S, Rees DG, Sigurdardottir A, Wilkinson T, Dodd RB, De Maria L, Mobarec JC, Shapiro L, Honig B, Buchanan A, Friesner RA, Wang L. Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590325. [PMID: 38712280 PMCID: PMC11071377 DOI: 10.1101/2024.04.22.590325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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Affiliation(s)
| | | | - Jianxin Duan
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | | | - Alina P. Sergeeva
- Columbia University, Department of Systems Biology, New York, NY, USA
| | | | - Seetha M. Mannepalli
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Fabiana A. Bahna
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Hélène Adihou
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stéphanie M. Guéret
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Ranganath Gopalakrishnan
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stefan Geschwindner
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | | | | | | | - Roger B. Dodd
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | - Leonardo De Maria
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Juan Carlos Mobarec
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | - Lawrence Shapiro
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
| | - Barry Honig
- Columbia University, Department of Systems Biology, New York, NY, USA
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
- Columbia University, Department of Medicine, New York, NY, USA
| | | | | | - Lingle Wang
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA
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4
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Schreiber S, Gercke D, Lenz F, Jose J. Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants. Appl Microbiol Biotechnol 2024; 108:305. [PMID: 38643427 PMCID: PMC11033240 DOI: 10.1007/s00253-024-13144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/25/2024] [Accepted: 04/09/2024] [Indexed: 04/22/2024]
Abstract
Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r = - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.
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Affiliation(s)
- Sebastian Schreiber
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, PharmaCampus, Corrensstr. 48, 48149, Münster, Germany
| | - David Gercke
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, PharmaCampus, Corrensstr. 48, 48149, Münster, Germany
| | - Florian Lenz
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, PharmaCampus, Corrensstr. 48, 48149, Münster, Germany
| | - Joachim Jose
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, PharmaCampus, Corrensstr. 48, 48149, Münster, Germany.
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5
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Thakur A, Gizzio J, Levy RM. Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability. J Phys Chem B 2024; 128:1656-1667. [PMID: 38350894 PMCID: PMC10939730 DOI: 10.1021/acs.jpcb.3c08097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.
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Affiliation(s)
- Abhishek Thakur
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania 19122, United States
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Joan Gizzio
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania 19122, United States
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania 19122, United States
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States
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6
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Liu X, Brooks Iii CL. Enhanced Sampling of Buried Charges in Free Energy Calculations Using Replica Exchange with Charge Tempering. J Chem Theory Comput 2024; 20:1051-1061. [PMID: 38232295 PMCID: PMC11275198 DOI: 10.1021/acs.jctc.3c00993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Buried ionizable groups in proteins often play important structural and functional roles. However, it is generally challenging to study the detailed molecular mechanisms solely based on experimental measurements. Free energy calculations using atomistic simulations, on the other hand, complement experimental studies and can provide high temporal and spatial resolution information that can lead to mechanistic insights. Nevertheless, it is also well recognized that sufficient sampling of such atomistic simulations can be challenging, considering that structural changes related to the buried charges may be very slow. In the present study, we describe a simple but effective enhanced sampling technique called replica exchange with charge tempering (REChgT) with a novel free energy method, multisite λ dynamics (MSλD), to study two systems containing buried charges, pKa prediction of a small molecule, orotate, in complex with the dihydroorotate dehydrogenase, and relative stability of a Glu-Lys pair buried in the hydrophobic core of two variants of Staphylococcal nuclease. Compared to the original MSλD simulations, the usage of REChgT dramatically increases sampling in both conformational and alchemical spaces, which directly translates into a significant reduction of wall time to converge the free energy calculations. This study highlights the importance of sufficient sampling toward developing improved free energy methods.
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Affiliation(s)
- Xiaorong Liu
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles L Brooks Iii
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States
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7
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Coskun D, Lihan M, Rodrigues JPGLM, Vass M, Robinson D, Friesner RA, Miller EB. Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation. J Chem Theory Comput 2024; 20:477-489. [PMID: 38100422 DOI: 10.1021/acs.jctc.3c00839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.
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Affiliation(s)
- Dilek Coskun
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Muyun Lihan
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | | | - Márton Vass
- Schrödinger Technologies Limited, Davidson House, First Floor, Reading RG1 3 EU, U.K
| | - Daniel Robinson
- Schrödinger Technologies Limited, Davidson House, First Floor, Reading RG1 3 EU, U.K
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York 10036, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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8
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Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
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Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
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9
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Haufe Y, Kuruva V, Samanani Z, Lokaj G, Kamnesky G, Shadamarshan P, Shahoei R, Katz D, Sampson JM, Pusch M, Brik A, Nicke A, Leffler AE. Basic Residues at Position 11 of α-Conotoxin LvIA Influence Subtype Selectivity between α3β2 and α3β4 Nicotinic Receptors via an Electrostatic Mechanism. ACS Chem Neurosci 2023; 14:4311-4322. [PMID: 38051211 DOI: 10.1021/acschemneuro.3c00506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023] Open
Abstract
Understanding the determinants of α-conotoxin (α-CTX) selectivity for different nicotinic acetylcholine receptor (nAChR) subtypes is a prerequisite for the design of tool compounds to study nAChRs. However, selectivity optimization of these small, disulfide-rich peptides is difficult not only because of an absence of α-CTX/nAChR co-structures but also because it is challenging to predict how a mutation to an α-CTX will alter its potency and selectivity. As a prototypical system to investigate selectivity, we employed the α-CTX LvIA that is 25-fold selective for the α3β2 nAChR over the related α3β4 nAChR subtype, which is a target for nicotine addiction. Using two-electrode voltage clamp electrophysiology, we identified LvIA[D11R] that is 2-fold selective for the α3β4 nAChR, reversing the subtype preference. This effect is specifically due to the change in charge and not shape of LvIA[D11R], as substitution of D11 with citrulline retains selectivity for the α3β2 nAChR. Furthermore, LvIA[D11K] shows a stronger reversal, with 4-fold selectivity for the α3β4 nAChR. Motivated by these findings, using site-directed mutagenesis, we found that β2[K79A] (I79 on β4), but not β2[K78A] (N78 on β4), largely restores the potency of basic mutants at position 11. Finally, to understand the structural basis of this effect, we used AlphaFold2 to generate models of LvIA in complex with both nAChR subtypes. Both models confirm the plausibility of an electrostatic mechanism to explain the data and also reproduce a broad range of potency and selectivity structure-activity relationships for LvIA mutants, as measured using free energy perturbation simulations. Our work highlights how electrostatic interactions can drive α-CTX selectivity and may serve as a strategy for optimizing the selectivity of LvIA and other α-CTXs.
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Affiliation(s)
- Yves Haufe
- Faculty of Medicine, Walther Straub Institute of Pharmacology and Toxicology, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Veeresh Kuruva
- Schulich Faculty of Chemistry, Technion-Israel Institute of Technology, Haifa 3200008, Israel
| | - Ziyana Samanani
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Gonxhe Lokaj
- Faculty of Medicine, Walther Straub Institute of Pharmacology and Toxicology, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Guy Kamnesky
- Schulich Faculty of Chemistry, Technion-Israel Institute of Technology, Haifa 3200008, Israel
| | - PranavKumar Shadamarshan
- Faculty of Medicine, Walther Straub Institute of Pharmacology and Toxicology, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Rezvan Shahoei
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Dana Katz
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Jared M Sampson
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Michael Pusch
- Istituto di Biofisica, CNR, Via De Marini 6, Genova 16149, Italy
| | - Ashraf Brik
- Schulich Faculty of Chemistry, Technion-Israel Institute of Technology, Haifa 3200008, Israel
| | - Annette Nicke
- Faculty of Medicine, Walther Straub Institute of Pharmacology and Toxicology, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Abba E Leffler
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
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10
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York DM. Modern Alchemical Free Energy Methods for Drug Discovery Explained. ACS PHYSICAL CHEMISTRY AU 2023; 3:478-491. [PMID: 38034038 PMCID: PMC10683484 DOI: 10.1021/acsphyschemau.3c00033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.
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Affiliation(s)
- Darrin M. York
- Laboratory for Biomolecular
Simulation Research, Institute for Quantitative Biomedicine, and Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway, New Jersey 08854, United States
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11
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Hernández González JE, de Araujo AS. Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States. J Chem Inf Model 2023; 63:6807-6822. [PMID: 37851531 DOI: 10.1021/acs.jcim.3c00972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.
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12
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Shirvanizadeh N, Vihinen M. VariBench, new variation benchmark categories and data sets. FRONTIERS IN BIOINFORMATICS 2023; 3:1248732. [PMID: 37795169 PMCID: PMC10546188 DOI: 10.3389/fbinf.2023.1248732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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13
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Bello M, Bandala C. Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors. RSC Adv 2023; 13:25118-25128. [PMID: 37614784 PMCID: PMC10443623 DOI: 10.1039/d3ra04916g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.
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Affiliation(s)
- Martiniano Bello
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Diaz Mirón s/n, Col. Casco de Santo Tomas Ciudad de México 11340 Mexico
| | - Cindy Bandala
- Escuela Superior de Medicina, Instituto Politécnico Nacional México City 11340 Mexico
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14
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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15
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Thanh ND, Lan PH, Hai DS, Anh HH, Giang NTK, Van HTK, Toan VN, Tri NM, Toan DN. Thiourea derivatives containing 4-arylthiazoles and d-glucose moiety: design, synthesis, antimicrobial activity evaluation, and molecular docking/dynamics simulations. RSC Med Chem 2023; 14:1114-1130. [PMID: 37360390 PMCID: PMC10285754 DOI: 10.1039/d3md00010a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/15/2023] [Indexed: 06/28/2023] Open
Abstract
Some substituted glucose-conjugated thioureas containing 1,3-thiazole ring, 4a-h, were synthesized by the reaction of the corresponding substituted 2-amino-4-phenyl-1,3-thiazoles 2a-h with 2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl isocyanate. The antibacterial and antifungal activities of these thiazole-containing thioureas were estimated using a minimum inhibitory concentration protocol. Among these compounds, 4c, 4g, and 4h were better inhibitors with MIC = 0.78-3.125 μg mL-1. These three compounds were also tested for their ability to inhibit S. aureus enzymes, including DNA gyrase, DNA topoisomerase IV (Topo IV), and dihydrofolate reductase, and compound 4h was found to be a strong inhibitor with IC50 = 1.25 ± 0.12, 67.28 ± 1.21, and 0.13 ± 0.05 μM, respectively. Induced-fit docking and MM-GBSA calculations were performed to observe the binding efficiencies and steric interactions of these compounds. The obtained results showed that compound 4h is compatible with the active site of S. aureus DNA gyrase 2XCS with four H-bond interactions with residues Ala1118, Met1121, and F:DC11 and also three interactions with F:DG10 (two interactions) and F:DC11 (one interaction). Molecular dynamics simulation in a water solvent system showed that ligand 4h had active interactions with enzyme 2XCS through residues Ala1083, Glu1088, Ala1118, Gly1117, and Met1121.
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Affiliation(s)
- Nguyen Dinh Thanh
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
| | - Pham Hong Lan
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Institute of Science and Technology, Ministry of Public Security of Vietnam 47 Pham Van Dong Cau Giay Ha Noi Vietnam
| | - Do Son Hai
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Institute of Science and Technology, Ministry of Public Security of Vietnam 47 Pham Van Dong Cau Giay Ha Noi Vietnam
| | - Hoang Huu Anh
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
| | - Nguyen Thi Kim Giang
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Institute of Science and Technology, Ministry of Public Security of Vietnam 47 Pham Van Dong Cau Giay Ha Noi Vietnam
| | - Hoang Thi Kim Van
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Faculty of Chemical Technology, Viet Tri University of Industry Tien Kien Lam Thao Phu Tho Vietnam
| | - Vu Ngoc Toan
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Institute of New Technology, Military Institute of Science and Technology (Ministry of Military) 17 Hoang Sam Cau Giay Ha Noi Vietnam
| | - Nguyen Minh Tri
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Institute of New Technology, Military Institute of Science and Technology (Ministry of Military) 17 Hoang Sam Cau Giay Ha Noi Vietnam
| | - Duong Ngoc Toan
- Faculty of Chemistry, University of Science (Vietnam National University, Hanoi) 19 Le Thanh Tong Ha Noi Vietnam
- Faculty of Chemistry, Thai Nguyen University of Education 20 Luong Ngoc Quyen Thai Nguyen Vietnam
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16
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Sergeeva AP, Katsamba PS, Liao J, Sampson JM, Bahna F, Mannepalli S, Morano NC, Shapiro L, Friesner RA, Honig B. Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity. J Mol Biol 2023:168187. [PMID: 37355034 DOI: 10.1016/j.jmb.2023.168187] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.
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Affiliation(s)
- Alina P Sergeeva
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032
| | - Phinikoula S Katsamba
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Junzhuo Liao
- Department of Chemistry, Columbia University, New York, NY 10027, USA
| | - Jared M Sampson
- Department of Chemistry, Columbia University, New York, NY 10027, USA; Schrödinger, Inc., New York, NY 10036, USA
| | - Fabiana Bahna
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Seetha Mannepalli
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Nicholas C Morano
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Lawrence Shapiro
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
| | | | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032; Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA; Department of Medicine, Columbia University, New York, NY 10032.
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17
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Sheng Z, Bimela JS, Wang M, Li Z, Guo Y, Ho DD. An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design. Front Immunol 2023; 14:1190416. [PMID: 37275896 PMCID: PMC10235760 DOI: 10.3389/fimmu.2023.1190416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 04/28/2023] [Indexed: 06/07/2023] Open
Abstract
Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.
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Affiliation(s)
- Zizhang Sheng
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Jude S. Bimela
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Maple Wang
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Zhiteng Li
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Yicheng Guo
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - David D. Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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18
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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19
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Rui H, Ashton KS, Min J, Wang C, Potts PR. Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction. RSC Chem Biol 2023; 4:192-215. [PMID: 36908699 PMCID: PMC9994104 DOI: 10.1039/d2cb00207h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.
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Affiliation(s)
- Huan Rui
- Center for Research Acceleration by Digital Innovation, Amgen Research Thousand Oaks CA 91320 USA
| | - Kate S Ashton
- Medicinal Chemistry, Amgen Research Thousand Oaks CA 91320 USA
| | - Jaeki Min
- Induced Proximity Platform, Amgen Research Thousand Oaks CA 91320 USA
| | - Connie Wang
- Digital, Technology & Innovation, Amgen Thousand Oaks CA 91320 USA
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20
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Ferraz MVF, Neto JCS, Lins RD, Teixeira ES. An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties. Phys Chem Chem Phys 2023; 25:7257-7267. [PMID: 36810523 DOI: 10.1039/d2cp05644e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
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Affiliation(s)
- Matheus V F Ferraz
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil.,Heidelberg Institute for Theoretical Studies, HITS, Heidelberg, Germany
| | - José C S Neto
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
| | - Roberto D Lins
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil
| | - Erico S Teixeira
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
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21
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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. J Cheminform 2023; 15:31. [PMID: 36864534 PMCID: PMC9983232 DOI: 10.1186/s13321-023-00701-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/17/2023] [Indexed: 03/04/2023] Open
Abstract
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.
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22
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Lihan M, Lupyan D, Oehme D. Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations. Protein Sci 2023; 32:e4557. [PMID: 36573828 PMCID: PMC9878467 DOI: 10.1002/pro.4557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.
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Affiliation(s)
- Muyun Lihan
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, and Center for Biophysics and Quantitative BiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Schrödinger Inc.CambridgeMassachusettsUSA
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23
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Mai H, Zimmer MH, Miller TF. Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods. J Phys Chem B 2023; 127:446-455. [PMID: 36607139 PMCID: PMC9869335 DOI: 10.1021/acs.jpcb.2c05795] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/18/2022] [Indexed: 01/07/2023]
Abstract
Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target-PROTAC-E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parametrization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current force field, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity.
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Affiliation(s)
- Huanghao Mai
- Division of Chemistry and Chemical
Engineering, California Institute of Technology, Pasadena, California91125, United States
| | - Matthew H. Zimmer
- Division of Chemistry and Chemical
Engineering, California Institute of Technology, Pasadena, California91125, United States
| | - Thomas F. Miller
- Division of Chemistry and Chemical
Engineering, California Institute of Technology, Pasadena, California91125, United States
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24
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Coskun D, Chen W, Clark AJ, Lu C, Harder ED, Wang L, Friesner RA, Miller EB. Reliable and Accurate Prediction of Single-Residue p Ka Values through Free Energy Perturbation Calculations. J Chem Theory Comput 2022; 18:7193-7204. [PMID: 36384001 DOI: 10.1021/acs.jctc.2c00954] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Accurate prediction of the pKa's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue pKa values. We begin with an initial set of 191 residues with experimentally determined pKa values. To isolate sampling limitations from force field inaccuracies, we develop an algorithm to classify residues whose environments are significantly affected by crystal packing effects. We then report an approach to identify buried histidines that require significant sampling beyond what is achieved in typical FEP calculations. We therefore define a clean data set not requiring algorithms capable of predicting major conformational changes on which other pKa prediction methods can be tested. On this data set, we report an RMSE of 0.76 pKa units for 35 ASP residues, 0.51 pKa units for 44 GLU residues, and 0.67 pKa units for 76 HIS residues.
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Affiliation(s)
- Dilek Coskun
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Wei Chen
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Anthony J Clark
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Chao Lu
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Edward D Harder
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Lingle Wang
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York10036, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
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25
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Kodchakorn K, Kongtawelert P. Molecular dynamics study on the strengthening behavior of Delta and Omicron SARS-CoV-2 spike RBD improved receptor-binding affinity. PLoS One 2022; 17:e0277745. [PMID: 36395151 PMCID: PMC9671323 DOI: 10.1371/journal.pone.0277745] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
The COVID-19 pandemic caused by a virus that can be transmitted from human to human via air droplets has changed the quality of life and economic systems all over the world. The viral DNA has mutated naturally over time leading to the diversity of coronavirus victims which has posed a serious threat to human security on a massive scale. The current variants have developed in a dominant way and are considered "Variants of Concern" by the World Health Organization (WHO). In this work, Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) variants were obtained to evaluate whether naturally occurring mutations have strengthened viral infectivity. We apply reliable in silico structural dynamics and energetic frameworks of the mutated S-RBD protein for ACE2-binding to analyze and compare the structural information related to the wild-type. In particular, the hotspot residues at Q493, Q498, and N501 on the S-RBD protein were determined as contributing factors to the employment stability of the relevant binding interface. The L452R mutation induces an increment of the hydrogen bonds formed by changing the Q493 environment for ACE2 binding. Moreover, the Q493K exchange in Omicron enables the formation of two additional salt bridges, leading to a strong binding affinity by increased electrostatic interaction energy. These results could be used in proposing concrete informative data for a structure-based design engaged in finding better therapeutics against novel variants.
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Affiliation(s)
- Kanchanok Kodchakorn
- Thailand Excellence Center for Tissue Engineering and Stem Cells, Department of Biochemistry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Prachya Kongtawelert
- Thailand Excellence Center for Tissue Engineering and Stem Cells, Department of Biochemistry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- * E-mail:
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26
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Bhadane R, Salo-Ahen OMH. High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2. Biomedicines 2022; 10:2779. [PMID: 36359299 PMCID: PMC9687918 DOI: 10.3390/biomedicines10112779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2023] Open
Abstract
The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.
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Affiliation(s)
- Rajendra Bhadane
- Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Biochemistry, Åbo Akademi University, FI-20520 Turku, Finland
- Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Pharmacy, Åbo Akademi University, FI-20520 Turku, Finland
| | - Outi M. H. Salo-Ahen
- Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Biochemistry, Åbo Akademi University, FI-20520 Turku, Finland
- Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Pharmacy, Åbo Akademi University, FI-20520 Turku, Finland
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27
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Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions. Molecules 2022; 27:molecules27206861. [PMID: 36296453 PMCID: PMC9610776 DOI: 10.3390/molecules27206861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.
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28
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Qasem Z, Pavlin M, Ritacco I, Avivi MY, Meron S, Hirsch M, Shenberger Y, Gevorkyan-Airapetov L, Magistrato A, Ruthstein S. Disrupting Cu trafficking as a potential therapy for cancer. Front Mol Biosci 2022; 9:1011294. [PMID: 36299299 PMCID: PMC9589254 DOI: 10.3389/fmolb.2022.1011294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Copper ions play a crucial role in various cellular biological processes. However, these copper ions can also lead to toxicity when their concentration is not controlled by a sophisticated copper-trafficking system. Copper dys-homeostasis has been linked to a variety of diseases, including neurodegeneration and cancer. Therefore, manipulating Cu-trafficking to trigger selective cancer cell death may be a viable strategy with therapeutic benefit. By exploiting combined in silico and experimental strategies, we identified small peptides able to bind Atox1 and metal-binding domains 3-4 of ATP7B proteins. We found that these peptides reduced the proliferation of cancer cells owing to increased cellular copper ions concentration. These outcomes support the idea of harming copper trafficking as an opportunity for devising novel anti-cancer therapies.
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Affiliation(s)
- Zena Qasem
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
| | - Matic Pavlin
- National Research Council of Italy (CNR)—Institute of Material (IOM) C/o International School for Advanced Studies (SISSA), Trieste, Italy
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Ljubljana, Slovenia
| | - Ida Ritacco
- National Research Council of Italy (CNR)—Institute of Material (IOM) C/o International School for Advanced Studies (SISSA), Trieste, Italy
- Department of Chemistry, University of Salerno, Salerno, Italy
| | - Matan Y. Avivi
- The Mina and Everard Goodman Faculty of Life-Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Shelly Meron
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
| | - Melanie Hirsch
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
| | - Yulia Shenberger
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
| | - Lada Gevorkyan-Airapetov
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
| | - Alessandra Magistrato
- National Research Council of Italy (CNR)—Institute of Material (IOM) C/o International School for Advanced Studies (SISSA), Trieste, Italy
- *Correspondence: Alessandra Magistrato, ; Sharon Ruthstein,
| | - Sharon Ruthstein
- Department of Chemistry and the Institute of Nanotechnology and Advanced Materials (BINA), Bar-Ilan University, Ramat-Gan, Israel
- *Correspondence: Alessandra Magistrato, ; Sharon Ruthstein,
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29
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Yu Y, Wang Z, Wang L, Tian S, Hou T, Sun H. Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses. J Cheminform 2022; 14:56. [PMID: 35987841 PMCID: PMC9392442 DOI: 10.1186/s13321-022-00639-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/08/2022] [Indexed: 12/04/2022] Open
Abstract
Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.
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30
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Xu T, Zhu K, Beautrait A, Vendome J, Borrelli KW, Abel R, Friesner RA, Miller EB. Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models. J Chem Theory Comput 2022; 18:5710-5724. [PMID: 35972903 DOI: 10.1021/acs.jctc.2c00371] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Homology models have been used for virtual screening and to understand the binding mode of a known active compound; however, rarely have the models been shown to be of sufficient accuracy, comparable to crystal structures, to support free-energy perturbation (FEP) calculations. We demonstrate here that the use of an advanced induced-fit docking methodology reliably enables predictive FEP calculations on congeneric series across homology models ≥30% sequence identity. Furthermore, we show that retrospective FEP calculations on a congeneric series of drug-like ligands are sufficient to discriminate between predicted binding modes. Results are presented for a total of 29 homology models for 14 protein targets, showing FEP results comparable to those obtained using experimentally determined crystal structures for 86% of homology models with template structure sequence identities ranging from 30 to 50%. Implications for the use and validation of homology models in drug discovery projects are discussed.
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Affiliation(s)
- Tianchuan Xu
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Kai Zhu
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | | | - Jeremie Vendome
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Kenneth W Borrelli
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York 10036, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, New York, New York 10036, United States
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31
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On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations. Antibodies (Basel) 2022; 11:antib11030051. [PMID: 35997345 PMCID: PMC9397028 DOI: 10.3390/antib11030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.
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32
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Zhu F, Bourguet FA, Bennett WFD, Lau EY, Arrildt KT, Segelke BW, Zemla AT, Desautels TA, Faissol DM. Large-scale application of free energy perturbation calculations for antibody design. Sci Rep 2022; 12:12489. [PMID: 35864134 PMCID: PMC9302960 DOI: 10.1038/s41598-022-14443-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/07/2022] [Indexed: 01/02/2023] Open
Abstract
Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.
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Affiliation(s)
- Fangqiang Zhu
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
| | - Feliza A Bourguet
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - William F D Bennett
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Edmond Y Lau
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Kathryn T Arrildt
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Brent W Segelke
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Adam T Zemla
- Global Security Computing Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Thomas A Desautels
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Daniel M Faissol
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
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33
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Giang NTK, Thanh ND, Quyen TH, Huong DT, Toan VN, Van HTK. Synthesis, bacterial and fungal inhibition assay, molecular docking study of substituted isatin (N-substituted 1,2,3,4-tetra-O-acetyl-β-glucopyranosyl)thiosemicarbazones. Med Chem Res 2022. [DOI: 10.1007/s00044-022-02920-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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La Serra MA, Vidossich P, Acquistapace I, Ganesan AK, De Vivo M. Alchemical Free Energy Calculations to Investigate Protein-Protein Interactions: the Case of the CDC42/PAK1 Complex. J Chem Inf Model 2022; 62:3023-3033. [PMID: 35679463 PMCID: PMC9241073 DOI: 10.1021/acs.jcim.2c00348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
![]()
Here, we show that
alchemical free energy calculations can quantitatively
compute the effect of mutations at the protein–protein interface.
As a test case, we have used the protein complex formed by the small
Rho-GTPase CDC42 and its downstream effector PAK1, a serine/threonine
kinase. Notably, the CDC42/PAK1 complex offers a wealth of structural,
mutagenesis, and binding affinity data because of its central role
in cellular signaling and cancer progression. In this context, we
have considered 16 mutations in the CDC42/PAK1 complex and obtained
excellent agreement between computed and experimental data on binding
affinity. Importantly, we also show that a careful analysis of the
side-chain conformations in the mutated amino acids can considerably
improve the computed estimates, solving issues related to sampling
limitations. Overall, this study demonstrates that alchemical free
energy calculations can conveniently be integrated into the design
of experimental mutagenesis studies.
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Affiliation(s)
- Maria Antonietta La Serra
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Pietro Vidossich
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Isabella Acquistapace
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Anand K Ganesan
- Department of Dermatology, University of California, Irvine, Irvine, California 92697, United States.,Department of Biological Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Marco De Vivo
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
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35
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Wu Z, Biggin PC. Correction Schemes for Absolute Binding Free Energies Involving Lipid Bilayers. J Chem Theory Comput 2022; 18:2657-2672. [PMID: 35315270 PMCID: PMC9082507 DOI: 10.1021/acs.jctc.1c01251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Absolute
binding free-energy (ABFE) calculations are playing an
increasing role in drug design, especially as they can be performed
on a range of disparate compounds and direct comparisons between them
can be made. It is, however, especially important to ensure that they
are as accurate as possible, as unlike relative binding free-energy
(RBFE) calculations, one does not benefit as much from a cancellation
of errors during the calculations. In most modern implementations
of ABFE calculations, a particle mesh Ewald scheme is typically used
to treat the electrostatic contribution to the free energy. A central
requirement of such schemes is that the box preserves neutrality throughout
the calculation. There are many ways to deal with this problem that
have been discussed over the years ranging from a neutralizing plasma
with a post hoc correction term through to a simple co-alchemical
ion within the same box. The post hoc correction approach is the most
widespread. However, the vast majority of these studies have been
applied to a soluble protein in a homogeneous solvent (water or salt
solution). In this work, we explore which of the more common approaches
would be the most suitable for a simulation box with a lipid bilayer
within it. We further develop the idea of the so-called Rocklin correction
for lipid-bilayer systems and show how such a correction could work.
However, we also show that it will be difficult to make this generalizable
in a practical way and thus we conclude that the use of a “co-alchemical
ion” is the most useful approach for simulations involving
lipid membrane systems.
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Affiliation(s)
- Zhiyi Wu
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
| | - Philip C Biggin
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
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36
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Wang J, Ishchenko A, Zhang W, Razavi A, Langley D. A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies. Sci Rep 2022; 12:2024. [PMID: 35132139 PMCID: PMC8821539 DOI: 10.1038/s41598-022-05875-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
Although seeking to develop a general and accurate binding free energy calculation method for protein-protein and protein-ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far. Here, we report the development of a metadynamics-based procedure that calculates Dissociation Free Energy (DFE) and its application to 19 non-congeneric protein-protein complexes and hundreds of protein-ligand complexes covering eight targets. We achieved very high correlations in comparison to experimental binding free energies for these diverse sets of systems, demonstrating the generality and accuracy of the method. Since structures of most proteins are available owing to the recent success of prediction by artificial intelligence, a general free energy method such as DFE, combined with other methods, can make structure-based drug design a widely viable and reliable solution to develop both traditional small molecule drugs and biologic drugs as well as PROTACS.
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Affiliation(s)
- Jing Wang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA.
| | | | - Wei Zhang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - Asghar Razavi
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - David Langley
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
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37
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Fratev F. R346K Mutation in the Mu Variant of SARS-CoV-2 Alters the Interactions with Monoclonal Antibodies from Class 2: A Free Energy Perturbation Study. J Chem Inf Model 2022; 62:627-631. [DOI: 10.1021/acs.jcim.1c01243] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Filip Fratev
- Micar Innovation (Micar21) Ltd., Persenk 34B, 1407 Sofia, Bulgaria
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, 1101 N Campbell Street, El Paso, Texas 79968, United States
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38
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Zacharias M. Match_Motif: A rapid computational tool to assist in protein-protein interaction design. Protein Sci 2022; 31:147-157. [PMID: 34648221 PMCID: PMC8740833 DOI: 10.1002/pro.4208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/12/2022]
Abstract
In order to generate protein assemblies with a desired function, the rational design of protein-protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein-protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.
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Affiliation(s)
- Martin Zacharias
- Center of Functional Protein AssembliesTechnical University of MunichGarchingGermany
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39
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Gallicchio E. Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria. Methods Mol Biol 2022; 2405:303-334. [PMID: 35298820 DOI: 10.1007/978-1-0716-1855-4_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.
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Affiliation(s)
- Emilio Gallicchio
- Department of Chemistry, Ph.D. Program in Biochemistry and Ph.D. Program in Chemistry at The Graduate Center of the City University of New York, Brooklyn College of the City University of New York, New York, NY, USA.
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40
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Fratev F. N501Y and K417N Mutations in the Spike Protein of SARS-CoV-2 Alter the Interactions with Both hACE2 and Human-Derived Antibody: A Free Energy of Perturbation Retrospective Study. J Chem Inf Model 2021; 61:6079-6084. [PMID: 34806876 DOI: 10.1021/acs.jcim.1c01242] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The N501Y and K417N mutations in the spike protein of SARS-CoV-2 and their combination gave rise to questions, but the data on their mechanism of action at the molecular level were limited. In this study, we present free energy perturbation (FEP) calculations, performed at the end of December 2020, for the interactions of the spike S1 receptor-binding domain (RBD) with both the ACE2 receptor and an antibody derived from COVID-19 patients. Our results showed that the S1 RBD-ACE2 interactions were significantly increased whereas those with the STE90-C11 antibody dramatically decreased. The K417N mutation in a combination with N501Y fully abolished the antibody effect. However, Lys417Asn seems to have a compensatory mechanism of action increasing the S1 RBD-ACE2 free energy of binding. This may explain the increased spread of the virus observed in the U.K. and South Africa and also gives rise to an important question regarding the possible human immune response and the success of the already available vaccines. Notably, when the experimental data became available confirming our calculations, it was demonstrated that protein-protein FEP can be a useful tool for providing urgent data to the scientific community.
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Affiliation(s)
- Filip Fratev
- Micar Innovation (Micar21) Ltd., Persenk 34B, 1407 Sofia, Bulgaria.,Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, 1101 N Campbell Street, El Paso, Texas 79968, United States
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41
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Boucher L, Somani S, Negron C, Ma W, Jacobs S, Chan W, Malia T, Obmolova G, Teplyakov A, Gilliland GL, Luo J. Surface salt bridges contribute to the extreme thermal stability of an FN3-like domain from a thermophilic bacterium. Proteins 2021; 90:270-281. [PMID: 34405904 DOI: 10.1002/prot.26218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 03/08/2021] [Accepted: 08/02/2021] [Indexed: 12/27/2022]
Abstract
This study uses differential scanning calorimetry, X-ray crystallography, and molecular dynamics simulations to investigate the structural basis for the high thermal stability (melting temperature 97.5°C) of a FN3-like protein domain from thermophilic bacteria Thermoanaerobacter tengcongensis (FN3tt). FN3tt adopts a typical FN3 fold with a three-stranded beta sheet packing against a four-stranded beta sheet. We identified three solvent exposed arginine residues (R23, R25, and R72), which stabilize the protein through salt bridge interactions with glutamic acid residues on adjacent strands. Alanine mutation of the three arginine residues reduced melting temperature by up to 22°C. Crystal structures of the wild type (WT) and a thermally destabilized (∆Tm -19.7°C) triple mutant (R23L/R25T/R72I) were found to be nearly identical, suggesting that the destabilization is due to interactions of the arginine residues. Molecular dynamics simulations showed that the salt bridge interactions in the WT were stable and provided a dynamical explanation for the cooperativity observed between R23 and R25 based on calorimetry measurements. In addition, folding free energy changes computed using free energy perturbation molecular dynamics simulations showed high correlation with melting temperature changes. This work is another example of surface salt bridges contributing to the enhanced thermal stability of thermophilic proteins. The molecular dynamics simulation methods employed in this study may be broadly useful for in silico surface charge engineering of proteins.
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Affiliation(s)
- Lauren Boucher
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Sandeep Somani
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | | | - Wenting Ma
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Steven Jacobs
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Winnie Chan
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Thomas Malia
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Galina Obmolova
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Alexey Teplyakov
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Gary L Gilliland
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | - Jinquan Luo
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
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42
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Exploring the effect of temperature on inhibition of non-structural protease 3 of Chikungunya virus using molecular dynamics simulations and thermodynamics parameters. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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43
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Katz D, DiMattia MA, Sindhikara D, Li H, Abraham N, Leffler AE. Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations. Mar Drugs 2021; 19:367. [PMID: 34202022 PMCID: PMC8306581 DOI: 10.3390/md19070367] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 01/18/2023] Open
Abstract
Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3β2 and α3β4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R2 of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3β2 and α3β4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R2 of 0.49. This is significantly better than the widely used molecular mechanics-generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R2 of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3β2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3β2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.
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Affiliation(s)
- Dana Katz
- Schrödinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (M.A.D.); (D.S.); (H.L.)
| | - Michael A. DiMattia
- Schrödinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (M.A.D.); (D.S.); (H.L.)
| | - Dan Sindhikara
- Schrödinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (M.A.D.); (D.S.); (H.L.)
| | - Hubert Li
- Schrödinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (M.A.D.); (D.S.); (H.L.)
| | | | - Abba E. Leffler
- Schrödinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (M.A.D.); (D.S.); (H.L.)
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44
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Daley SK, Cordell GA. Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs. Molecules 2021; 26:molecules26133800. [PMID: 34206470 PMCID: PMC8270272 DOI: 10.3390/molecules26133800] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/05/2021] [Accepted: 06/14/2021] [Indexed: 12/15/2022] Open
Abstract
An overview is presented of the well-established role of alkaloids in drug discovery, the application of more sustainable chemicals, and biological approaches, and the implementation of information systems to address the current challenges faced in meeting global disease needs. The necessity for a new international paradigm for natural product discovery and development for the treatment of multidrug resistant organisms, and rare and neglected tropical diseases in the era of the Fourth Industrial Revolution and the Quintuple Helix is discussed.
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Affiliation(s)
| | - Geoffrey A. Cordell
- Natural Products Inc., Evanston, IL 60202, USA;
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
- Correspondence:
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45
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Zou J, Li Z, Liu S, Peng C, Fang D, Wan X, Lin Z, Lee TS, Raleigh DP, Yang M, Simmerling C. Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies. J Chem Theory Comput 2021; 17:3710-3726. [PMID: 34029468 PMCID: PMC8215533 DOI: 10.1021/acs.jctc.1c00214] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In silico screening of drug-target interactions is a key part of the drug discovery process. Changes in the drug scaffold via contraction or expansion of rings, the breaking of rings, and the introduction of cyclic structures from acyclic structures are commonly applied by medicinal chemists to improve binding affinity and enhance favorable properties of candidate compounds. These processes, commonly referred to as scaffold hopping, are challenging to model computationally. Although relative binding free energy (RBFE) calculations have shown success in predicting binding affinity changes caused by perturbing R-groups attached to a common scaffold, applications of RBFE calculations to modeling scaffold hopping are relatively limited. Scaffold hopping inevitably involves breaking and forming bond interactions of quadratic functional forms, which is highly challenging. A novel method for handling ring opening/closure/contraction/expansion and linker contraction/expansion is presented here. To the best of our knowledge, RBFE calculations on linker contraction/expansion have not been previously reported. The method uses auxiliary restraints to hold the atoms at the ends of a bond in place during the breaking and forming of the bonds. The broad applicability of the method was demonstrated by examining perturbations involving small-molecule macrocycles and mutations of proline in proteins. High accuracy was obtained using the method for most of the perturbations studied. The rigor of the method was isolated from the force field by validating the method using relative and absolute hydration free energy calculations compared to standard simulation results. Unlike other methods that rely on λ-dependent functional forms for bond interactions, the method presented here can be employed using modern molecular dynamics software without modification of codes or force field functions.
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Affiliation(s)
- Junjie Zou
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794-3400, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-3400, United States
| | - Zhipeng Li
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Shuai Liu
- XtalPi Inc., 245 Main St, 11th Floor, Cambridge, MA 02142, United States
| | - Chunwang Peng
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Dong Fang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Xiao Wan
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Zhixiong Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, Rutgers University, Piscataway, New Jersey, 08854-8076, United States
| | - Daniel P. Raleigh
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794-3400, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-3400, United States
| | - Mingjun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794-3400, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-3400, United States
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46
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Debnath P, Bhaumik S, Sen D, Muttineni RK, Debnath S. Identification of SARS-CoV-2 Main Protease Inhibitors Using Structure Based Virtual Screening and Molecular Dynamics Simulation of DrugBank Database. ChemistrySelect 2021; 6:4991-5013. [PMID: 34541295 PMCID: PMC8441713 DOI: 10.1002/slct.202100854] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/12/2021] [Indexed: 12/19/2022]
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is highly pathogenic to humans and has created an unprecedented global health care threat. Globally, intense efforts are going on to discover a vaccine or new drug molecules to control the COVID-19. However, till today, there is no effective therapeutics or treatment available for COVID-19. In this study, we aim to find out potential small molecule inhibitors for SARS-CoV-2 main protease (Mpro) from the known DrugBank database version 5.1.8. We applied structure-based virtual screening of the database containing 11875 numbers of drug candidates to identify potential hits for SARS-CoV-2 Mpro inhibitors. Seven potential inhibitors having admirable XP glide score ranging from -15.071 to -8.704 kcal/mol and good binding affinity with the active sites amino acids of Mpro were identified. The selected hits were further analyzed with 50 ns molecular dynamics (MD) simulation to examine the stability of protein-ligand complexes. The root mean square deviation and potential energy plot indicates the stability of the complexes during the 50 ns MD simulation. The MM-GBSA analysis also showed good binding energy of the selected hits (-83.2718 to -58.6618 kcal/mol). Further analysis revealed critical hydrogen bonds and hydrophobic interactions between compounds and the target protein. The compounds bind to biologically important regions of Mpro, indicating their potential to inhibit the functionality of this component.
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Affiliation(s)
- Pradip Debnath
- Department of ChemistryMaharaja Bir Bikram CollegeAgartalaTripura799004India
| | - Samhita Bhaumik
- Department of ChemistryWomen's CollegeAgartalaTripura799001India
| | - Debanjan Sen
- BCDA College of Pharmacy & TechnologyJessore Road South, HridaypurKolkataWest Bengal700127India
| | | | - Sudhan Debnath
- Department of ChemistryMaharaja Bir Bikram CollegeAgartalaTripura799004India
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47
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Öhlknecht C, Katz S, Kröß C, Sprenger B, Engele P, Schneider R, Oostenbrink C. Efficient In Silico Saturation Mutagenesis of a Member of the Caspase Protease Family. J Chem Inf Model 2021; 61:1193-1203. [PMID: 33570387 PMCID: PMC8023567 DOI: 10.1021/acs.jcim.0c01216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Indexed: 12/28/2022]
Abstract
Rational-design methods have proven to be a valuable toolkit in the field of protein design. Numerical approaches such as free-energy calculations or QM/MM methods are fit to widen the understanding of a protein-sequence space but require large amounts of computational time and power. Here, we apply an efficient method for free-energy calculations that combines the one-step perturbation (OSP) with the third-power-fitting (TPF) approach. It is fit to calculate full free energies of binding from three different end states only. The nonpolar contribution to the free energies are calculated for a set of chosen amino acids from a single simulation of a judiciously chosen reference state. The electrostatic contributions, on the other hand, are predicted from simulations of the neutral and charged end states of the individual amino acids. We used this method to perform in silico saturation mutagenesis of two sites in human Caspase-2. We calculated relative binding free energies toward two different substrates that differ in their P1' site and in their affinity toward the unmutated protease. Although being approximate, our approach showed very good agreement upon validation against experimental data. 76% of the predicted relative free energies of amino acid mutations was found to be true positives or true negatives. We observed that this method is fit to discriminate amino acid mutations because the rate of false negatives is very low (<1.5%). The approach works better for a substrate with medium/low affinity with a Matthews correlation coefficient (MCC) of 0.63, whereas for a substrate with very low affinity, the MCC was 0.38. In all cases, the combined TPF + OSP approach outperformed the linear interaction energy method.
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Affiliation(s)
- Christoph Öhlknecht
- Institute
of Molecular Modeling and Simulation, University
of Natural Resources and Life Sciences, Vienna A-1190, Austria
- Austrian
Centre of Industrial Biotechnology, Petersgasse 14, Graz 8041, Austria
| | - Sonja Katz
- Institute
of Molecular Modeling and Simulation, University
of Natural Resources and Life Sciences, Vienna A-1190, Austria
- Austrian
Centre of Industrial Biotechnology, Petersgasse 14, Graz 8041, Austria
| | - Christina Kröß
- Austrian
Centre of Industrial Biotechnology, Petersgasse 14, Graz 8041, Austria
- Institute
of Biochemistry and Center of Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Bernhard Sprenger
- Austrian
Centre of Industrial Biotechnology, Petersgasse 14, Graz 8041, Austria
- Institute
of Biochemistry and Center of Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Petra Engele
- Austrian
Centre of Industrial Biotechnology, Petersgasse 14, Graz 8041, Austria
- Institute
of Biochemistry and Center of Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Rainer Schneider
- Institute
of Biochemistry and Center of Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck 6020, Austria
| | - Chris Oostenbrink
- Institute
of Molecular Modeling and Simulation, University
of Natural Resources and Life Sciences, Vienna A-1190, Austria
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48
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Katz D, Sindhikara D, DiMattia M, Leffler AE. Potency-Enhancing Mutations of Gating Modifier Toxins for the Voltage-Gated Sodium Channel Na V1.7 Can Be Predicted Using Accurate Free-Energy Calculations. Toxins (Basel) 2021; 13:193. [PMID: 33800031 PMCID: PMC8002187 DOI: 10.3390/toxins13030193] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 01/28/2023] Open
Abstract
Gating modifier toxins (GMTs) isolated from venomous organisms such as Protoxin-II (ProTx-II) and Huwentoxin-IV (HwTx-IV) that inhibit the voltage-gated sodium channel NaV1.7 by binding to its voltage-sensing domain II (VSDII) have been extensively investigated as non-opioid analgesics. However, reliably predicting how a mutation to a GMT will affect its potency for NaV1.7 has been challenging. Here, we hypothesize that structure-based computational methods can be used to predict such changes. We employ free-energy perturbation (FEP), a physics-based simulation method for predicting the relative binding free energy (RBFE) between molecules, and the cryo electron microscopy (cryo-EM) structures of ProTx-II and HwTx-IV bound to VSDII of NaV1.7 to re-predict the relative potencies of forty-seven point mutants of these GMTs for NaV1.7. First, FEP predicted these relative potencies with an overall root mean square error (RMSE) of 1.0 ± 0.1 kcal/mol and an R2 value of 0.66, equivalent to experimental uncertainty and an improvement over the widely used molecular-mechanics/generalized born-surface area (MM-GB/SA) RBFE method that had an RMSE of 3.9 ± 0.8 kcal/mol. Second, inclusion of an explicit membrane model was needed for the GMTs to maintain stable binding poses during the FEP simulations. Third, MM-GB/SA and FEP were used to identify fifteen non-standard tryptophan mutants at ProTx-II[W24] predicted in silico to have a at least a 1 kcal/mol gain in potency. These predicted potency gains are likely due to the displacement of high-energy waters as identified by the WaterMap algorithm for calculating the positions and thermodynamic properties of water molecules in protein binding sites. Our results expand the domain of applicability of FEP and set the stage for its prospective use in biologics drug discovery programs involving GMTs and NaV1.7.
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Affiliation(s)
| | | | | | - Abba E. Leffler
- Schrӧdinger, Inc., 120 West 45th St., New York, NY 10036, USA; (D.K.); (D.S.); (M.D.)
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49
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Shah B, Sindhikara D, Borrelli K, Leffler AE. Water Thermodynamics of Peptide Toxin Binding Sites on Ion Channels. Toxins (Basel) 2020; 12:toxins12100652. [PMID: 33053750 PMCID: PMC7599640 DOI: 10.3390/toxins12100652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/21/2020] [Accepted: 10/09/2020] [Indexed: 12/20/2022] Open
Abstract
Peptide toxins isolated from venomous creatures, long prized as research tools due to their innate potency for ion channels, are emerging as drugs as well. However, it remains challenging to understand why peptide toxins bind with high potency to ion channels, to identify residues that are key for activity, and to improve their affinities via mutagenesis. We use WaterMap, a molecular dynamics simulation-based method, to gain computational insight into these three questions by calculating the locations and thermodynamic properties of water molecules in the peptide toxin binding sites of five ion channels. These include an acid-sensing ion channel, voltage-gated potassium channel, sodium channel in activated and deactivated states, transient-receptor potential channel, and a nicotinic receptor whose structures were recently determined by crystallography and cryo-electron microscopy (cryo-EM). All channels had water sites in the peptide toxin binding site, and an average of 75% of these sites were stable (low-energy), and 25% were unstable (medium or high energy). For the sodium channel, more unstable water sites were present in the deactivated state structure than the activated. Additionally, for each channel, unstable water sites coincided with the positions of peptide toxin residues that previous mutagenesis experiments had shown were important for activity. Finally, for the sodium channel in the deactivated state, unstable water sites were present in the peptide toxin binding pocket but did not overlap with the peptide toxin, suggesting that future experimental efforts could focus on targeting these sites to optimize potency.
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Affiliation(s)
- Binita Shah
- Schrödinger, Inc. 120 W. 45th St, New York, NY 10036, USA; (B.S.); (D.S.); (K.B.)
- PhD Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA 02115, USA
| | - Dan Sindhikara
- Schrödinger, Inc. 120 W. 45th St, New York, NY 10036, USA; (B.S.); (D.S.); (K.B.)
| | - Ken Borrelli
- Schrödinger, Inc. 120 W. 45th St, New York, NY 10036, USA; (B.S.); (D.S.); (K.B.)
| | - Abba E. Leffler
- Schrödinger, Inc. 120 W. 45th St, New York, NY 10036, USA; (B.S.); (D.S.); (K.B.)
- Correspondence:
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Srivastava P, Choudhury A, Talwar M, Mohanty S, Narad P, Sengupta A. VIRdb: a comprehensive database for interactive analysis of genes/proteins involved in the pathogenesis of vitiligo. PeerJ 2020; 8:e9119. [PMID: 32509450 PMCID: PMC7246032 DOI: 10.7717/peerj.9119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/13/2020] [Indexed: 01/04/2023] Open
Abstract
Vitiligo is a chronic asymptomatic disorder affecting melanocytes from the basal layer of the epidermis which leads to a patchy loss of skin color. Even though it is one of the neglected disease conditions, people suffering from vitiligo are more prone to psychological disorders. As of now, various studies have been done in order to project auto-immune implications as the root cause. To understand the complexity of vitiligo, we propose the Vitiligo Information Resource (VIRdb) that integrates both the drug-target and systems approach to produce a comprehensive repository entirely devoted to vitiligo, along with curated information at both protein level and gene level along with potential therapeutics leads. These 25,041 natural compounds are curated from Natural Product Activity and Species Source Database. VIRdb is an attempt to accelerate the drug discovery process and laboratory trials for vitiligo through the computationally derived potential drugs. It is an exhaustive resource consisting of 129 differentially expressed genes, which are validated through gene ontology and pathway enrichment analysis. We also report 22 genes through enrichment analysis which are involved in the regulation of epithelial cell differentiation. At the protein level, 40 curated protein target molecules along with their natural hits that are derived through virtual screening. We also demonstrate the utility of the VIRdb by exploring the Protein-Protein Interaction Network and Gene-Gene Interaction Network of the target proteins and differentially expressed genes. For maintaining the quality and standard of the data in the VIRdb, the gold standard in bioinformatics toolkits like Cytoscape, Schrödinger's GLIDE, along with the server installation of MATLAB, are used for generating results. VIRdb can be accessed through "http://www.vitiligoinfores.com/".
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Affiliation(s)
| | - Alakto Choudhury
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Mehak Talwar
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Sabyasachi Mohanty
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Priyanka Narad
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Abhishek Sengupta
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
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