1
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Zheng L, Shi F, Peng C, Xu M, Fan F, Li Y, Zhang L, Du J, Wang Z, Lin Z, Sun Y, Deng C, Duan X, Wei L, Zhao C, Fang L, Zhang P, Ma S, Lai L, Yang M. Application scenario-oriented molecule generation platform developed for drug discovery. Methods 2024; 222:112-121. [PMID: 38215898 DOI: 10.1016/j.ymeth.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/22/2023] [Accepted: 12/23/2023] [Indexed: 01/14/2024] Open
Abstract
Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.
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Affiliation(s)
- Lianjun Zheng
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Fangjun Shi
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - 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
| | - Min Xu
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Fangda Fan
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Yuanpeng Li
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Lin Zhang
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Jiewen Du
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Zonghu Wang
- XtalPi Innovation Center, XtalPi Inc., Beijing, 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
| | - Yina Sun
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Chenglong Deng
- Jingtai Zhiyao Technology (Shanghai) Co., Ltd. (XtalPi), No. 207 Huanqiao Road, Pudong New Area, Shanghai 201315, China
| | - Xinli Duan
- XtalPi Innovation Center, XtalPi Inc., Beijing, China
| | - Lin Wei
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | | | - Lei 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
| | - 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
| | - Songling Ma
- XtalPi Innovation Center, XtalPi Inc., Beijing, China.
| | - Lipeng Lai
- XtalPi Innovation Center, XtalPi Inc., Beijing, 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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2
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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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3
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Jiang W. Enhanced Configurational Sampling Approaches to Alchemical Ligand Binding Free Energy Simulations: Current Status and Challenges. J Phys Chem B 2023; 127:6835-6841. [PMID: 37499215 DOI: 10.1021/acs.jpcb.3c02020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Ligand binding free energy simulations (LB-FES) have been routine tasks in modern drug discovery campaign. A long-standing challenge for LB-FES is the difficulty in adequately sampling nontrivial environmental reorganizations in response to ligand binding. Therefore, various enhanced configurational sampling (ECS) approaches were devised to speed up fluctuations of relevant slow degrees of freedom (SDOF) and ensure simulation convergence. However, in contrast to the achievements in parametrization, software performance, and workflow automation, efficient ECS methodology suitable for high throughput screening remains in an early stage of development. Here, a review of ECS developments with LB-FES is presented, revisiting current approaches and underlining the major technical pitfalls and challenges. This Perspective focuses on alchemical LB-FES on account of their predominant role in high throughput drug screening as well as the established partnership with ECS. The critical aspects of designing ECS approaches, from both theoretical and applied perspectives, are described. This work is intended to provide a contemporary review of the scientific, technical, and practical issues associated with the accelerating convergence of alchemical LB-FES.
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Affiliation(s)
- Wei Jiang
- Computational Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, Illinois 60439, United States
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4
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Baumann H, Dybeck E, McClendon CL, Pickard FC, Gapsys V, Pérez-Benito L, Hahn DF, Tresadern G, Mathiowetz AM, Mobley DL. Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach. J Chem Theory Comput 2023; 19:5058-5076. [PMID: 37487138 PMCID: PMC10413862 DOI: 10.1021/acs.jctc.3c00282] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Indexed: 07/26/2023]
Abstract
Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.
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Affiliation(s)
- Hannah
M. Baumann
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
| | - Eric Dybeck
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Christopher L. McClendon
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Frank C. Pickard
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Vytautas Gapsys
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Laura Pérez-Benito
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - David F. Hahn
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gary Tresadern
- Computational
Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Alan M. Mathiowetz
- Pfizer
Worldwide Research, Development, and Medical, 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - David L. Mobley
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
- Department
of Chemistry, University of California,
Irvine, Irvine, California 92697, United States
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5
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Macchiagodena M, Pagliai M, Procacci P. NE-RDFE: A protocol and toolkit for computing relative dissociation free energies with GROMACS between dissimilar molecules using bidirectional nonequilibrium dual topology schemes. J Comput Chem 2023; 44:1221-1230. [PMID: 36704972 DOI: 10.1002/jcc.27077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 01/07/2023] [Indexed: 01/28/2023]
Abstract
We describe a step-by-step protocol and toolkit for the computation of the relative dissociation free energy (RDFE) with the GROMACS molecular dynamics package, based on a novel bidirectional nonequilibrium alchemical approach. The proposed methodology does not require any intervention on the code and allows computing with good accuracy the RDFE between small molecules with arbitrary differences in volume, charge, and chemical topology. The procedure is illustrated for the challenging SAMPL9 batch of host-guest pairs. The article is supplemented by a detailed online tutorial, available at https://procacci.github.io/vdssb_gromacs/NE-RDFE and by a public Zenodo repository available at https://zenodo.org/record/6982932.
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Affiliation(s)
- Marina Macchiagodena
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
| | - Marco Pagliai
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
| | - Piero Procacci
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Sesto Fiorentino, Italy
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6
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Pitman M, Hahn DF, Tresadern G, Mobley DL. To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough. J Chem Inf Model 2023; 63:1776-1793. [PMID: 36878475 PMCID: PMC10547263 DOI: 10.1021/acs.jcim.2c01579] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)─a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n·ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A- or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.
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Affiliation(s)
- Mary Pitman
- Department of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - David L. Mobley
- Department of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, CA 92697, USA
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7
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Xu H. The slow but steady rise of binding free energy calculations in drug discovery. J Comput Aided Mol Des 2023; 37:67-74. [PMID: 36469232 DOI: 10.1007/s10822-022-00494-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.
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Affiliation(s)
- Huafeng Xu
- Roivant Discovery, 151 West 42nd Street, New York, NY, 10036, USA.
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8
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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9
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Procacci P. Relative Binding Free Energy between Chemically Distant Compounds Using a Bidirectional Nonequilibrium Approach. J Chem Theory Comput 2022; 18:4014-4026. [PMID: 35642423 PMCID: PMC9202353 DOI: 10.1021/acs.jctc.2c00295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Indexed: 12/02/2022]
Abstract
In the context of advanced hit-to-lead drug design based on atomistic molecular dynamics simulations, we propose a dual topology alchemical approach for calculating the relative binding free energy (RBFE) between two chemically distant compounds. The method (termed NE-RBFE) relies on the enhanced sampling of the end-states in bulk and in the bound state via Hamiltonian Replica Exchange, alchemically connected by a series of independent and fast nonequilibrium (NE) simulations. The technique has been implemented in a bidirectional fashion, applying the Crooks theorem to the NE work distributions for RBFE predictions. The dissipation of the NE process, negatively affecting accuracy, has been minimized by introducing a smooth regularization based on shifted electrostatic and Lennard-Jones non bonded potentials. As a challenging testbed, we have applied our method to the calculation of the RBFEs in the recent host-guest SAMPL international contest, featuring a macrocyclic host with guests varying in the net charge, volume, and chemical fingerprints. Closure validation has been successfully verified in cycles involving compounds with disparate Tanimoto coefficients, volume, and net charge. NE-RBFE is specifically tailored for massively parallel facilities and can be used with little or no code modification on most of the popular software packages supporting nonequilibrium alchemical simulations, such as Gromacs, Amber, NAMD, or OpenMM. The proposed methodology bypasses most of the entanglements and limitations of the standard single topology RBFE approach for strictly congeneric series based on free-energy perturbation, such as slowly relaxing cavity water, sampling issues along the alchemical stratification, and the need for highly overlapping molecular fingerprints.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica “Ugo
Schiff”, Università degli
Studi di Firenze, Via
della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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10
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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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