51
|
Amezcua M, Setiadi J, Ge Y, Mobley DL. An overview of the SAMPL8 host-guest binding challenge. J Comput Aided Mol Des 2022; 36:707-734. [PMID: 36229622 PMCID: PMC9596595 DOI: 10.1007/s10822-022-00462-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022]
Abstract
The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.
Collapse
Affiliation(s)
- Martin Amezcua
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA. .,Department of Chemistry, University of California, Irvine, CA, 92697, USA.
| |
Collapse
|
52
|
In Silico Maturation of a Nanomolar Antibody against the Human CXCR2. Biomolecules 2022; 12:biom12091285. [PMID: 36139124 PMCID: PMC9496334 DOI: 10.3390/biom12091285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
The steady increase in computational power in the last 50 years is opening unprecedented opportunities in biology, as computer simulations of biological systems have become more accessible and can reproduce experimental results more accurately. Here, we wanted to test the ability of computer simulations to replace experiments in the limited but practically useful scope of improving the biochemical characteristics of the abN48 antibody, a nanomolar antagonist of the CXC chemokine receptor 2 (CXCR2) that was initially selected from a combinatorial antibody library. Our results showed a good correlation between the computed binding energies of the antibody to the peptide target and the experimental binding affinities. Moreover, we showed that it is possible to design new antibody sequences in silico with a higher affinity to the desired target using a Monte Carlo Metropolis algorithm. The newly designed sequences had an affinity comparable to the best ones obtained using in vitro affinity maturation and could be obtained within a similar timeframe. The methodology proposed here could represent a valid alternative for improving antibodies in cases in which experiments are too expensive or technically tricky and could open an opportunity for designing antibodies for targets that have been elusive so far.
Collapse
|
53
|
Sun S, Huggins DJ. Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations. Front Mol Biosci 2022; 9:972162. [PMID: 36225254 PMCID: PMC9549959 DOI: 10.3389/fmolb.2022.972162] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Software for accurate prediction of protein-ligand binding affinity can be a key enabling tool for small molecule drug discovery. Free energy perturbation (FEP) is a computational technique that can be used to compute binding affinity differences between molecules in a congeneric series. It has shown promise in reliably generating accurate predictions and is now widely used in the pharmaceutical industry. However, the high computational cost and use of commercial software, together with the technical challenges to setup, run, and analyze the simulations, limits the usage of FEP. Here, we use an automated FEP workflow which uses the open-source OpenMM package. To enable effective application of FEP, we compared the performance of different water models, partial charge assignments, and AMBER protein forcefields in eight benchmark test cases previously assembled for FEP validation studies.
Collapse
Affiliation(s)
- Shan Sun
- Tri-Institutional Therapeutics Discovery Institute, New York, NY, United States
| | - David J. Huggins
- Tri-Institutional Therapeutics Discovery Institute, New York, NY, United States
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY, United States
| |
Collapse
|
54
|
Hahn DF, Bayly CI, Boby ML, Macdonald HEB, Chodera JD, Gapsys V, Mey ASJS, Mobley DL, Benito LP, Schindler CEM, Tresadern G, Warren GL. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1497. [PMID: 36382113 PMCID: PMC9662604 DOI: 10.33011/livecoms.4.1.1497] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.
Collapse
Affiliation(s)
- David F. Hahn
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Melissa L. Boby
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- MSD R&D Innovation Centre, 120 Moorgate, London EC2M 6UR, United Kingdom
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, CA USA
| | - Laura Perez Benito
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Gary Tresadern
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | |
Collapse
|
55
|
Dietschreit JCB, Diestler DJ, Hulm A, Ochsenfeld C, Gómez-Bombarelli R. From free-energy profiles to activation free energies. J Chem Phys 2022; 157:084113. [DOI: 10.1063/5.0102075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) discriminating between R and P, we define the free-energy profile (FEP) as the logarithm of the marginal Boltzmann distribution of the CV. This FEP is not a true free energy. Nevertheless, it is common to treat the FEP as the “free-energy” analog of the minimum potential energy path and to take the activation free energy, [Formula: see text], as the difference between the maximum at the transition state and the minimum at R. We show that this approximation can result in large errors. The FEP depends on the CV and is, therefore, not unique. For the same reaction, different discriminating CVs can yield different [Formula: see text]. We derive an exact expression for the activation free energy that avoids this ambiguity. We find [Formula: see text] to be a combination of the probability of the system being in the reactant state, the probability density on the dividing surface, and the thermal de Broglie wavelength associated with the transition. We apply our formalism to simple analytic models and realistic chemical systems and show that the FEP-based approximation applies only at low temperatures for CVs with a small effective mass. Most chemical reactions occur on complex, high-dimensional PES that cannot be treated analytically and pose the added challenge of choosing a good CV. We study the influence of that choice and find that, while the reaction free energy is largely unaffected, [Formula: see text] is quite sensitive.
Collapse
Affiliation(s)
- Johannes C. B. Dietschreit
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | | | - Andreas Hulm
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
| | - Christian Ochsenfeld
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
- Max Planck Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| |
Collapse
|
56
|
González D, Macaya L, Castillo-Orellana C, Verstraelen T, Vogt-Geisse S, Vöhringer-Martinez E. Nonbonded Force Field Parameters from Minimal Basis Iterative Stockholder Partitioning of the Molecular Electron Density Improve CB7 Host-Guest Affinity Predictions. J Chem Inf Model 2022; 62:4162-4174. [PMID: 35959540 DOI: 10.1021/acs.jcim.2c00316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Binding affinity prediction by means of computer simulation has been increasingly incorporated in drug discovery projects. Its wide application, however, is limited by the prediction accuracy of the free energy calculations. The main error sources are force fields used to describe molecular interactions and incomplete sampling of the configurational space. Organic host-guest systems have been used to address force field quality because they share similar interactions found in ligands and receptors, and their rigidity facilitates configurational sampling. Here, we test the binding free energy prediction accuracy for 14 guests with an aromatic or adamantane core and the CB7 host using molecular electron density derived nonbonded force field parameters. We developed a computational workflow written in Python to derive atomic charges and Lennard-Jones parameters with the Minimal Basis Iterative Stockholder method using the polarized electron density of several configurations of each guest in the bound and unbound states. The resulting nonbonded force field parameters improve binding affinity prediction, especially for guests with an adamantane core in which repulsive exchange and dispersion interactions to the host dominate.
Collapse
Affiliation(s)
- Duván González
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Luis Macaya
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Carlos Castillo-Orellana
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnnarde 46, B-9052 Ghent, Belgium
| | - Stefan Vogt-Geisse
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Esteban Vöhringer-Martinez
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| |
Collapse
|
57
|
Zhu S. Computational characterization of homologous ligands binding to a deep hydrophobic pocket in
Shigella flexneri
pilot protein MxiM. Proteins 2022; 90:2116-2123. [DOI: 10.1002/prot.26402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/10/2022] [Accepted: 07/19/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Shun Zhu
- Department of Cellular and Genetic Medicine, School of Basic Medical Sciences Fudan University Shanghai People's Republic of China
| |
Collapse
|
58
|
Peluso P, Chankvetadze B. Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers. Chem Rev 2022; 122:13235-13400. [PMID: 35917234 DOI: 10.1021/acs.chemrev.1c00846] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.
Collapse
Affiliation(s)
- Paola Peluso
- Istituto di Chimica Biomolecolare ICB, CNR, Sede secondaria di Sassari, Traversa La Crucca 3, Regione Baldinca, Li Punti, I-07100 Sassari, Italy
| | - Bezhan Chankvetadze
- Institute of Physical and Analytical Chemistry, School of Exact and Natural Sciences, Tbilisi State University, Chavchavadze Avenue 3, 0179 Tbilisi, Georgia
| |
Collapse
|
59
|
Joshi DC, Gosse C, Huang SY, Lin JH. A Curvilinear-Path Umbrella Sampling Approach to Characterizing the Interactions Between Rapamycin and Three FKBP12 Variants. Front Mol Biosci 2022; 9:879000. [PMID: 35874613 PMCID: PMC9304761 DOI: 10.3389/fmolb.2022.879000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Rapamycin is an immunosuppressant macrolide that exhibits anti-proliferative properties through inhibiting the mTOR kinase. In fact, the drug first associates with the FKBP12 enzyme before interacting with the FRB domain of its target. Despite the availability of structural and thermodynamic information on the interaction of FKBP12 with rapamycin, the energetic and mechanistic understanding of this process is still incomplete. We recently reported a multiple-walker umbrella sampling simulation approach to characterizing the protein–protein interaction energetics along curvilinear paths. In the present paper, we extend our investigations to a protein-small molecule duo, the FKBP12•rapamycin complex. We estimate the binding free energies of rapamycin with wild-type FKBP12 and two mutants in which a hydrogen bond has been removed, D37V and Y82F. Furthermore, the underlying mechanistic details are analyzed. The calculated standard free energies of binding agree well with the experimental data, and the roles of the hydrogen bonds are shown to be quite different for each of these two mutated residues. On one hand, removing the carboxylate group of D37 strongly destabilizes the association; on the other hand, the hydroxyl group of Y82 is nearly unnecessary for the stability of the complex because some nonconventional, cryptic, indirect interaction mechanisms seem to be at work.
Collapse
Affiliation(s)
| | - Charlie Gosse
- Institut de Biologie de l’Ecole Normale Supérieure, ENS, CNRS, INSERM, PSL Research University, Paris, France
| | - Shu-Yu Huang
- Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan
| | - Jung-Hsin Lin
- Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Biomedical Translation Research Center, National Biotechnology Research Park, Academia Sinica, Taipei, Taiwan
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- College of Engineering Sciences, Chang Gung University, Taoyuan, Taiwan
- *Correspondence: Jung-Hsin Lin,
| |
Collapse
|
60
|
Cain S, Risheh A, Forouzesh N. A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database. Biomolecules 2022; 12:919. [PMID: 35883475 PMCID: PMC9312865 DOI: 10.3390/biom12070919] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/26/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, on the other hand, are often accurate yet not fully interpretable and also likely to be overfitted. In this research, we explore the application of Theory-Guided Data Science in studying protein-ligand binding. A hybrid model is introduced by integrating Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). The proposed physics-data model is tested on a dataset of 368 complexes from the PDBbind refined set and 72 host-guest systems. Results demonstrate that the proposed Physics-Guided Neural Network can successfully improve the "accuracy" of the pure data-driven model. In addition, the "interpretability" and "transferability" of our model have boosted compared to the purely data-driven model. Further analyses include evaluating model robustness and understanding relationships between the physical features.
Collapse
Affiliation(s)
- Sahar Cain
- Department of Computer Science, California State University, Los Angeles, CA 90032, USA;
| | - Ali Risheh
- Department of Computer Engineering, Amirkabir University of Technology, Tehran 15914, Iran;
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, CA 90032, USA;
| |
Collapse
|
61
|
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.
Collapse
Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica “Ugo
Schiff”, Università degli
Studi di Firenze, Via
della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| |
Collapse
|
62
|
Oshima H, Sugita Y. Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions. J Chem Inf Model 2022; 62:2846-2856. [PMID: 35639709 DOI: 10.1021/acs.jcim.1c01532] [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
The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.
Collapse
Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| |
Collapse
|
63
|
Reif MM, Zacharias M. Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications. J Chem Theory Comput 2022; 18:3873-3893. [PMID: 35653503 DOI: 10.1021/acs.jctc.1c01194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.
Collapse
Affiliation(s)
- Maria M Reif
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics (T38), Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| | - Martin Zacharias
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics (T38), Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| |
Collapse
|
64
|
Volkov M, Turk JA, Drizard N, Martin N, Hoffmann B, Gaston-Mathé Y, Rognan D. On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks. J Med Chem 2022; 65:7946-7958. [PMID: 35608179 DOI: 10.1021/acs.jmedchem.2c00487] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free and bound states, we unambiguously evidence that an explicit description of protein-ligand noncovalent interactions does not provide any advantage with respect to ligand or protein descriptors. Simple models, inferring binding affinities of test samples from that of the closest ligands or proteins in the training set, already exhibit good performances, suggesting that memorization largely dominates true learning in the deep neural networks. The current study suggests considering only noncovalent interactions while omitting their protein and ligand atomic environments. Removing all hidden biases probably requires much denser protein-ligand training matrices and a coordinated effort of the drug design community to solve the necessary protein-ligand structures.
Collapse
Affiliation(s)
- Mikhail Volkov
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, Illkirch 67400, France
| | | | | | | | | | | | - Didier Rognan
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, Illkirch 67400, France
| |
Collapse
|
65
|
Zara L, Moraca F, Van Muijlwijk-Koezen JE, Zarzycka B, Abel R, de Esch IJP. Exploring the Activity Profile of TbrPDEB1 and hPDE4 Inhibitors Using Free Energy Perturbation. ACS Med Chem Lett 2022; 13:904-910. [PMID: 35707144 PMCID: PMC9190044 DOI: 10.1021/acsmedchemlett.1c00690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Lorena Zara
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Francesca Moraca
- Schrodinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Jacqueline E. Van Muijlwijk-Koezen
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Barbara Zarzycka
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Robert Abel
- Schrodinger, Inc., 1540 Broadway, New York, New York 10036, United States
| | - Iwan J. P. de Esch
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| |
Collapse
|
66
|
Wieder M, Fleck M, Braunsfeld B, Boresch S. Alchemical free energy simulations without speed limits. A generic framework to calculate free energy differences independent of the underlying molecular dynamics program. J Comput Chem 2022; 43:1151-1160. [PMID: 35485139 PMCID: PMC9323469 DOI: 10.1002/jcc.26877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/17/2022] [Accepted: 04/11/2022] [Indexed: 01/03/2023]
Abstract
We describe the theory of the so‐called common‐core/serial‐atom‐insertion (CC/SAI) approach to compute alchemical free energy differences and its practical implementation in a Python package called Transformato. CC/SAI is not tied to a specific biomolecular simulation program and does not rely on special purpose code for alchemical transformations. To calculate the alchemical free energy difference between several small molecules, the physical end‐states are mutated into a suitable common core. Since this only requires turning off interactions, the setup of intermediate states is straightforward to automate. Transformato currently supports CHARMM and OpenMM as back ends to carry out the necessary molecular dynamics simulations, as well as post‐processing calculations. We validate the method by computing a series of relative solvation free energy differences.
Collapse
Affiliation(s)
- Marcus Wieder
- Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Markus Fleck
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Benedict Braunsfeld
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Stefan Boresch
- Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| |
Collapse
|
67
|
Suating P, Ernst NE, Alagbe BD, Skinner HA, Mague JT, Ashbaugh HS, Gibb BC. On the Nature of Guest Complexation in Water: Triggered Wetting-Water-Mediated Binding. J Phys Chem B 2022; 126:3150-3160. [PMID: 35438501 PMCID: PMC9059121 DOI: 10.1021/acs.jpcb.2c00628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/18/2022] [Indexed: 11/30/2022]
Abstract
The complexity of macromolecular surfaces means that there are still many open questions regarding how specific areas are solvated and how this might affect the complexation of guests. Contributing to the identification and classification of the different possible mechanisms of complexation events in aqueous solution, and as part of the recent SAMPL8 exercise, we report here on the synthesis and conformational properties of TEEtOA 2, a cavitand with conformationally flexible ethyl groups at its portal. Using a combination of ITC and NMR spectroscopy, we report the binding affinities of a series of carboxylates to 2 and compare it to a related cavitand TEMOA 1. Additionally, we report MD simulations revealing how the wetting of the pocket of 2 is controlled by the conformation of its rim ethyl groups and, correspondingly, a novel triggered wetting, guest complexation mechanism, whereby the approaching guest opens up the pocket of the host, inducing its wetting and ultimately allows the formation of a hydrated host-guest complex (H·G·H2O). A general classification of complexation mechanisms is also suggested.
Collapse
Affiliation(s)
- Paolo Suating
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Nicholas E. Ernst
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Busayo D. Alagbe
- Department
of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Hannah A. Skinner
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Joel T. Mague
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| | - Henry S. Ashbaugh
- Department
of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Bruce C. Gibb
- Department
of Chemistry, Tulane University, New Orleans, Louisiana 70118, United States
| |
Collapse
|
68
|
Chan KW, Yu KY, Yiu WH, Xue R, Lok SWY, Li H, Zou Y, Ma J, Lai KN, Tang SCW. Potential Therapeutic Targets of Rehmannia Formulations on Diabetic Nephropathy: A Comparative Network Pharmacology Analysis. Front Pharmacol 2022; 13:794139. [PMID: 35387335 PMCID: PMC8977554 DOI: 10.3389/fphar.2022.794139] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/10/2022] [Indexed: 11/14/2022] Open
Abstract
Background: Previous retrospective cohorts showed that Rehmannia-6 (R-6, Liu-wei-di-huang-wan) formulations were associated with significant kidney function preservation and mortality reduction among chronic kidney disease patients with diabetes. This study aimed to investigate the potential mechanism of action of common R-6 variations in a clinical protocol for diabetic nephropathy (DN) from a system pharmacology approach. Study Design and Methods: Disease-related genes were retrieved from GeneCards and OMIM by searching “Diabetic Nephropathy” and “Macroalbuminuria”. Variations of R-6 were identified from a published existing clinical practice guideline developed from expert consensus and pilot clinical service program. The chemical compound IDs of each herb were retrieved from TCM-Mesh and PubChem. Drug targets were subsequently revealed via PharmaMapper and UniProtKB. The disease gene interactions were assessed through STRING, and disease–drug protein–protein interaction network was integrated and visualized by Cytoscape. Clusters of disease–drug protein–protein interaction were constructed by Molecular Complex Detection (MCODE) extension. Functional annotation of clusters was analyzed by DAVID and KEGG pathway enrichment. Differences among variations of R-6 were compared. Binding was verified by molecular docking with AutoDock. Results: Three hundred fifty-eight genes related to DN were identified, forming 11 clusters which corresponded to complement and coagulation cascades and signaling pathways of adipocytokine, TNF, HIF-1, and AMPK. Five variations of R-6 were analyzed. Common putative targets of the R-6 variations on DN included ACE, APOE, CCL2, CRP, EDN1, FN1, HGF, ICAM1, IL10, IL1B, IL6, INS, LEP, MMP9, PTGS2, SERPINE1, and TNF, which are related to regulation of nitric oxide biosynthesis, lipid storage, cellular response to lipopolysaccharide, inflammatory response, NF-kappa B transcription factor activity, smooth muscle cell proliferation, blood pressure, cellular response to interleukin-1, angiogenesis, cell proliferation, peptidyl-tyrosine phosphorylation, and protein kinase B signaling. TNF was identified as the seed for the most significant cluster of all R-6 variations. Targets specific to each formulation were identified. The key chemical compounds of R-6 have good binding ability to the putative protein targets. Conclusion: The mechanism of action of R-6 on DN is mostly related to the TNF signaling pathway as a core mechanism, involving amelioration of angiogenesis, fibrosis, inflammation, disease susceptibility, and oxidative stress. The putative targets identified could be validated through clinical trials.
Collapse
Affiliation(s)
- Kam Wa Chan
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kam Yan Yu
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wai Han Yiu
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Rui Xue
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Sarah Wing-Yan Lok
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Hongyu Li
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yixin Zou
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jinyuan Ma
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kar Neng Lai
- Department of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | | |
Collapse
|
69
|
McNutt AT, Koes DR. Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network. J Chem Inf Model 2022; 62:1819-1829. [PMID: 35380443 PMCID: PMC9038699 DOI: 10.1021/acs.jcim.1c01497] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.
Collapse
Affiliation(s)
- Andrew T McNutt
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - David Ryan Koes
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| |
Collapse
|
70
|
Wu Z, Biggin PC. Correction Schemes for Absolute Binding Free Energies Involving Lipid Bilayers. J Chem Theory Comput 2022; 18:2657-2672. [PMID: 35315270 PMCID: PMC9082507 DOI: 10.1021/acs.jctc.1c01251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Absolute
binding free-energy (ABFE) calculations are playing an
increasing role in drug design, especially as they can be performed
on a range of disparate compounds and direct comparisons between them
can be made. It is, however, especially important to ensure that they
are as accurate as possible, as unlike relative binding free-energy
(RBFE) calculations, one does not benefit as much from a cancellation
of errors during the calculations. In most modern implementations
of ABFE calculations, a particle mesh Ewald scheme is typically used
to treat the electrostatic contribution to the free energy. A central
requirement of such schemes is that the box preserves neutrality throughout
the calculation. There are many ways to deal with this problem that
have been discussed over the years ranging from a neutralizing plasma
with a post hoc correction term through to a simple co-alchemical
ion within the same box. The post hoc correction approach is the most
widespread. However, the vast majority of these studies have been
applied to a soluble protein in a homogeneous solvent (water or salt
solution). In this work, we explore which of the more common approaches
would be the most suitable for a simulation box with a lipid bilayer
within it. We further develop the idea of the so-called Rocklin correction
for lipid-bilayer systems and show how such a correction could work.
However, we also show that it will be difficult to make this generalizable
in a practical way and thus we conclude that the use of a “co-alchemical
ion” is the most useful approach for simulations involving
lipid membrane systems.
Collapse
Affiliation(s)
- Zhiyi Wu
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
| | - Philip C Biggin
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
| |
Collapse
|
71
|
Huggins DJ. Comparing the Performance of Different AMBER Protein Forcefields, Partial Charge Assignments, and Water Models for Absolute Binding Free Energy Calculations. J Chem Theory Comput 2022; 18:2616-2630. [PMID: 35266690 DOI: 10.1021/acs.jctc.1c01208] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower the cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper, we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular, AMBER ff15ipq.
Collapse
Affiliation(s)
- David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States.,Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
| |
Collapse
|
72
|
Wickstrom L, Gallicchio E, Chen L, Kurtzman T, Deng N. Developing end-point methods for absolute binding free energy calculation using the Boltzmann-quasiharmonic model. Phys Chem Chem Phys 2022; 24:6037-6052. [PMID: 35212338 PMCID: PMC9044818 DOI: 10.1039/d1cp05075c] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Understanding the physical forces underlying receptor-ligand binding requires robust methods for analyzing the binding thermodynamics. In end-point binding free energy methods the binding free energy is naturally decomposable into physically intuitive contributions such as the solvation free energy and configurational entropy that can provide insights. Here we present a new end-point method called EE-BQH (Effective Energy-Boltzmann-Quasiharmonic) which combines the Boltzmann-Quasiharmonic model for configurational entropy with different solvation free energy methods, such as the continuum solvent PBSA model and the integral equation-based 3D-RISM, to estimate the absolute binding free energy. We compare EE-BQH with other treatments of configurational entropy such as Quasiharmonic models in internal coordinates (QHIC) and in Cartesian coordinates (QHCC), and Normal Mode analysis (NMA), by testing them on the octa acids host-guest complexes from the SAMPL8 blind challenge. The accuracies in the calculated absolute binding free energies strongly depend on the configurational entropy and solvation free energy methods used. QHIC and BQH yield the best agreements with the established potential of mean force (PMF) estimates, with R2 of ∼0.7 and mean unsigned error of ∼1.7 kcal mol-1. These results from the end-point calculations are also in similar agreement with experiments. While 3D-RISM in combination with QHIC or BQH lead to reasonable correlations with the PMF results and experiments, the calculated absolute binding free energies are underestimated by ∼5 kcal mol-1. While the binding is accompanied by a significant reduction in the ligand translational/rotational entropy, the change in the torsional entropy in these host-guest systems is slightly positive. Compared with BQH, QHIC underestimates the reduction of configurational entropy because of the non-Gaussian probability distributions in the ligand rotation and a small number of torsions. The study highlights the crucial role of configurational entropy in determining binding and demonstrates the potential of using the new end-point method to provide insights in more complex protein-ligand systems.
Collapse
Affiliation(s)
- Lauren Wickstrom
- Borough of Manhattan Community College, The City University of New York, Department of Science, New York, New York, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, New York, USA.,PhD Program in Chemistry, Graduate Center of the City University of New York, New York, USA.,PhD Program in Biochemistry, Graduate Center of the City University of New York, New York, USA
| | - Lieyang Chen
- PhD Program in Chemistry, Graduate Center of the City University of New York, New York, USA.,PhD Program in Biochemistry, Graduate Center of the City University of New York, New York, USA.,Department of Chemistry, Lehman College, The City University of New York, Bronx, New York, USA
| | - Tom Kurtzman
- PhD Program in Chemistry, Graduate Center of the City University of New York, New York, USA.,PhD Program in Biochemistry, Graduate Center of the City University of New York, New York, USA.,Department of Chemistry, Lehman College, The City University of New York, Bronx, New York, USA
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York, USA.
| |
Collapse
|
73
|
Ge Y, Wych DC, Samways ML, Wall ME, Essex JW, Mobley DL. Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques. J Chem Theory Comput 2022; 18:1359-1381. [PMID: 35148093 PMCID: PMC9241631 DOI: 10.1021/acs.jctc.1c00590] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.
Collapse
Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - David C Wych
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Marley L Samways
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Michael E Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
| |
Collapse
|
74
|
Rizzuti B. Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2022; 1870:140757. [PMID: 35051666 DOI: 10.1016/j.bbapap.2022.140757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 12/22/2022]
Abstract
Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.
Collapse
Affiliation(s)
- Bruno Rizzuti
- CNR-NANOTEC, SS Rende (CS), Department of Physics, University of Calabria, 87036 Rende, Italy; Institute for Biocomputation and Physics of Complex Systems (BIFI), Joint Unit GBsC-CSIC-BIFI, University of Zaragoza, 50018 Zaragoza, Spain.
| |
Collapse
|
75
|
Hata H, Phuoc Tran D, Marzouk Sobeh M, Kitao A. Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model. Biophys Physicobiol 2022; 18:305-316. [PMID: 35178333 PMCID: PMC8694779 DOI: 10.2142/biophysico.bppb-v18.037] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 12/02/2021] [Indexed: 01/01/2023] Open
Abstract
We recently proposed a computational procedure to simulate the dissociation of protein/ligand complexes using the dissociation Parallel Cascade Selection Molecular Dynamics simulation (dPaCS-MD) method and to analyze the generated trajectories using the Markov state model (MSM). This procedure, called dPaCS-MD/MSM, enables calculation of the dissociation free energy profile and the standard binding free energy. To examine whether this method can reproduce experimentally determined binding free energies for a variety of systems, we used it to investigate the dissociation of three protein/ligand complexes: trypsin/benzamine, FKBP/FK506, and adenosine A2A receptor/T4E. First, dPaCS-MD generated multiple dissociation pathways within a reasonable computational time for all the complexes, although the complexes differed significantly in the size of the molecules and in intermolecular interactions. Subsequent MSM analyses produced free energy profiles for the dissociations, which provided insights into how each ligand dissociates from the protein. The standard binding free energies obtained by dPaCS-MD/MSM are in good agreement with experimental values for all the complexes. We conclude that dPaCS-MD/MSM can accurately calculate the binding free energies of these complexes.
Collapse
Affiliation(s)
- Hiroaki Hata
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| | - Duy Phuoc Tran
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| | - Mohamed Marzouk Sobeh
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.,Physics Department, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
| | - Akio Kitao
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| |
Collapse
|
76
|
Subbotina J, Lobaskin V. Multiscale Modeling of Bio-Nano Interactions of Zero-Valent Silver Nanoparticles. J Phys Chem B 2022; 126:1301-1314. [PMID: 35132861 PMCID: PMC8859825 DOI: 10.1021/acs.jpcb.1c09525] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
![]()
Understanding the
specifics of interaction between the protein
and nanomaterial is crucial for designing efficient, safe, and selective
nanoplatforms, such as biosensor or nanocarrier systems. Routing experimental
screening for the most suitable complementary pair of biomolecule
and nanomaterial used in such nanoplatforms might be a resource-intensive
task. While a range of computational tools are available for prescreening
libraries of proteins for their interactions with small molecular
ligands, choices for high-throughput screening of protein libraries
for binding affinities to new and existing nanomaterials are very
limited. In the current work, we present the results of the systematic
computational study of interaction of various biomolecules with pristine
zero-valent noble metal nanoparticles, namely, AgNPs, by using the UnitedAtom multiscale approach. A set of blood plasma and
dietary proteins for which the interaction with AgNPs was described
experimentally were examined computationally to evaluate the performance
of the UnitedAtom method. A set of interfacial descriptors
(log PNM, adsorption affinities, and adsorption
affinity ranking), which can characterize the relative hydrophobicity/hydrophilicity/lipophilicity
of the nanosized silver and its ability to form bio(eco)corona, was
evaluated for future use in nano-QSAR/QSPR studies.
Collapse
Affiliation(s)
- Julia Subbotina
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Vladimir Lobaskin
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland
| |
Collapse
|
77
|
Reif MM, Zacharias M. Computational Tools for Accurate Binding Free-Energy Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2385:255-292. [PMID: 34888724 DOI: 10.1007/978-1-0716-1767-0_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.
Collapse
Affiliation(s)
- Maria M Reif
- Physics Department (T38), Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physics Department (T38), Technische Universität München, Garching, Germany.
| |
Collapse
|
78
|
Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2385:313-323. [PMID: 34888726 DOI: 10.1007/978-1-0716-1767-0_14] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Many proteins have a solvent-exposed binding cleft, which permits their inhibitors to bind and unbind without significant protein conformation transforms. The binding/unbinding pathways of these protein-inhibitor complexes can be rather straightforwardly sampled by using umbrella sampling (US) simulation methods. During a US simulation, the Cα atoms of the protein are restrained via a harmonic force. The potential of mean force (PMF) along the binding pathway can be estimated by using the weighted histogram analysis method (WHAM). The binding affinity is then computed as the difference in PMF between the binding and unbinding states.
Collapse
|
79
|
Azimi S, Wu JZ, Khuttan S, Kurtzman T, Deng N, Gallicchio E. Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge. J Comput Aided Mol Des 2022; 36:63-76. [PMID: 35059940 PMCID: PMC8982563 DOI: 10.1007/s10822-021-00437-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/01/2021] [Indexed: 01/23/2023]
Abstract
We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.
Collapse
Affiliation(s)
- Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Joe Z. Wu
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Tom Kurtzman
- Department of Chemistry, Lehman College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| |
Collapse
|
80
|
Gundelach L, Fox T, Tautermann CS, Skylaris CK. BRD4: quantum mechanical protein–ligand binding free energies using the full-protein DFT-based QM-PBSA method. Phys Chem Chem Phys 2022; 24:25240-25249. [PMID: 36222107 DOI: 10.1039/d2cp03705j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Fully quantum mechanical approaches to calculating protein–ligand free energies of binding have the potential to reduce empiricism and explicitly account for all physical interactions responsible for protein–ligand binding.
Collapse
Affiliation(s)
- Lennart Gundelach
- University of Southampton, Faculty of Engineering Science and Mathematics, Chemistry, University Road, Southampton, SO17 1BJ, UK
| | - Thomas Fox
- Boehringer Ingelheim Pharma GmbH & Co KG, Medicinal Chemistry, Birkendorfer Str 65, 88397, Biberach, Germany
| | - Christofer S. Tautermann
- Boehringer Ingelheim Pharma GmbH & Co KG, Medicinal Chemistry, Birkendorfer Str 65, 88397, Biberach, Germany
| | - Chris-Kriton Skylaris
- University of Southampton, Faculty of Engineering Science and Mathematics, Chemistry, University Road, Southampton, SO17 1BJ, UK
| |
Collapse
|
81
|
González-Fernández C, Bringas E, Oostenbrink C, Ortiz I. In silico investigation and surmounting of Lipopolysaccharide barrier in Gram-Negative Bacteria: How far has molecular dynamics Come? Comput Struct Biotechnol J 2022; 20:5886-5901. [PMID: 36382192 PMCID: PMC9636410 DOI: 10.1016/j.csbj.2022.10.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/29/2022] Open
Abstract
Lipopolysaccharide (LPS), a main component of the outer membrane of Gram-negative bacteria, has crucial implications on both antibiotic resistance and the overstimulation of the host innate immune system. Fighting against these global concerns calls for the molecular understanding of the barrier function and immunostimulatory ability of LPS. Molecular dynamics (MD) simulations have become an invaluable tool for uncovering important findings in LPS research. While the reach of MD simulations for investigating the immunostimulatory ability of LPS has been already outlined, little attention has been paid to the role of MD simulations for exploring its barrier function and synthesis. Herein, we give an overview about the impact of MD simulations on gaining insight into the shield role and synthesis pathway of LPS, which have attracted considerable attention to discover molecules able to surmount antibiotic resistance, either circumventing LPS defenses or disrupting its synthesis. We specifically focus on the enhanced sampling and free energy calculation methods that have been combined with MD simulations to address such research. We also highlight the use of special-purpose MD supercomputers, the importance of appropriate LPS and ions parameterization to obtain reliable results, and the complementary views that MD and wet-lab experiments provide. Thereby, this work, which covers the last five years of research, apart from outlining the phenomena and strategies that are being explored, evidences the valuable insights that are gained by MD, which may be useful to advance antibiotic design, and what the prospects of this in silico method could be in LPS research.
Collapse
Affiliation(s)
- Cristina González-Fernández
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Eugenio Bringas
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, BOKU – University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Inmaculada Ortiz
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
- Corresponding author.
| |
Collapse
|
82
|
Tam NM, Nguyen TH, Ngan VT, Tung NT, Ngo ST. Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211480. [PMID: 35116157 PMCID: PMC8790385 DOI: 10.1098/rsos.211480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 12/20/2021] [Indexed: 05/03/2023]
Abstract
The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.
Collapse
Affiliation(s)
- Nguyen Minh Tam
- Computational Chemistry Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Trung Hai Nguyen
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Laboratory of Theoretical and Computational Biophysics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Vu Thi Ngan
- Laboratory of Computational Chemistry and Modelling, Department of Chemistry, Quy Nhon University, Quy Nhon, Vietnam
| | - Nguyen Thanh Tung
- Institute of Materials Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Son Tung Ngo
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Laboratory of Theoretical and Computational Biophysics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| |
Collapse
|
83
|
Zara L, Efrém NL, van Muijlwijk-Koezen JE, de Esch IJP, Zarzycka B. Progress in Free Energy Perturbation: Options for Evolving Fragments. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 40:36-42. [PMID: 34916020 DOI: 10.1016/j.ddtec.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 01/18/2023]
Abstract
One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.
Collapse
Affiliation(s)
- Lorena Zara
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Nina-Louisa Efrém
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Jacqueline E van Muijlwijk-Koezen
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Barbara Zarzycka
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands..
| |
Collapse
|
84
|
Ray D, Stone SE, Andricioaei I. Markovian Weighted Ensemble Milestoning (M-WEM): Long-Time Kinetics from Short Trajectories. J Chem Theory Comput 2021; 18:79-95. [PMID: 34910499 DOI: 10.1021/acs.jctc.1c00803] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-time-scale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond time-scale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy but it also allows for a scheme to reconstruct a multidimensional free-energy landscape along additional degrees of freedom, which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to 4 orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential applications in kinetics and free-energy-based computational drug design.
Collapse
Affiliation(s)
- Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
| | - Sharon Emily Stone
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States.,Department of Physics and Astronomy, University of California Irvine, Irvine, California 92697, United States
| |
Collapse
|
85
|
Zhao Q, Capelli R, Carloni P, Lüscher B, Li J, Rossetti G. Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors. J Chem Theory Comput 2021; 17:7899-7911. [PMID: 34813698 DOI: 10.1021/acs.jctc.1c00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.
Collapse
Affiliation(s)
- Qianqian Zhao
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Riccardo Capelli
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Applied Science and Technology, Politecnico di Torino, Torino 10129, Italy
| | - Paolo Carloni
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Institute for Neuroscience and Medicine (INM)-11, Forschungszentrum Jülich, 52428 Jülich, Germany.,Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52062 Aachen, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
| | - Bernhard Lüscher
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52062 Aachen, Germany
| | - Jinyu Li
- College of Chemistry, Fuzhou University, Fuzhou 350002, China
| | - Giulia Rossetti
- Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Jülich Supercomputing Center (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Neurology, RWTH Aachen University, 52062 Aachen, Germany
| |
Collapse
|
86
|
Ben-Shalom IY, Lin C, Radak BK, Sherman W, Gilson MK. Fast Equilibration of Water between Buried Sites and the Bulk by Molecular Dynamics with Parallel Monte Carlo Water Moves on Graphical Processing Units. J Chem Theory Comput 2021; 17:7366-7372. [PMID: 34762421 PMCID: PMC8716912 DOI: 10.1021/acs.jctc.1c00867] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Molecular dynamics (MD) simulations of proteins are commonly used to sample from the Boltzmann distribution of conformational states, with wide-ranging applications spanning chemistry, biophysics, and drug discovery. However, MD can be inefficient at equilibrating water occupancy for buried cavities in proteins that are inaccessible to the surrounding solvent. Indeed, the time needed for water molecules to equilibrate between the bulk solvent and the binding site can be well beyond what is practical with standard MD, which typically ranges from hundreds of nanoseconds to a few microseconds. We recently introduced a hybrid Monte Carlo/MD (MC/MD) method, which speeds up the equilibration of water between buried cavities and the surrounding solvent, while sampling from the thermodynamically correct distribution of states. While the initial implementation of the MC functionality led to considerable slowing of the overall simulations, here we address this problem with a parallel MC algorithm implemented on graphical processing units. This results in speed-ups of 10-fold to 1000-fold over the original MC/MD algorithm, depending on the system and simulation parameters. The present method is available for use in the AMBER simulation software.
Collapse
Affiliation(s)
- Ido Y. Ben-Shalom
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 92093 La Jolla, California, USA
| | - Charles Lin
- Roivant Discovery, Boston, Massachusetts, 02110, USA
| | | | - Woody Sherman
- Roivant Discovery, Boston, Massachusetts, 02110, USA
| | - Michael K. Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 92093 La Jolla, California, USA,To whom correspondence should be addressed,
| |
Collapse
|
87
|
Phenolic-protein interactions in foods and post ingestion: Switches empowering health outcomes. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
88
|
Castro LHE, Sant'Anna CMR. Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications. Curr Top Med Chem 2021; 22:333-346. [PMID: 34844540 DOI: 10.2174/1568026621666211129140958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/23/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
Collapse
Affiliation(s)
| | - Carlos Mauricio R Sant'Anna
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica. Brazil
| |
Collapse
|
89
|
Zuckerman DM, Russo JD. A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications. AMERICAN JOURNAL OF PHYSICS 2021; 89:1048-1061. [PMID: 35530173 PMCID: PMC9075726 DOI: 10.1119/10.0005603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/25/2021] [Indexed: 06/14/2023]
Abstract
Despite the importance of non-equilibrium statistical mechanics in modern physics and related fields, the topic is often omitted from undergraduate and core-graduate curricula. Key aspects of non-equilibrium physics, however, can be understood with a minimum of formalism based on a rigorous trajectory picture. The fundamental object is the ensemble of trajectories, a set of independent time-evolving systems, which easily can be visualized or simulated (e.g., for protein folding) and which can be analyzed rigorously in analogy to an ensemble of static system configurations. The trajectory picture provides a straightforward basis for understanding first-passage times, "mechanisms" in complex systems, and fundamental constraints on the apparent reversibility of complex processes. Trajectories make concrete the physics underlying the diffusion and Fokker-Planck partial differential equations. Last but not least, trajectory ensembles underpin some of the most important algorithms that have provided significant advances in biomolecular studies of protein conformational and binding processes.
Collapse
|
90
|
González-Fernández C, Basauri A, Fallanza M, Bringas E, Oostenbrink C, Ortiz I. Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review. J Chem Inf Model 2021; 61:4839-4851. [PMID: 34559524 PMCID: PMC8549069 DOI: 10.1021/acs.jcim.1c00613] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
![]()
Lipopolysaccharide
(LPS) is the primary component of the outer
leaflet of Gram-negative bacterial outer membranes. LPS elicits an
overwhelming immune response during infection, which can lead to life-threatening
sepsis or septic shock for which no suitable treatment is available
so far. As a result of the worldwide expanding multidrug-resistant
bacteria, the occurrence and frequency of sepsis are expected to increase;
thus, there is an urge to develop novel strategies for treating bacterial
infections. In this regard, gaining an in-depth understanding about
the ability of LPS to both stimulate the host immune system and interact
with several molecules is crucial for fighting against LPS-caused
infections and allowing for the rational design of novel antisepsis
drugs, vaccines and LPS sequestration and detection methods. Molecular
dynamics (MD) simulations, which are understood as being a computational
microscope, have proven to be of significant value to understand LPS-related
phenomena, driving and optimizing experimental research studies. In
this work, a comprehensive review on the methods that can be combined
with MD simulations, recently applied in LPS research, is provided.
We focus especially on both enhanced sampling methods, which enable
the exploration of more complex systems and access to larger time
scales, and free energy calculation approaches. Thereby, apart from
outlining several strategies for surmounting LPS-caused infections,
this work reports the current state-of-the-art of the methods applied
with MD simulations for moving a step forward in the development of
such strategies.
Collapse
Affiliation(s)
- Cristina González-Fernández
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Arantza Basauri
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Marcos Fallanza
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Eugenio Bringas
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, BOKU - University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Inmaculada Ortiz
- Department of Chemical and Biomolecular Engineering, ETSIIT, University of Cantabria, Avda. Los Castros s/n, 39005 Santander, Spain
| |
Collapse
|
91
|
González D, Macaya L, Vöhringer-Martinez E. Molecular Environment-Specific Atomic Charges Improve Binding Affinity Predictions of SAMPL5 Host-Guest Systems. J Chem Inf Model 2021; 61:4462-4474. [PMID: 34464129 DOI: 10.1021/acs.jcim.1c00655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on quantum mechanics/molecular mechanics calculations and minimal basis iterative stockholder (MBIS) partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software packages provides D-MBIS charges that represent the guest's average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the general Amber force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction originates from the difference in potential energy that is required to polarize the guest in the bound and unbound state and contributes significantly to the binding affinity of anionic guests.
Collapse
Affiliation(s)
- Duván González
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Luis Macaya
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Esteban Vöhringer-Martinez
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| |
Collapse
|
92
|
Bradford SYC, El Khoury L, Ge Y, Osato M, Mobley DL, Fischer M. Temperature artifacts in protein structures bias ligand-binding predictions. Chem Sci 2021; 12:11275-11293. [PMID: 34667539 PMCID: PMC8447925 DOI: 10.1039/d1sc02751d] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/09/2021] [Indexed: 12/14/2022] Open
Abstract
X-ray crystallography is the gold standard to resolve conformational ensembles that are significant for protein function, ligand discovery, and computational methods development. However, relevant conformational states may be missed at common cryogenic (cryo) data-collection temperatures but can be populated at room temperature. To assess the impact of temperature on making structural and computational discoveries, we systematically investigated protein conformational changes in response to temperature and ligand binding in a structural and computational workhorse, the T4 lysozyme L99A cavity. Despite decades of work on this protein, shifting to RT reveals new global and local structural changes. These include uncovering an apo helix conformation that is hidden at cryo but relevant for ligand binding, and altered side chain and ligand conformations. To evaluate the impact of temperature-induced protein and ligand changes on the utility of structural information in computation, we evaluated how temperature can mislead computational methods that employ cryo structures for validation. We find that when comparing simulated structures just to experimental cryo structures, hidden successes and failures often go unnoticed. When using structural information in ligand binding predictions, both coarse docking and rigorous binding free energy calculations are influenced by temperature effects. The trend that cryo artifacts limit the utility of structures for computation holds across five distinct protein classes. Our results suggest caution when consulting cryogenic structural data alone, as temperature artifacts can conceal errors and prevent successful computational predictions, which can mislead the development and application of computational methods in discovering bioactive molecules.
Collapse
Affiliation(s)
- Shanshan Y C Bradford
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital Memphis TN 38105 USA
| | - Léa El Khoury
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - Meghan Osato
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California Irvine CA 92697 USA
- Department of Chemistry, University of California Irvine CA 92697 USA
| | - Marcus Fischer
- Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital Memphis TN 38105 USA
- Department of Structural Biology, St. Jude Children's Research Hospital Memphis TN 38105 USA
| |
Collapse
|
93
|
Bertazzo M, Gobbo D, Decherchi S, Cavalli A. Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy. J Chem Theory Comput 2021; 17:5287-5300. [PMID: 34260233 PMCID: PMC8389529 DOI: 10.1021/acs.jctc.1c00177] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simulative possibilities for the systematic and robust computation of thermodynamic observables, including the free energy of a drug binding to a target. In contrast to calculations of relative binding free energy, which are nowadays widely exploited for drug discovery, we here push the boundary of computing the binding free energy and the potential of mean force. We introduce a novel protocol that leverages enhanced sampling, machine learning, and ad hoc algorithms to limit human intervention, computing time, and free parameters in free energy calculations. We first validate the method on a host-guest system, and then we apply the protocol to glycogen synthase kinase 3 beta, a protein kinase of pharmacological interest. Overall, we obtain a good correlation with experimental values in relative and absolute terms. While we focus on protein-ligand binding, the strategy is of broad applicability to any complex event that can be described with a path collective variable. We systematically discuss key details that influence the final result. The parameters and simulation settings are available at PLUMED-NEST to allow full reproducibility.
Collapse
Affiliation(s)
- Martina Bertazzo
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology (FaBiT), Alma
Mater Studiorum − University of Bologna, via Belmeloro 6, 40126 Bologna, Italy
| | - Dorothea Gobbo
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
| | - Sergio Decherchi
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- BiKi
Technologies s.r.l., Via XX Settembre 33/10, 16121 Genoa, Italy
| | - Andrea Cavalli
- Computational
& Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology (FaBiT), Alma
Mater Studiorum − University of Bologna, via Belmeloro 6, 40126 Bologna, Italy
| |
Collapse
|
94
|
Bisht N, Sah AN, Bisht S, Joshi H. Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development. Mini Rev Med Chem 2021; 21:1025-1032. [PMID: 33319657 DOI: 10.2174/1389557520666201214101329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/05/2020] [Accepted: 10/09/2020] [Indexed: 11/22/2022]
Abstract
In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.
Collapse
Affiliation(s)
- Neema Bisht
- Assistant Professor, College of Pharmacy, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
| | - Archana N Sah
- Head and Dean, Department of Pharmaceutical Sciences, Faculty of Technology, Sir J.C. Bose Technical Campus, Bhimtal, Kumaun University Nainital, Uttarakhand 263136, India
| | - Sandeep Bisht
- Assistant Professor, School of Management, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
| | - Himanshu Joshi
- Professor, College of Pharmacy, Graphic Era Hill University, Bhimtal Campus, Sattal Road, Bhimtal, Uttarakhand 263136, India
| |
Collapse
|
95
|
Ali HS, Chakravorty A, Kalayan J, de Visser SP, Henchman RH. Energy-entropy method using multiscale cell correlation to calculate binding free energies in the SAMPL8 host-guest challenge. J Comput Aided Mol Des 2021; 35:911-921. [PMID: 34264476 PMCID: PMC8367938 DOI: 10.1007/s10822-021-00406-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/22/2021] [Indexed: 11/29/2022]
Abstract
Free energy drives a wide range of molecular processes such as solvation, binding, chemical reactions and conformational change. Given the central importance of binding, a wide range of methods exist to calculate it, whether based on scoring functions, machine-learning, classical or electronic structure methods, alchemy, or explicit evaluation of energy and entropy. Here we present a new energy-entropy (EE) method to calculate the host-guest binding free energy directly from molecular dynamics (MD) simulation. Entropy is evaluated using Multiscale Cell Correlation (MCC) which uses force and torque covariance and contacts at two different length scales. The method is tested on a series of seven host-guest complexes in the SAMPL8 (Statistical Assessment of the Modeling of Proteins and Ligands) "Drugs of Abuse" Blind Challenge. The EE-MCC binding free energies are found to agree with experiment with an average error of 0.9 kcal mol-1. MCC makes clear the origin of the entropy changes, showing that the large loss of positional, orientational, and to a lesser extent conformational entropy of each binding guest is compensated for by a gain in orientational entropy of water released to bulk, combined with smaller decreases in vibrational entropy of the host, guest and contacting water.
Collapse
Affiliation(s)
- Hafiz Saqib Ali
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Arghya Chakravorty
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jas Kalayan
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Samuel P de Visser
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Richard H Henchman
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
- Department of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
- Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
| |
Collapse
|
96
|
Ryazantsev MN, Strashkov DM, Nikolaev DM, Shtyrov AA, Panov MS. Photopharmacological compounds based on azobenzenes and azoheteroarenes: principles of molecular design, molecular modelling, and synthesis. RUSSIAN CHEMICAL REVIEWS 2021. [DOI: 10.1070/rcr5001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
97
|
Duboué-Dijon E, Hénin J. Building intuition for binding free energy calculations: Bound state definition, restraints, and symmetry. J Chem Phys 2021; 154:204101. [PMID: 34241173 DOI: 10.1063/5.0046853] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The theory behind computation of absolute binding free energies using explicit-solvent molecular simulations is well-established, yet somewhat complex, with counter-intuitive aspects. This leads to frequent frustration, common misconceptions, and sometimes erroneous numerical treatment. To improve this, we present the main practically relevant segments of the theory with constant reference to physical intuition. We pinpoint the role of the implicit or explicit definition of the bound state (or the binding site) to make a robust link between an experimental measurement and a computational result. We clarify the role of symmetry and discuss cases where symmetry number corrections have been misinterpreted. In particular, we argue that symmetry corrections as classically presented are a source of confusion and could be advantageously replaced by restraint free energy contributions. We establish that contrary to a common intuition, partial or missing sampling of some modes of symmetric bound states does not affect the calculated decoupling free energies. Finally, we review these questions and pitfalls in the context of a few common practical situations: binding to a symmetric receptor (equivalent binding sites), binding of a symmetric ligand (equivalent poses), and formation of a symmetric complex, in the case of homodimerization.
Collapse
Affiliation(s)
- E Duboué-Dijon
- CNRS, Université de Paris, UPR 9080, Laboratoire de Biochimie Théorique, 13 rue Pierre et Marie Curie, 75005 Paris, France
| | - J Hénin
- CNRS, Université de Paris, UPR 9080, Laboratoire de Biochimie Théorique, 13 rue Pierre et Marie Curie, 75005 Paris, France
| |
Collapse
|
98
|
Nonadditivity in public and inhouse data: implications for drug design. J Cheminform 2021; 13:47. [PMID: 34215341 PMCID: PMC8254291 DOI: 10.1186/s13321-021-00525-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 06/09/2021] [Indexed: 11/10/2022] Open
Abstract
Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein-ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.
Collapse
|
99
|
Goel H, Hazel A, Ustach VD, Jo S, Yu W, MacKerell AD. Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation. Chem Sci 2021; 12:8844-8858. [PMID: 34257885 PMCID: PMC8246086 DOI: 10.1039/d1sc01781k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/24/2021] [Indexed: 01/18/2023] Open
Abstract
Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.
Collapse
Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Vincent D Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Sunhwan Jo
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
| |
Collapse
|
100
|
Bocian W, Bednarek E, Michalska K. Explanation of the Formation of Complexes between Representatives of Oxazolidinones and HDAS-β-CD Using Molecular Modeling as a Complementary Technique to cEKC and NMR. Int J Mol Sci 2021; 22:7139. [PMID: 34281189 PMCID: PMC8268345 DOI: 10.3390/ijms22137139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/21/2021] [Accepted: 06/26/2021] [Indexed: 12/17/2022] Open
Abstract
Molecular modeling (MM) results for tedizolid and radezolid with heptakis-(2,3-diacetyl-6-sulfo)-β-cyclodextrin (HDAS-β-CD) are presented and compared with the results previously obtained for linezolid and sutezolid. The mechanism of interaction of chiral oxazolidinone ligands belonging to a new class of antibacterial agents, such as linezolid, tedizolid, radezolid, and sutezolid, with HDAS-β-CD based on capillary electrokinetic chromatography (cEKC), nuclear magnetic resonance (NMR) spectroscopy, and MM methods was described. Principles of chiral separation of oxazolidinone analogues using charged single isomer derivatives of cyclodextrin by the cEKC method were presented, including the selection of the optimal chiral selector and separation conditions, complex stoichiometry, and binding constants, which provided a comprehensive basis for MM studies. In turn, NMR provided, where possible, direct information on the geometry of the inclusion complexes and also provided the necessary structural information to validate the MM calculations. Consequently, MM contributed to the understanding of the structure of diastereomeric complexes, the thermodynamics of complexation, and the visualization of their structures. The most probable mean geometries of the studied supramolecular complexes and their dynamics (geometry changes over time) were determined by molecular dynamics methods. Oxazolidinone ligands have been shown to complex mainly the inner part of cyclodextrin, while the external binding is less privileged, which is consistent with the conclusions of the NMR studies. Enthalpy values of binding of complexes were calculated using long-term molecular dynamics in explicit water as well as using molecular mechanics, the Poisson-Boltzmann or generalized Born, and surface area continuum solvation (MM/PBSA and MM/GBSA) methods. Computational methods predicted the effect of changes in pH and composition of the solution on the strength and complexation process, and it adapted the conditions selected as optimal during the cEKC study. By changing the dielectric constant in the MM/PBSA and MM/GBSA calculations, the effect of changing the solution to methanol/acetonitrile was investigated. A fairly successful attempt was made to predict the chiral separation of the oxazolidinones using the modified cyclodextrin by computational methods.
Collapse
Affiliation(s)
- Wojciech Bocian
- Falsified Medicines and Medical Devices Department, National Medicines Institute, Chełmska 30/34, 00-725 Warsaw, Poland; (W.B.); (E.B.)
| | - Elżbieta Bednarek
- Falsified Medicines and Medical Devices Department, National Medicines Institute, Chełmska 30/34, 00-725 Warsaw, Poland; (W.B.); (E.B.)
| | - Katarzyna Michalska
- Department of Synthetic Drugs, National Medicines Institute, Chełmska 30/34, 00-725 Warsaw, Poland
| |
Collapse
|