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Mevizou R, Aouad H, Sauvage FL, Arnion H, Pinault E, Bernard JS, Bertho G, Giraud N, de Sousa RA, Lopez-Noriega A, Di Meo F, Campana M, Marquet P. Revisiting the nature and pharmacodynamics of tacrolimus metabolites. Pharmacol Res 2024; 209:107438. [PMID: 39357691 DOI: 10.1016/j.phrs.2024.107438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Revised: 09/27/2024] [Accepted: 09/27/2024] [Indexed: 10/04/2024]
Abstract
The toxicity of tacrolimus metabolites and their potential pharmacodynamic (PD) interactions with tacrolimus might respectively explain the surprising combination of higher toxicity and lower efficacy of tacrolimus despite normal blood concentrations, described in extensive metabolizers. To evaluate such interactions, we produced tacrolimus metabolites in vitro and characterized them by high resolution mass spectrometry (HRMS, for all) and nuclear magnetic resonance (NMR, for the most abundant, M-I). We quantified tacrolimus metabolites and checked their structure in patient whole blood and peripheral blood mononuclear cells (PBMC). We explored the interactions of M-I with tacrolimus in silico, in vitro and ex vivo. In vitro metabolization produced isoforms of tacrolimus and of its metabolites M-I and M-III, whose HRMS fragmentation suggested an open-ring structure. M-I and M-III open-ring isomers were also observed in patient blood. By contrast, NMR could not detect these open-ring forms. Transplant patients expressing CYP3A5 exhibited higher M-I/TAC ratios in blood and PBMC than non-expressers. Molecular Dynamics simulations showed that: all possible tacrolimus metabolites and isomers bind FKPB12; and the hypothetical open-ring structures induce looser binding between FKBP12 and calcineurins, leading to lower CN inhibition. In vitro, tacrolimus bound FKPB12 with more affinity than purified M-I, and the pool of tacrolimus metabolites and purified M-I had only weak inhibitory activity on IL2 secretion and not at all on NFAT nuclear translocation. M-I showed no competitive effect with tacrolimus on either test. Finally, M-I or the metabolite pool did not significantly interact with tacrolimus MLR suppression, thus eliminating a pharmacodynamic interaction.
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Affiliation(s)
- Rudy Mevizou
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France; Medincell, Jacou, France
| | - Hassan Aouad
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France
| | | | - Hélène Arnion
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France
| | - Emilie Pinault
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France; Université de Limoges, CNRS, Inserm, CHU Limoges, UAR2015, US42, Integrative Biology Health Chemistry and Environment BISCEm, Limoges, France
| | - Jean-Sébastien Bernard
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France
| | - Gildas Bertho
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Nicolas Giraud
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | - Rodolphe Alves de Sousa
- Université Paris Cité, CNRS, Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Paris, France
| | | | - Florent Di Meo
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France; Université de Limoges, CNRS, Inserm, CHU Limoges, UAR2015, US42, Integrative Biology Health Chemistry and Environment BISCEm, Limoges, France
| | | | - Pierre Marquet
- Université de Limoges, Inserm, UMR1248, Pharmacology & Transplantation P&T, Limoges, France; Department of pharmacology, toxicology and pharmacovigilance, Centre Hospitalier Universitaire de Limoges, Limoges, France.
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2
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Diakogiannaki I, Papadourakis M, Spyridaki V, Cournia Z, Koutselos A. Computational Investigation of BMAA and Its Carbamate Adducts as Potential GluR2 Modulators. J Chem Inf Model 2024; 64:5140-5150. [PMID: 38973304 PMCID: PMC11234361 DOI: 10.1021/acs.jcim.3c01195] [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] [Received: 07/31/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 07/09/2024]
Abstract
Beta-N-methylamino-l-alanine (BMAA) is a potential neurotoxic nonprotein amino acid, which can reach the human body through the food chain. When BMAA interacts with bicarbonate in the human body, carbamate adducts are produced, which share a high structural similarity with the neurotransmitter glutamate. It is believed that BMAA and its l-carbamate adducts bind in the glutamate binding site of ionotropic glutamate receptor 2 (GluR2). Chronic exposure to BMAA and its adducts could cause neurological illness such as neurodegenerative diseases. However, the mechanism of BMAA action and its carbamate adducts bound to GluR2 has not yet been elucidated. Here, we investigate the binding modes and the affinity of BMAA and its carbamate adducts to GluR2 in comparison to the natural agonist, glutamate, to understand whether these can act as GluR2 modulators. Initially, we perform molecular dynamics simulations of BMAA and its carbamate adducts bound to GluR2 to examine the stability of the ligands in the S1/S2 ligand-binding core of the receptor. In addition, we utilize alchemical free energy calculations to compute the difference in the free energy of binding of the beta-carbamate adduct of BMAA to GluR2 compared to that of glutamate. Our findings indicate that carbamate adducts of BMAA and glutamate remain stable in the binding site of the GluR2 compared to BMAA. Additionally, alchemical free energy results reveal that glutamate and the beta-carbamate adduct of BMAA have comparable binding affinity to the GluR2. These results provide a rationale that BMAA carbamate adducts may be, in fact, the modulators of GluR2 and not BMAA itself.
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Affiliation(s)
- Isidora Diakogiannaki
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, Athens 11527, Greece
- Department
of Chemistry, Physical Chemistry Laboratory, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece
| | - Michail Papadourakis
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, Athens 11527, Greece
- Department
of Nursing, Faculty of Health Sciences, Hellenic Mediterranean University, Heraklion, Crete 71004, Greece
| | - Vasileia Spyridaki
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, Athens 11527, Greece
- School
of Chemical Engineering, National Technical
University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
| | - Zoe Cournia
- Biomedical
Research Foundation, Academy of Athens, 4 Soranou Ephessiou, Athens 11527, Greece
| | - Andreas Koutselos
- Department
of Chemistry, Physical Chemistry Laboratory, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece
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3
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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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4
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Pandey U, Behara SM, Sharma S, Patil RS, Nambiar S, Koner D, Bhukya H. DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences. J Chem Inf Model 2024; 64:1806-1815. [PMID: 38458968 DOI: 10.1021/acs.jcim.3c01151] [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: 03/10/2024]
Abstract
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.
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Affiliation(s)
- Uddeshya Pandey
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Sasi M Behara
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Siddhant Sharma
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Rachit S Patil
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Souparnika Nambiar
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
| | - Debasish Koner
- Department of Chemistry, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Hussain Bhukya
- Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India
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5
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Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J Chem Inf Model 2024; 64:1481-1485. [PMID: 38376463 PMCID: PMC11214867 DOI: 10.1021/acs.jcim.3c02031] [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: 02/21/2024]
Abstract
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Raimondas Galvelis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Emilio Gallicchio
- Department of Chemistry, Graduate Center, Brooklyn College, City University of New York, New York, New York 11210, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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6
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Khuttan S, Gallicchio E. What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling. J Chem Theory Comput 2024; 20:1489-1501. [PMID: 38252868 PMCID: PMC10867849 DOI: 10.1021/acs.jctc.3c01250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.
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Affiliation(s)
- Sheenam Khuttan
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
| | - Emilio Gallicchio
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
- Ph.D.
Program in Chemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
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7
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Yi C, Taylor ML, Ziebarth J, Wang Y. Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity. ACS OMEGA 2024; 9:3454-3468. [PMID: 38284090 PMCID: PMC10809705 DOI: 10.1021/acsomega.3c06996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 01/30/2024]
Abstract
Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.
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Affiliation(s)
- Carey
Huang Yi
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Mitchell Lee Taylor
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Jesse Ziebarth
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Yongmei Wang
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
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8
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Chen L, Wu Y, Wu C, Silveira A, Sherman W, Xu H, Gallicchio E. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. J Chem Inf Model 2024; 64:250-264. [PMID: 38147877 DOI: 10.1021/acs.jcim.3c01705] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
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Affiliation(s)
- Lieyang Chen
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Yujie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Chuanjie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Ana Silveira
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Huafeng Xu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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9
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Sabanés Zariquiey F, Pérez A, Majewski M, Gallicchio E, De Fabritiis G. Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series. J Chem Inf Model 2023; 63:2438-2444. [PMID: 37042797 PMCID: PMC10577236 DOI: 10.1021/acs.jcim.3c00178] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Adrià Pérez
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | | | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- PhD Program in Chemistry Graduate Center of the City University of New York, New York, New York 10016, United States
- PhD Program in Biochemistry, Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera, Devonshire House 582 Honeypot Lane, Stanmore, Middlesex HA7 1JS, United Kingdom
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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10
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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11
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Kasavajhala K, Simmerling C. Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations. J Chem Theory Comput 2023; 19:1931-1944. [PMID: 36861842 PMCID: PMC10658647 DOI: 10.1021/acs.jctc.3c00005] [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] [Indexed: 03/03/2023]
Abstract
Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.
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Affiliation(s)
- Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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12
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Pal S, Kumar A, Vashisth H. Role of Dynamics and Mutations in Interactions of a Zinc Finger Antiviral Protein with CG-rich Viral RNA. J Chem Inf Model 2023; 63:1002-1011. [PMID: 36707411 PMCID: PMC10129844 DOI: 10.1021/acs.jcim.2c01487] [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: 01/29/2023]
Abstract
Zinc finger antiviral protein (ZAP) is a host antiviral factor that selectively inhibits the replication of a variety of viruses. ZAP recognizes the CG-enriched RNA sequences and activates the viral RNA degradation machinery. In this work, we investigated the dynamics of a ZAP/RNA complex and computed the energetics of mutations in ZAP that affect its binding to the viral RNA. The crystal structure of a mouse-ZAP/RNA complex showed that RNA interacts with the zinc finger 2 (ZF2) and ZF3 domains. However, we found that due to the dynamic behavior of the single-stranded RNA, the terminal nucleotides C1 and G2 of RNA change their positions from the ZF3 to the ZF1 domain. Moreover, the electrostatic interactions between the zinc ions and the viral RNA provide further stability to the ZAP/RNA complex. We also provide structural and thermodynamic evidence for seven residue pairs (C1-Arg74, C1-Arg179, G2-Arg74, U3-Lys76, C4-Lys76, G5-Arg95, and U6-Glu204) that show favorable ZAP/RNA interactions, although these interactions were not observed in the ZAP/RNA crystal structure. Consistent with the observations from the mouse-ZAP/RNA crystal structure, we found that four residue pairs (C4-Lys89, C4-Leu90, C4-Tyr108, and G5-Lys107) maintained stable interactions in MD simulations. Based on experimental mutagenesis studies and our residue-level interaction analysis, we chose seven residues (Arg74, Lys76, Lys89, Arg95, Lys107, Tyr108, and Arg179) for individual alanine mutations. In addition, we studied mutations in those residues that are only observed in the crystal structures as interacting with RNA (Tyr98, Glu148, and Arg170). Out of these 10 mutations, we found that the Ala mutation in each of the five residues Arg74, Lys76, Lys89, Lys107, and Glu148 significantly reduced the binding affinity of ZAP to RNA.
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Affiliation(s)
- Saikat Pal
- Department of Chemical Engineering, University of New Hampshire, Durham, New Hampshire03824, United States
| | - Amit Kumar
- Department of Chemical Engineering, University of New Hampshire, Durham, New Hampshire03824, United States
| | - Harish Vashisth
- Department of Chemical Engineering, University of New Hampshire, Durham, New Hampshire03824, United States
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13
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Love O, Pacheco Lima MC, Clark C, Cornillie S, Roalstad S, Cheatham TE. Evaluating the accuracy of the AMBER protein force fields in modeling dihydrofolate reductase structures: misbalance in the conformational arrangements of the flexible loop domains. J Biomol Struct Dyn 2022:1-15. [PMID: 35838167 PMCID: PMC9840716 DOI: 10.1080/07391102.2022.2098823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Protein flexible loop regions were once thought to be simple linkers between other more functional secondary structural elements. However, as it becomes clearer that these loop domains are critical players in a plethora of biological processes, accurate conformational sampling of 3D loop structures is vital to the advancement of drug design techniques and the overall growth of knowledge surrounding molecular systems. While experimental techniques provide a wealth of structural information, the resolution of flexible loop domains is sometimes low or entirely absent due to their complex and dynamic nature. This highlights an opportunity for de novo structure prediction using in silico methods with molecular dynamics (MDs). This study evaluates some of the AMBER protein force field's (ffs) ability to accurately model dihydrofolate reductase (DHFR) conformations, a protein complex characterized by specific arrangements and interactions of multiple flexible loops whose conformations are determined by the presence or absence of bound ligands and cofactors. Although the AMBER ffs, including ff19SB, studied well model most protein structures with rich secondary structure, results obtained here suggest the inability to significantly sample the expected DHFR loop-loop conformations - of the six distinct protein-ligand systems simulated, a majority lacked consistent stabilization of experimentally derived metrics definitive the three enzyme conformations. Although under-sampling and the chosen ff parameter combinations could be the cause, given past successes with these MD approaches for many protein systems, this suggests a potential misbalance in available ff parameters required to accurately predict the structure of multiple flexible loop regions present in proteins.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Olivia Love
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | | | - Casey Clark
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Sean Cornillie
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Shelly Roalstad
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Thomas E. Cheatham
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
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14
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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15
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Pan X, Wang H, Zhang Y, Wang X, Li C, Ji C, Zhang JZH. AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking. J Chem Inf Model 2022; 62:2499-2509. [PMID: 35452230 DOI: 10.1021/acs.jcim.1c01537] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.
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Affiliation(s)
- Xiaolin Pan
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - Hao Wang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - Yueqing Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - Xingyu Wang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - Cuiyu Li
- Advanced Computing East China Sub-center, Suma Technology Co., Ltd., Kunshan 215300, China
| | - Changge Ji
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - John Z H Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.,Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China.,Department of Chemistry, New York University, New York 10003, United States.,Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan Shanxi 030006, China
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16
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Yaqoob S, Hameed A, Ahmed M, Imran M, Qadir MA, Ramzan M, Yousaf N, Iqbal J, Muddassar M. Antiurease screening of alkyl chain-linked thiourea derivatives: in vitro biological activities, molecular docking, and dynamic simulations studies. RSC Adv 2022; 12:6292-6302. [PMID: 35424581 PMCID: PMC8981555 DOI: 10.1039/d1ra08694d] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/08/2022] [Indexed: 12/23/2022] Open
Abstract
Urease has become an important therapeutic target because it stimulates the pathogenesis of many human health conditions, such as pyelonephritis, the development of urolithiasis, hepatic encephalopathy, peptic ulcers, gastritis and gastric cancer. A series of alkyl chain-linked thiourea derivatives were synthesized to screen for urease inhibition activity. Structure elucidation of these compounds was done by spectral studies, such as IR, 1H NMR and 13C NMR, and MS analysis. In vitro urease enzyme inhibition assay revealed that compound 3c was the most potent thiourea derivative among the series with IC50 values of 10.65 ± 0.45 μM, while compound 3g also exhibited good activity with an IC50 value of 15.19 ± 0.58 μM compared to standard thiourea with an IC50 value of 15.51 ± 0.11 μM. The other compounds in the series possessed moderate to weak urease inhibition activity with IC50 values ranging from 20.16 ± 0.48 to 60.11 ± 0.78 μM. The most potent compounds 3c and 3g were docked to jack bean urease (PDB ID: 4H9M) to evaluate their binding affinities and to find the plausible binding poses. The docked complexes were refined through 100 ns-long MD simulations. The simulation results revealed that the average RMSD of 3c was less than that of the 3g compound. Furthermore, the radius of gyration plots for both complexes showed that 3c and 3g docking predicted binding modes did not induce any conformational change in the urease structure.
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Affiliation(s)
- Sana Yaqoob
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi Karachi Pakistan
| | - Abdul Hameed
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi Karachi Pakistan
- Department of Chemistry, University of Sahiwal Sahiwal Pakistan
| | - Mahmood Ahmed
- Department of Chemistry, Division of Science and Technology, University of Education College Road Lahore Pakistan
| | - Muhammad Imran
- KAM-School of Life Sciences, FC College (A Chartered University) Lahore Pakistan
| | | | - Mahwish Ramzan
- Department of Biosciences, COMSATS University Islamabad Park Road Islamabad Pakistan
| | - Numan Yousaf
- Department of Biosciences, COMSATS University Islamabad Park Road Islamabad Pakistan
| | - Jamshed Iqbal
- Center for Advanced Drug Research, COMSATS Institute of Information Technology Abbottabad 22060 Pakistan
| | - Muhammad Muddassar
- Department of Biosciences, COMSATS University Islamabad Park Road Islamabad Pakistan
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17
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Azimi S, Khuttan S, Wu JZ, Pal RK, Gallicchio E. Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method. J Chem Inf Model 2022; 62:309-323. [PMID: 34990555 DOI: 10.1021/acs.jcim.1c01129] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present an extension of the alchemical transfer method (ATM) for the estimation of relative binding free energies of molecular complexes applicable to conventional, as well as scaffold-hopping, alchemical transformations. Named ATM-RBFE, the method is implemented in the free and open-source OpenMM molecular simulation package and aims to provide a simpler and more generally applicable route to the calculation of relative binding free energies than what is currently available. ATM-RBFE is based on sound statistical mechanics theory and a novel coordinate perturbation scheme designed to swap the positions of a pair of ligands such that one is transferred from the bulk solvent to the receptor binding site while the other moves simultaneously in the opposite direction. The calculation is conducted directly in a single solvent box with a system prepared with conventional setup tools, without splitting of electrostatic and nonelectrostatic transformations, and without pairwise soft-core potentials. ATM-RBFE is validated here against the absolute binding free energies of the SAMPL8 GDCC host-guest benchmark set and against protein-ligand benchmark sets that include complexes of the estrogen receptor ERα and those of the methyltransferase EZH2. In each case the method yields self-consistent and converged relative binding free energy estimates in agreement with absolute binding free energies and reference literature values, as well as experimental measurements.
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Affiliation(s)
- Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Joe Z Wu
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Rajat K Pal
- Roivant Sciences, Inc., Boston, Massachusetts 02210, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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18
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Liu C, Brini E, Dill KA. Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins. J Chem Theory Comput 2021; 18:374-379. [PMID: 34877865 DOI: 10.1021/acs.jctc.1c00855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular docking algorithms are used to seek the most active compounds from a pool of ligands. In principle, molecular dynamics (MD) simulations with accurate physical potentials and sampling could yield better enrichments, but they are computationally expensive. Here, we describe a method called MELD-Bracket that utilizes biased replica exchange ladders in MD in order to compete different ligands against each other within a fast bracket style "binding tournament". MELD-Bracket finds best-binders rapidly when ligands are well separated in their binding affinities.
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Affiliation(s)
- Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, United States
| | - Emiliano Brini
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, New York 14623, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, United States.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11790, United States
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19
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Tissue-Nonspecific Alkaline Phosphatase (TNAP) as the Enzyme Involved in the Degradation of Nucleotide Analogues in the Ligand Docking and Molecular Dynamics Approaches. Biomolecules 2021; 11:biom11081104. [PMID: 34439771 PMCID: PMC8391816 DOI: 10.3390/biom11081104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/16/2021] [Accepted: 07/20/2021] [Indexed: 11/17/2022] Open
Abstract
Tissue-nonspecific alkaline phosphatase (TNAP) is known to be involved in the degradation of extracellular ATP via the hydrolysis of pyrophosphate (PPi). We investigated, using three different computational methods, namely molecular docking, thermodynamic integration (TI) and conventional molecular dynamics (MD), whether TNAP may also be involved in the utilization of β,γ-modified ATP analogues. For that, we analyzed the interaction of bisphosphonates with this enzyme and evaluated the obtained structures using in silico studies. Complexes formed between pyrophosphate, hypophosphate, imidodiphosphate, methylenediphosphonic acid monothiopyrophosphate, alendronate, pamidronate and zoledronate with TNAP were generated and analyzed based on ligand docking, molecular dynamics and thermodynamic integration. The obtained results indicate that all selected ligands show high affinity toward this enzyme. The forming complexes are stabilized through hydrogen bonds, electrostatic interactions and van der Waals forces. Short- and middle-term molecular dynamics simulations yielded very similar affinity results and confirmed the stability of the protein and its complexes. The results suggest that certain effectors may have a significant impact on the enzyme, changing its properties.
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20
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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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21
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Lin Z, Zou J, Liu S, Peng C, Li Z, Wan X, Fang D, Yin J, Gobbo G, Chen Y, Ma J, Wen S, Zhang P, Yang M. A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Predictions: New Opportunities and Challenges for Drug Discovery. J Chem Inf Model 2021; 61:2720-2732. [PMID: 34086476 DOI: 10.1021/acs.jcim.0c01329] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. However, limitations of FEP applications also exist, including, but not limited to, high cost, long waiting time, limited scalability, and breadth of application scenarios. To overcome these problems, we have developed XFEP, a scalable cloud computing platform for both relative and absolute free energy predictions using optimized simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable, and affordable way, for example, the evaluation of 5000 compounds can be performed in 1 week using 50-100 GPUs with a computing cost roughly equivalent to the cost for the synthesis of only one new compound. By combining these capabilities with artificial intelligence techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization based not only on structure exploitation within the given chemical series but also including evaluation and comparison of completely unrelated molecules during structure exploration in a larger chemical space. XFEP provides the basis for scalable FEP applications to become more widely used in drug discovery projects and to speed up the drug discovery process from hit identification to preclinical candidate compound nomination.
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Affiliation(s)
- 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
| | - 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
| | - Shuai Liu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China.,XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 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
| | - 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
| | - 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
| | - 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
| | - Jian Yin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Gianpaolo Gobbo
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Yongpan Chen
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Jian Ma
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Shuhao Wen
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China.,XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Peiyu Zhang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - 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
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22
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Wu JZ, Azimi S, Khuttan S, Deng N, Gallicchio E. Alchemical Transfer Approach to Absolute Binding Free Energy Estimation. J Chem Theory Comput 2021; 17:3309-3319. [PMID: 33983730 DOI: 10.1021/acs.jctc.1c00266] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The alchemical transfer method (ATM) for the calculation of standard binding free energies of noncovalent molecular complexes is presented. The method is based on a coordinate displacement perturbation of the ligand between the receptor binding site and the explicit solvent bulk and a thermodynamic cycle connected by a symmetric intermediate in which the ligand interacts with the receptor and solvent environments with equal strength. While the approach is alchemical, the implementation of the ATM is as straightforward as that for physical pathway methods of binding. The method is applicable, in principle, with any force field, as it does not require splitting the alchemical transformations into electrostatic and nonelectrostatic steps, and it does not require soft-core pair potentials. We have implemented the ATM as a freely available and open-source plugin of the OpenMM molecular dynamics library. The method and its implementation are validated on the SAMPL6 SAMPLing host-guest benchmark set. The work paves the way to streamlined alchemical relative and absolute binding free energy implementations on many molecular simulation packages and with arbitrary energy functions including polarizable, quantum-mechanical, and artificial neural network potentials.
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Affiliation(s)
- Joe Z Wu
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York 10038, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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23
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Forouzesh N, Mishra N. An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor. Molecules 2021; 26:2383. [PMID: 33923909 PMCID: PMC8074138 DOI: 10.3390/molecules26082383] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 12/23/2022] Open
Abstract
The binding free energy calculation of protein-ligand complexes is necessary for research into virus-host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras-Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: -14.7(ΔGbindBondi)<-10.6(ΔGbindExp.)<-4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, CA 90032, USA
| | - Nikita Mishra
- Department of Chemistry and Biochemistry, California State University, Los Angeles, CA 90032, USA;
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24
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King E, Qi R, Li H, Luo R, Aitchison E. Estimating the Roles of Protonation and Electronic Polarization in Absolute Binding Affinity Simulations. J Chem Theory Comput 2021; 17:2541-2555. [PMID: 33764050 DOI: 10.1021/acs.jctc.0c01305] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Accurate prediction of binding free energies is critical to streamlining the drug development and protein design process. With the advent of GPU acceleration, absolute alchemical methods, which simulate the removal of ligand electrostatics and van der Waals interactions with the protein, have become routinely accessible and provide a physically rigorous approach that enables full consideration of flexibility and solvent interaction. However, standard explicit solvent simulations are unable to model protonation or electronic polarization changes upon ligand transfer from water to the protein interior, leading to inaccurate prediction of binding affinities for charged molecules. Here, we perform extensive simulation totaling ∼540 μs to benchmark the impact of modeling conditions on predictive accuracy for absolute alchemical simulations. Binding to urokinase plasminogen activator (UPA), a protein frequently overexpressed in metastatic tumors, is evaluated for a set of 10 inhibitors with extended flexibility, highly charged character, and titratable properties. We demonstrate that the alchemical simulations can be adapted to utilize the MBAR/PBSA method to improve the accuracy upon incorporating electronic polarization, highlighting the importance of polarization in alchemical simulations of binding affinities. Comparison of binding energy prediction at various protonation states indicates that proper electrostatic setup is also crucial in binding affinity prediction of charged systems, prompting us to propose an alternative binding mode with protonated ligand phenol and Hid-46 at the binding site, a testable hypothesis for future experimental validation.
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Affiliation(s)
| | - Ruxi Qi
- Cryo-EM Center, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
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25
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Zou J, Xiao S, Simmerling C, Raleigh DP. Quantitative Analysis of Protein Unfolded State Energetics: Experimental and Computational Studies Demonstrate That Non-Native Side-Chain Interactions Stabilize Local Native Backbone Structure. J Phys Chem B 2021; 125:3269-3277. [PMID: 33779182 DOI: 10.1021/acs.jpcb.0c08922] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Proteins fold on relatively smooth free energy landscapes which are biased toward the native state, but even simple topologies which fold rapidly can experience roughness on their free energy landscape. The details of these interactions are difficult to elucidate experimentally. Closely related to the problem of deciphering the details of the free energy landscape is the problem of defining the interactions in the denatured state ensemble (DSE) which is populated under native conditions, that is, under conditions where the native state is stable. The DSE of many proteins deviates from random coil models, but quantifying and defining the energetics of the transiently populated interactions in this ensemble is extremely challenging. Characterization of the DSE of proteins which fold to compact structures is also relevant to studies of intrinsically disordered proteins (IDPs) since interactions in the dynamic ensemble populated by IDPs can modulate their behavior. Here we show how experimental thermodynamic and pKa measurements can be combined with computational thermodynamic integration to quantify interactions in the DSE. We show that non-native side chain interactions can stabilize native backbone structure in the DSE and demonstrate that that even rapidly folding proteins can form energetically significant non-native interactions in their DSE. As an example, we characterize a non-native salt bridge that stabilizes local native backbone structure in the DSE of a widely studied fast-folding protein, the villin headpiece helical domain. The combined computational experimental approach is applicable to other protein unfolded states and provides insight that is impossible to obtain with either method alone.
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Affiliation(s)
- Junjie Zou
- 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
| | - Shifeng Xiao
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, 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
| | - 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
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26
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Brown BP, Mendenhall J, Geanes AR, Meiler J. General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps. J Chem Inf Model 2021; 61:603-620. [PMID: 33496578 PMCID: PMC7903419 DOI: 10.1021/acs.jcim.0c01001] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Indexed: 12/20/2022]
Abstract
The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.
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Affiliation(s)
- Benjamin P. Brown
- Chemical
and Physical Biology Program, Medical Scientist Training Program,
Center for Structural Biology, Vanderbilt
University, Nashville, Tennessee 37232, United States
| | - Jeffrey Mendenhall
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Alexander R. Geanes
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Departments
of Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37212, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig SAC 04103, Germany
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27
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Abstract
Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.
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Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. .,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New NY 11794, USA
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28
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Bao J, He X, Zhang JZ. Development of a New Scoring Function for Virtual Screening: APBScore. J Chem Inf Model 2020; 60:6355-6365. [DOI: 10.1021/acs.jcim.0c00474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Jingxiao Bao
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China
| | - John Z.H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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29
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Forouzesh N, Onufriev AV. MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.08.25.267625. [PMID: 32869029 PMCID: PMC7457614 DOI: 10.1101/2020.08.25.267625] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The ability to estimate protein-protein binding free energy in a computationally efficient via a physics-based approach is beneficial to research focused on the mechanism of viruses binding to their target proteins. Implicit solvation methodology may be particularly useful in the early stages of such research, as it can offer valuable insights into the binding process, quickly. Here we evaluate the potential of the related molecular mechanics generalized Born surface area (MMGB/SA) approach to estimate the binding free energy ΔGbind between the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. The calculations are based on a recent flavor of the generalized Born model, GBNSR6. Two estimates of ΔGbind are performed: one based on standard bondi radii, and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. We take the average of the resulting two ΔGbind values as the consensus estimate. For the well-studied Ras-Raf protein-protein complex, which has similar binding free energy to that of the SARS-CoV-2/ACE2 complex, the consensus ΔGbind = -11.8 ± 1 kcal/mol, vs. experimental -9.7 ± 0.2 kcal/mol. The consensus estimates for the SARS-CoV-2/ACE2 complex is ΔGbind = -9.4 ± 1.5 kcal/mol, which is in near quantitative agreement with experiment (-10.6 kcal/mol). The availability of a conceptually simple MMGB/SA-based protocol for analysis of the SARS-CoV-2 /ACE2 binding may be beneficial in light of the need to move forward fast.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, Los Angeles, CA 90032, USA
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061, USA
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30
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Forouzesh N, Mukhopadhyay A, Watson LT, Onufriev AV. Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding. J Chem Theory Comput 2020; 16:4669-4684. [PMID: 32450041 PMCID: PMC8594251 DOI: 10.1021/acs.jctc.0c00142] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accuracy of protein-ligand binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a global multidimensional optimization pipeline is developed to find optimal atomic radii specifically for protein-ligand binding calculations in implicit solvent. The computational pipeline has these three key components: (1) a massively parallel implementation of a deterministic global optimization algorithm (VTDIRECT95), (2) an accurate yet reasonably fast generalized Born implicit solvent model (GBNSR6), and (3) a novel robustness metric that helps distinguish between nearly degenerate local minima via a postprocessing step of the optimization. A graph-based "kT-connectivity" approach to explore and visualize the multidimensional energy landscape is proposed: local minima that can be reached from the global minimum without exceeding a given energy threshold (kT) are considered to be connected. As an illustration of the capabilities of the optimization pipeline, we apply it to find a global optimum in the space of just five radii: four atomic (O, H, N, and C) radii and water probe radius. The optimized radii, ρW = 1.37 Å, ρC = 1.40 Å, ρH = 1.55 Å, ρN = 2.35 Å, and ρO = 1.28 Å, lead to a closer agreement of electrostatic binding free energies with the explicit solvent reference than two commonly used sets of radii previously optimized for small molecules. At the same time, the ability of the optimizer to find the global optimum reveals fundamental limits of the common two-dielectric implicit solvation model: the computed electrostatic binding free energies are still almost 4 kcal/mol away from the explicit solvent reference. The proposed computational approach opens the possibility to further improve the accuracy of practical computational protocols for binding free energy calculations.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Abhishek Mukhopadhyay
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Layne T Watson
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Mathematics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
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31
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Diller DJ, Swanson J, Bayden AS, Brown CJ, Thean D, Lane DP, Partridge AW, Sawyer TK, Audie J. Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides. Molecules 2019; 24:E4586. [PMID: 31847417 PMCID: PMC6943714 DOI: 10.3390/molecules24244586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 12/25/2022] Open
Abstract
There is interest in peptide drug design, especially for targeting intracellular protein-protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.
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Affiliation(s)
- David J. Diller
- CMDBioscience, 5 Park Avenue, New Haven, CT 06511, USA; (D.J.D.); (J.S.); (A.S.B.)
- Venenum BioDesign, LLC, 8 Black Forest Road, Hamilton, NJ 08691, USA
| | - Jon Swanson
- CMDBioscience, 5 Park Avenue, New Haven, CT 06511, USA; (D.J.D.); (J.S.); (A.S.B.)
- ChemModeling, LLC, Suite 101, 500 Huber Park Ct, Weldon Spring, MO 63304, USA
| | - Alexander S. Bayden
- CMDBioscience, 5 Park Avenue, New Haven, CT 06511, USA; (D.J.D.); (J.S.); (A.S.B.)
- Kleo Pharmaceuticals, 25 Science Park, Ste 235, New Haven, CT 06511, USA
| | - Chris J. Brown
- A*STAR, p53 Laboratory, Singapore 138648, Singapore; (C.J.B.); (D.T.); (D.P.L.)
| | - Dawn Thean
- A*STAR, p53 Laboratory, Singapore 138648, Singapore; (C.J.B.); (D.T.); (D.P.L.)
| | - David P. Lane
- A*STAR, p53 Laboratory, Singapore 138648, Singapore; (C.J.B.); (D.T.); (D.P.L.)
| | - Anthony W. Partridge
- MSD International GmbH, Singapore 138665, Singapore;
- Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Tomi K. Sawyer
- Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Joseph Audie
- CMDBioscience, 5 Park Avenue, New Haven, CT 06511, USA; (D.J.D.); (J.S.); (A.S.B.)
- College of Arts and Sciences, Department of Chemistry, Sacred Heart University, 5151 Park Avenue, Fairfield, CT 06825, USA
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