1
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Gilson MK, Kurtzman T. Free Energy Density of a Fluid and Its Role in Solvation and Binding. J Chem Theory Comput 2024; 20:2871-2887. [PMID: 38536144 PMCID: PMC11197885 DOI: 10.1021/acs.jctc.3c01173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
The concept that a fluid has a position-dependent free energy density appears in the literature but has not been fully developed or accepted. We set this concept on an unambiguous theoretical footing via the following strategy. First, we set forth four desiderata that should be satisfied by any definition of the position-dependent free energy density, f(R), in a system comprising only a fluid and a rigid solute: its volume integral, plus the fixed internal energy of the solute, should be the system free energy; it deviates from its bulk value, fbulk, near a solute but should asymptotically approach fbulk with increasing distance from the solute; it should go to zero where the solvent density goes to zero; and it should be well-defined in the most general case of a fluid made up of flexible molecules with an arbitrary interaction potential. Second, we use statistical thermodynamics to formulate a definition of the free energy density that satisfies these desiderata. Third, we show how any free energy density satisfying the desiderata may be used to analyze molecular processes in solution. In particular, because the spatial integral of f(R) equals the free energy of the system, it can be used to compute free energy changes that result from the rearrangement of solutes as well as the forces exerted on the solutes by the solvent. This enables the use of a thermodynamic analysis of water in protein binding sites to inform ligand design. Finally, we discuss related literature and address published concerns regarding the thermodynamic plausibility of a position-dependent free energy density. The theory presented here has applications in theoretical and computational chemistry and may be further generalizable beyond fluids, such as to solids and macromolecules.
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Affiliation(s)
- Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, and Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA, 92093, USA
| | - Tom Kurtzman
- PhD Programs in Chemistry, Biochemistry, and Biology, The Graduate Center of the City University of New York, New York, 10016, USA; Department of Chemistry, Lehman College, The City University of New York, Bronx, New York, 10468, USA
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2
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Zhi D, An Z, Li L, Zheng C, Yuan X, Lan Y, Zhang J, Xu Y, Ma H, Li N, Wang J. 3-Amide-β-carbolines block the cell cycle by targeting CDK2 and DNA in tumor cells potentially as anti-mitotic agents. Bioorg Chem 2024; 145:107216. [PMID: 38387396 DOI: 10.1016/j.bioorg.2024.107216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/04/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
β-Carboline alkaloids are natural and synthetic products with outstanding antitumor activity. C3 substituted and dimerized β-carbolines exert excellent antitumor activity. In the present research, 37 β-carboline derivatives were synthesized and characterized. Their cytotoxicity, cell cycle, apoptosis, and CDK2- and DNA-binding affinity were evaluated. β-Carboline monomer M3 and dimer D4 showed selective activity and higher cytotoxicity in tumor cells than in normal cells. Structure-activity relationships (SAR) indicated that the amide group at C3 enhanced the antitumor activity. M3 blocked the A549 (IC50 = 1.44 ± 1.10 μM) cell cycle in the S phase and inhibited A549 cell migration, while D4 blocked the HepG2 (IC50 = 2.84 ± 0.73 μM) cell cycle in the G0/G1 phase, both of which ultimately induced apoptosis. Furthermore, associations of M3 and D4 with CDK2 and DNA were proven by network pharmacology analysis, molecular docking, and western blotting. The expression level of CDK2 was downregulated in M3-treated A549 cells and D4-treated HepG2 cells. Moreover, M3 and D4 interact with DNA and CDK2 at sub-micromolar concentrations in endothermic interactions caused by entropy-driven adsorption processes, which means that the favorable entropy change (ΔS > 0) overcomes the unfavorable enthalpy change (ΔH > 0) and drives the spontaneous reaction (ΔG < 0). Overall, these results clarified the antitumor mechanisms of M3 and D4 through disrupting the cell cycle by binding DNA and CDK2, which demonstrated the potential of M3 and D4 as novel antiproliferative drugs targeting mitosis.
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Affiliation(s)
- Dongming Zhi
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Zhiyuan An
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Lishan Li
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Chaojia Zheng
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Xiaorong Yuan
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Yu Lan
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Jinghan Zhang
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Yujie Xu
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Huiya Ma
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China
| | - Na Li
- College of Chemistry and Life Science, Chifeng University, Inner Mongolia Autonomous Region, China.
| | - Junru Wang
- College of Chemistry & Pharmacy, Northwest A&F University, Yangling, China.
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3
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Zsidó BZ, Bayarsaikhan B, Börzsei R, Szél V, Mohos V, Hetényi C. The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering. Int J Mol Sci 2023; 24:11784. [PMID: 37511543 PMCID: PMC10381018 DOI: 10.3390/ijms241411784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.
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Affiliation(s)
- Balázs Zoltán Zsidó
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Bayartsetseg Bayarsaikhan
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Rita Börzsei
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Viktor Szél
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Violetta Mohos
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Csaba Hetényi
- Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
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4
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Eberhardt J, Forli S. WaterKit: Thermodynamic Profiling of Protein Hydration Sites. J Chem Theory Comput 2023; 19:2535-2556. [PMID: 37094087 PMCID: PMC10732097 DOI: 10.1021/acs.jctc.2c01087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.
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Affiliation(s)
- Jerome Eberhardt
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
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5
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Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features. Int J Mol Sci 2022; 23:ijms232315018. [PMID: 36499344 PMCID: PMC9739603 DOI: 10.3390/ijms232315018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 12/02/2022] Open
Abstract
Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand-receptor complexes, which is a common problem in the field.
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6
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Jiang H, Wang J, Cong W, Huang Y, Ramezani M, Sarma A, Dokholyan NV, Mahdavi M, Kandemir MT. Predicting Protein-Ligand Docking Structure with Graph Neural Network. J Chem Inf Model 2022; 62:2923-2932. [PMID: 35699430 PMCID: PMC10279412 DOI: 10.1021/acs.jcim.2c00127] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.
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Affiliation(s)
- Huaipan Jiang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Jian Wang
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Weilin Cong
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Yihe Huang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Morteza Ramezani
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Anup Sarma
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mehrdad Mahdavi
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mahmut T Kandemir
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
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7
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Ahmed M, Ganesan A, Barakat K. Leveraging structural and 2D-QSAR to investigate the role of functional group substitutions, conserved surface residues and desolvation in triggering the small molecule-induced dimerization of hPD-L1. BMC Chem 2022; 16:49. [PMID: 35761353 PMCID: PMC9238240 DOI: 10.1186/s13065-022-00842-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 06/21/2022] [Indexed: 12/02/2022] Open
Abstract
Small molecules are rising as a new generation of immune checkpoints’ inhibitors, with compounds targeting the human Programmed death-ligand 1 (hPD-L1) protein are pioneering this area of research. Promising examples include the recently disclosed compounds from Bristol-Myers-Squibb (BMS). These molecules bind specifically to hPD-L1 through a unique mode of action. They induce dimerization between two hPD-L1 monomers through the hPD-1 binding interface in each monomer, thereby inhibiting the PD-1/PD-L1 axis. While the recently reported crystal structures of such small molecules bound to hPD-L1 reveal valuable insights regarding their molecular interactions, there is still limited information about the dynamics driving this unusual complex formation. The current study provides an in-depth computational structural analysis to study the interactions of five small molecule compounds in complex with hPD-L1. By employing a combination of molecular dynamic simulations, binding energy calculations and computational solvent mapping techniques, our analyses quantified the dynamic roles of different hydrophilic and lipophilic residues at the surface of hPD-L1 in mediating these interactions. Furthermore, ligand-based analyses, including Free-Wilson 2D-QSAR was conducted to quantify the impact of R-group substitutions at different sites of the phenoxy-methyl biphenyl core. Our results emphasize the importance of a terminal phenyl ring that must be present in any hPD-L1 small molecule inhibitor. This phenyl moiety overlaps with a very unfavorable hydration site, which can explain the ability of such small molecules to trigger hPD-L1 dimerization.
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Affiliation(s)
- Marawan Ahmed
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Aravindhan Ganesan
- ArGan's Lab, School of Pharmacy, University of Waterloo, Kitchener, ON, Canada
| | - Khaled Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada. .,Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada.
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8
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Park S, Seok C. GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network. J Chem Inf Model 2022; 62:3157-3168. [PMID: 35749367 DOI: 10.1021/acs.jcim.2c00306] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.
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Affiliation(s)
- Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.,Galux Inc., Gwanak-gu, Seoul 08738, Republic of Korea
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9
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Smith ST, Shub L, Meiler J. PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking. PLoS One 2022; 17:e0269072. [PMID: 35639743 PMCID: PMC9154094 DOI: 10.1371/journal.pone.0269072] [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: 02/02/2022] [Accepted: 05/13/2022] [Indexed: 01/29/2023] Open
Abstract
Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these 'bridging waters' has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named "PlaceWaters" to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.
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Affiliation(s)
- Shannon T. Smith
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Laura Shub
- Biomedical Informatics Program, University of California San Francisco, San Francisco, California, United States of America
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Departments of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology and Institute of Chemical Biology, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, SAC, Leipzig, Germany
- * E-mail:
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10
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Waibl F, Kraml J, Hoerschinger VJ, Hofer F, Kamenik AS, Fernández-Quintero ML, Liedl KR. Grid inhomogeneous solvation theory for cross-solvation in rigid solvents. J Chem Phys 2022; 156:204101. [PMID: 35649837 DOI: 10.1063/5.0087549] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Grid Inhomogeneous Solvation Theory (GIST) has proven useful to calculate localized thermodynamic properties of water around a solute. Numerous studies have leveraged this information to enhance structure-based binding predictions. We have recently extended GIST toward chloroform as a solvent to allow the prediction of passive membrane permeability. Here, we further generalize the GIST algorithm toward all solvents that can be modeled as rigid molecules. This restriction is inherent to the method and is already present in the inhomogeneous solvation theory. Here, we show that our approach can be applied to various solvent molecules by comparing the results of GIST simulations with thermodynamic integration (TI) calculations and experimental results. Additionally, we analyze and compare a matrix consisting of 100 entries of ten different solvent molecules solvated within each other. We find that the GIST results are highly correlated with TI calculations as well as experiments. For some solvents, we find Pearson correlations of up to 0.99 to the true entropy, while others are affected by the first-order approximation more strongly. The enthalpy-entropy splitting provided by GIST allows us to extend a recently published approach, which estimates higher order entropies by a linear scaling of the first-order entropy, to solvents other than water. Furthermore, we investigate the convergence of GIST in different solvents. We conclude that our extension to GIST reliably calculates localized thermodynamic properties for different solvents and thereby significantly extends the applicability of this widely used method.
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Affiliation(s)
- Franz Waibl
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Johannes Kraml
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Valentin J Hoerschinger
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Florian Hofer
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Anna S Kamenik
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Monica L Fernández-Quintero
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
| | - Klaus R Liedl
- Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, Innsbruck, Austria
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11
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Can docking scoring functions guarantee success in virtual screening? VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Nguyen TB, Pires DEV, Ascher DB. CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function. Brief Bioinform 2021; 23:6457169. [PMID: 34882232 DOI: 10.1093/bib/bbab512] [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: 07/23/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 12/29/2022] Open
Abstract
Protein-carbohydrate interactions are crucial for many cellular processes but can be challenging to biologically characterise. To improve our understanding and ability to model these molecular interactions, we used a carefully curated set of 370 protein-carbohydrate complexes with experimental structural and biophysical data in order to train and validate a new tool, cutoff scanning matrix (CSM)-carbohydrate, using machine learning algorithms to accurately predict their binding affinity and rank docking poses as a scoring function. Information on both protein and carbohydrate complementarity, in terms of shape and chemistry, was captured using graph-based structural signatures. Across both training and independent test sets, we achieved comparable Pearson's correlations of 0.72 under cross-validation [root mean square error (RMSE) of 1.58 Kcal/mol] and 0.67 on the independent test (RMSE of 1.72 Kcal/mol), providing confidence in the generalisability and robustness of the final model. Similar performance was obtained across mono-, di- and oligosaccharides, further highlighting the applicability of this approach to the study of larger complexes. We show CSM-carbohydrate significantly outperformed previous approaches and have implemented our method and make all data freely available through both a user-friendly web interface and application programming interface, to facilitate programmatic access at http://biosig.unimelb.edu.au/csm_carbohydrate/. We believe CSM-carbohydrate will be an invaluable tool for helping assess docking poses and the effects of mutations on protein-carbohydrate affinity, unravelling important aspects that drive binding recognition.
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Affiliation(s)
- Thanh Binh Nguyen
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia.,Department of Biochemistry, University of Cambridge, Cambridge, UK
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13
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Asai A, Konno M, Taniguchi M, Vecchione A, Ishii H. Computational healthcare: Present and future perspectives (Review). Exp Ther Med 2021; 22:1351. [PMID: 34659497 PMCID: PMC8515560 DOI: 10.3892/etm.2021.10786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/19/2021] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.
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Affiliation(s)
- Ayumu Asai
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.,Artificial Intelligence Research Center, Osaka University, Ibaraki, Osaka 567-0047, Japan.,The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Masamitsu Konno
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masateru Taniguchi
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan
| | - Andrea Vecchione
- Department of Clinical and Molecular Medicine, University of Rome 'Sapienza', Santo Andrea Hospital, I-1035-00189 Rome, Italy
| | - Hideshi Ishii
- Center of Medical Innovation and Translational Research, Department of Medical Data Science, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
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14
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Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J Chem Inf Model 2021; 61:3891-3898. [PMID: 34278794 PMCID: PMC10683950 DOI: 10.1021/acs.jcim.1c00203] [Citation(s) in RCA: 1498] [Impact Index Per Article: 499.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.
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Affiliation(s)
- Jerome Eberhardt
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Andreas F Tillack
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
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15
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Samways ML, Taylor RD, Bruce Macdonald HE, Essex JW. Water molecules at protein-drug interfaces: computational prediction and analysis methods. Chem Soc Rev 2021; 50:9104-9120. [PMID: 34184009 DOI: 10.1039/d0cs00151a] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.
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Affiliation(s)
- Marley L Samways
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
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16
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Tanaka S, Tokutomi S, Hatada R, Okuwaki K, Akisawa K, Fukuzawa K, Komeiji Y, Okiyama Y, Mochizuki Y. Dynamic Cooperativity of Ligand-Residue Interactions Evaluated with the Fragment Molecular Orbital Method. J Phys Chem B 2021; 125:6501-6512. [PMID: 34124906 DOI: 10.1021/acs.jpcb.1c03043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
By the splendid advance in computation power realized with the Fugaku supercomputer, it has become possible to perform ab initio fragment molecular orbital (FMO) calculations for thousands of dynamic structures of protein-ligand complexes in a parallel way. We thus carried out electron-correlated FMO calculations for a complex of the 3C-like (3CL) main protease (Mpro) of the new coronavirus (SARS-CoV-2) and its inhibitor N3 incorporating the structural fluctuations sampled by classical molecular dynamics (MD) simulation in hydrated conditions. Along with a statistical evaluation of the interfragment interaction energies (IFIEs) between the N3 ligand and the surrounding amino-acid residues for 1000 dynamic structure samples, in this study we applied a novel approach based on principal component analysis (PCA) and singular value decomposition (SVD) to the analysis of IFIE data in order to extract the dynamically cooperative interactions between the ligand and the residues. We found that the relative importance of each residue is modified via the structural fluctuations and that the ligand is bound in the pharmacophore in a dynamic manner through collective interactions formed by multiple residues, thus providing new insight into structure-based drug discovery.
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Affiliation(s)
- Shigenori Tanaka
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Shusuke Tokutomi
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Ryo Hatada
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Koji Okuwaki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kazuki Akisawa
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kaori Fukuzawa
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan.,Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, 6-6-11 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Yuto Komeiji
- Biomedical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan
| | - Yoshio Okiyama
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 201-9501, Japan
| | - Yuji Mochizuki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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17
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Heo L, Park S, Seok C. GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential. J Chem Inf Model 2021; 61:2283-2293. [PMID: 33938216 DOI: 10.1021/acs.jcim.0c01434] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.
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Affiliation(s)
- Lim Heo
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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18
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Chen L, Cruz A, Roe DR, Simmonett AC, Wickstrom L, Deng N, Kurtzman T. Thermodynamic Decomposition of Solvation Free Energies with Particle Mesh Ewald and Long-Range Lennard-Jones Interactions in Grid Inhomogeneous Solvation Theory. J Chem Theory Comput 2021; 17:2714-2724. [PMID: 33830762 DOI: 10.1021/acs.jctc.0c01185] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Grid Inhomogeneous Solvation Theory (GIST) maps out solvation thermodynamic properties on a fine meshed grid and provides a statistical mechanical formalism for thermodynamic end-state calculations. However, differences in how long-range nonbonded interactions are calculated in molecular dynamics engines and in the current implementation of GIST have prevented precise comparisons between free energies estimated using GIST and those from other free energy methods such as thermodynamic integration (TI). Here, we address this by presenting PME-GIST, a formalism by which particle mesh Ewald (PME)-based electrostatic energies and long-range Lennard-Jones (LJ) energies are decomposed and assigned to individual atoms and the corresponding voxels they occupy in a manner consistent with the GIST approach. PME-GIST yields potential energy calculations that are precisely consistent with modern simulation engines and performs these calculations at a dramatically faster speed than prior implementations. Here, we apply PME-GIST end-state analyses to 32 small molecules whose solvation free energies are close to evenly distributed from 2 kcal/mol to -17 kcal/mol and obtain solvation energies consistent with TI calculations (R2 = 0.99, mean unsigned difference 0.8 kcal/mol). We also estimate the entropy contribution from the second and higher order entropy terms that are truncated in GIST by the differences between entropies calculated in TI and GIST. With a simple correction for the high order entropy terms, PME-GIST obtains solvation free energies that are highly consistent with TI calculations (R2 = 0.99, mean unsigned difference = 0.4 kcal/mol) and experimental results (R2 = 0.88, mean unsigned difference = 1.4 kcal/mol). The precision of PME-GIST also enables us to show that the solvation free energy of small hydrophobic and hydrophilic molecules can be largely understood based on perturbations of the solvent in a region extending a few solvation shells from the solute. We have integrated PME-GIST into the open-source molecular dynamics analysis software CPPTRAJ.
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Affiliation(s)
- Lieyang Chen
- Department of Chemistry, Lehman College, The City University of New York, 250 Bedford Park Boulevard West, Bronx, New York 10468, United States.,Ph.D. Program in Biochemistry, The Graduate Center of The City University of New York, New York, New York 10016, United States
| | - Anthony Cruz
- Department of Chemistry, Lehman College, The City University of New York, 250 Bedford Park Boulevard West, Bronx, New York 10468, United States.,Ph.D. Program in Chemistry, The Graduate Center of The City University of New York, New York, New York 10016, United States
| | - Daniel R Roe
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, United States
| | - Andrew C Simmonett
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, United States
| | - Lauren Wickstrom
- Department of Science, Borough of Manhattan Community College, The City University of New York, New York, New York 10007, United States
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York 10038, United States
| | - Tom Kurtzman
- Department of Chemistry, Lehman College, The City University of New York, 250 Bedford Park Boulevard West, Bronx, New York 10468, United States.,Ph.D. Program in Biochemistry, The Graduate Center of The City University of New York, New York, New York 10016, United States.,Ph.D. Program in Chemistry, The Graduate Center of The City University of New York, New York, New York 10016, United States
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19
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Bitencourt-Ferreira G, Duarte da Silva A, Filgueira de Azevedo W. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2. Curr Med Chem 2021; 28:253-265. [PMID: 31729287 DOI: 10.2174/2213275912666191102162959] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/22/2019] [Accepted: 09/24/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. OBJECTIVE Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. METHODS We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. RESULTS Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. CONCLUSION Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.
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Affiliation(s)
- Gabriela Bitencourt-Ferreira
- Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil
| | - Amauri Duarte da Silva
- Specialization Program in Bioinformatics. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900, Brazil
| | - Walter Filgueira de Azevedo
- Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil
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20
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Olson B, Cruz A, Chen L, Ghattas M, Ji Y, Huang K, Ayoub S, Luchko T, McKay DJ, Kurtzman T. An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets. J Comput Aided Mol Des 2020; 34:1219-1228. [PMID: 32918236 PMCID: PMC7486166 DOI: 10.1007/s10822-020-00341-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 08/29/2020] [Indexed: 12/01/2022]
Abstract
SARS-CoV-2 recently jumped species and rapidly spread via human-to-human transmission to cause a global outbreak of COVID-19. The lack of effective vaccine combined with the severity of the disease necessitates attempts to develop small molecule drugs to combat the virus. COVID19_GIST_HSA is a freely available online repository to provide solvation thermodynamic maps of COVID-19-related protein small molecule drug targets. Grid inhomogeneous solvation theory maps were generated using AmberTools cpptraj-GIST, 3D reference interaction site model maps were created with AmberTools rism3d.snglpnt and hydration site analysis maps were created using SSTMap code. The resultant data can be applied to drug design efforts: scoring solvent displacement for docking, rational lead modification, prioritization of ligand- and protein- based pharmacophore elements, and creation of water-based pharmacophores. Herein, we demonstrate the use of the solvation thermodynamic mapping data. It is hoped that this freely provided data will aid in small molecule drug discovery efforts to defeat SARS-CoV-2.
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Affiliation(s)
- Brian Olson
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
- Department of Biology and Chemistry, County College of Morris, 214 Center Grove Rd, Randolph, NJ, 07869, USA
| | - Anthony Cruz
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Lieyang Chen
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Mossa Ghattas
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Yeonji Ji
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Kunhui Huang
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA
| | - Steven Ayoub
- Department of Chemistry and Biochemistry, California State University, Northridge, 18111 Nordhoff Street, Northridge, CA, 91330, USA
| | - Tyler Luchko
- Department of Physics and Astronomy, Center for Biological Physics, California State University, Northridge, 18111 Nordhoff Street, Northridge, CA, 91330, USA
| | - Daniel J McKay
- Ventus Therapeutics, Frederick-Banting, Montreal, QC, H9S 2A1, Canada
| | - Tom Kurtzman
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd, Bronx, NY, 10468, USA.
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA.
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York, NY, 10016, USA.
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21
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Jiang H, Fan M, Wang J, Sarma A, Mohanty S, Dokholyan NV, Mahdavi M, Kandemir MT. Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks. J Chem Inf Model 2020; 60:4594-4602. [PMID: 33100014 PMCID: PMC10706896 DOI: 10.1021/acs.jcim.0c00542] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.
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Affiliation(s)
- Huaipan Jiang
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Mengran Fan
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Jian Wang
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey 17033, United States
| | - Anup Sarma
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Shruti Mohanty
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, State College 16802, United States
| | - Mehrdad Mahdavi
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
| | - Mahmut T Kandemir
- Department of Computer Science and Engineering, Pennsylvania State University, State College 16802, United States
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22
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Kraml J, Hofer F, Kamenik AS, Waibl F, Kahler U, Schauperl M, Liedl KR. Solvation Thermodynamics in Different Solvents: Water-Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory. J Chem Inf Model 2020; 60:3843-3853. [PMID: 32639731 PMCID: PMC7460078 DOI: 10.1021/acs.jcim.0c00289] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Indexed: 11/28/2022]
Abstract
Reliable information on partition coefficients plays a key role in drug development, as solubility decisively affects bioavailability. In a physicochemical context, the partition coefficient of a solute between two different solvents can be described as a function of solvation free energies. Hence, substantial scientific efforts have been made toward accurate predictions of solvation free energies in various solvents. The grid inhomogeneous solvation theory (GIST) facilitates the calculation of solvation free energies. In this study, we introduce an extended version of the GIST algorithm, which enables the calculation for chloroform in addition to water. Furthermore, GIST allows localization of enthalpic and entropic contributions. We test our approach by calculating partition coefficients between water and chloroform for a set of eight small molecules. We report a Pearson correlation coefficient of 0.96 between experimentally determined and calculated partition coefficients. The capability to reliably predict partition coefficients between water and chloroform and the possibility to localize their contributions allow the optimization of a compound's partition coefficient. Therefore, we presume that this methodology will be of great benefit for the efficient development of pharmaceuticals.
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Affiliation(s)
- Johannes Kraml
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Florian Hofer
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Franz Waibl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Ursula Kahler
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Michael Schauperl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
| | - Klaus R. Liedl
- Institute of General, Inorganic and
Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80−82, A-6020 Innsbruck, Austria
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23
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Olson B, Cruz A, Chen L, Ghattas M, Ji Y, Huang K, McKay DJ, Kurtzman T. An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:10.26434/chemrxiv.12275705.v1. [PMID: 32511289 PMCID: PMC7263766 DOI: 10.26434/chemrxiv.12275705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 05/13/2020] [Indexed: 11/09/2022]
Abstract
SARS-CoV-2 recently jumped species and rapidly spread via human-to-human transmission to cause a global outbreak of COVID-19. The lack of effective vaccine combined with the severity of the disease necessitates attempts to develop small molecule drugs to combat the virus. COVID19_GIST_HSA is a freely available online repository to provide solvation thermodynamic maps of COVID-19-related protein small molecule drug targets. Grid Inhomogeneous Solvation Theory maps were generated using AmberTools cpptraj-GIST and Hydration Site Analysis maps were created using SSTmap code. The resultant data can be applied to drug design efforts: scoring solvent displacement for docking, rational lead modification, prioritization of ligand- and protein- based pharmacophore elements, and creation of water-based pharmacophores. Herein, we demonstrate the use of the solvation thermodynamic mapping data. It is hoped that this freely provided data will aid in small molecule drug discovery efforts to defeat SARS-CoV-2.
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Affiliation(s)
- Brian Olson
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
- County College of Morris, Department of Biology and Chemistry, 214 Center Grove Rd, Randolph, NJ, United States of America, 07869
| | - Anthony Cruz
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
| | - Lieyang Chen
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
| | - Mossa Ghattas
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
| | - Yeonji Ji
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
| | - Kunhui Huang
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
| | - Daniel J McKay
- Ventus Therapeutics, 7150 Frederick-Banting Montreal, Quebec H9S 2A1
| | - Tom Kurtzman
- Lehman College Department of Chemistry, 205 W Bedford Park Blvd Bronx, NY, United States of America, 10468
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, 365 5th Avenue, New York New York, United States of America, 10016
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24
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Hüfner-Wulsdorf T, Klebe G. Role of Water Molecules in Protein–Ligand Dissociation and Selectivity Discrimination: Analysis of the Mechanisms and Kinetics of Biomolecular Solvation Using Molecular Dynamics. J Chem Inf Model 2020; 60:1818-1832. [DOI: 10.1021/acs.jcim.0c00156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Tobias Hüfner-Wulsdorf
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
| | - Gerhard Klebe
- Institut für Pharmazeutische Chemie, Philipps Universität Marburg, Marbacher Weg 6, 35037 Marburg, Germany
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25
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Mitusińska K, Raczyńska A, Bzówka M, Bagrowska W, Góra A. Applications of water molecules for analysis of macromolecule properties. Comput Struct Biotechnol J 2020; 18:355-365. [PMID: 32123557 PMCID: PMC7036622 DOI: 10.1016/j.csbj.2020.02.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/26/2020] [Accepted: 02/01/2020] [Indexed: 01/12/2023] Open
Abstract
Water molecules maintain proteins' structures, functions, stabilities and dynamics. They can occupy certain positions or pass quickly via a protein's interior. Regardless of their behaviour, water molecules can be used for the analysis of proteins' structural features and biochemical properties. Here, we present a list of several software programs that use the information provided by water molecules to: i) analyse protein structures and provide the optimal positions of water molecules for protein hydration, ii) identify high-occupancy water sites in order to analyse ligand binding modes, and iii) detect and describe tunnels and cavities. The analysis of water molecules' distribution and trajectories sheds a light on proteins' interactions with small molecules, on the dynamics of tunnels and cavities, on protein composition and also on the functionality, transportation network and location of functionally relevant residues. Finally, the correct placement of water molecules in protein crystal structures can significantly improve the reliability of molecular dynamics simulations.
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Affiliation(s)
| | | | | | | | - Artur Góra
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, Gliwice, Poland
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26
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Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
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Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
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27
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Kraml J, Kamenik AS, Waibl F, Schauperl M, Liedl KR. Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces. J Chem Theory Comput 2019; 15:5872-5882. [PMID: 31589427 PMCID: PMC7032847 DOI: 10.1021/acs.jctc.9b00742] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Indexed: 12/27/2022]
Abstract
Solvation and hydrophobicity play a key role in a variety of biological mechanisms. In substrate binding, but also in structure-based drug design, the thermodynamic properties of water molecules surrounding a given protein are of high interest. One of the main algorithms devised in recent years to quantify thermodynamic properties of water is the grid inhomogeneous solvation theory (GIST), which calculates these features on a grid surrounding the protein. Despite the inherent advantages of GIST, the computational demand is a major drawback, as calculations for larger systems can take days or even weeks. Here, we present a GPU accelerated version of the GIST algorithm, which facilitates efficient estimates of solvation free energy even of large biomolecular interfaces. Furthermore, we show that GIST can be used as a reliable tool to evaluate protein surface hydrophobicity. We apply the approach on a set of nine different proteases calculating localized solvation free energies on the surface of the binding interfaces as a measure of their hydrophobicity. We find a compelling agreement with the hydrophobicity of their substrates, i.e., peptides, binding into the binding cleft, and thus our approach provides a reliable description of hydrophobicity characteristics of these biological interfaces.
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Affiliation(s)
- Johannes Kraml
- Institute
of General, Inorganic and Theoretical Chemistry and Center for Molecular
Biosciences Innsbruck (CMBI), University
of Innsbruck, Innsbruck 6020, Austria
| | - Anna S. Kamenik
- Institute
of General, Inorganic and Theoretical Chemistry and Center for Molecular
Biosciences Innsbruck (CMBI), University
of Innsbruck, Innsbruck 6020, Austria
| | - Franz Waibl
- Institute
of General, Inorganic and Theoretical Chemistry and Center for Molecular
Biosciences Innsbruck (CMBI), University
of Innsbruck, Innsbruck 6020, Austria
| | - Michael Schauperl
- Skaggs
School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92039-0736, United States
| | - Klaus R. Liedl
- Institute
of General, Inorganic and Theoretical Chemistry and Center for Molecular
Biosciences Innsbruck (CMBI), University
of Innsbruck, Innsbruck 6020, Austria
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28
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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9214538] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.
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29
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Loeffler JR, Schauperl M, Liedl KR. Hydration of Aromatic Heterocycles as an Adversary of π-Stacking. J Chem Inf Model 2019; 59:4209-4219. [PMID: 31566975 PMCID: PMC7032848 DOI: 10.1021/acs.jcim.9b00395] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Hydration is one of the key players in the protein-ligand binding process. It not only influences the binding process per se, but also the drug's absorption, distribution, metabolism, and excretion properties. To gain insights into the hydration of aromatic cores, the solvation thermodynamics of 40 aromatic mono- and bicyclic systems, frequently occurring in medicinal chemistry, are investigated. Thermodynamics is analyzed with two different methods: grid inhomogeneous solvation theory (GIST) and thermodynamic integration (TI). Our results agree well with previously published experimental and computational solvation free energies. The influence of adding heteroatoms to aromatic systems and how the position of these heteroatoms impacts the compound's interactions with water is studied. The solvation free energies of these heteroaromatics are highly correlated to their gas phase interaction energies with benzene: compounds showing a high interaction energy also have a high solvation free energy value. Therefore, replacing a compound with one having a higher gas phase interaction energy might not result in the expected improvement in affinity. The desolvation costs counteract the higher stacking interactions, hence weakening or even inverting the expected gain in binding free energy.
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Affiliation(s)
- Johannes R Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, and Center of Molecular Biosciences Innsbruck (CMBI) , University of Innsbruck , Innrain 80-82 , A-6020 Innsbruck , Tyrol , Austria
| | - Michael Schauperl
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92039-0736 , United States
| | - Klaus R Liedl
- Institute of General, Inorganic and Theoretical Chemistry, and Center of Molecular Biosciences Innsbruck (CMBI) , University of Innsbruck , Innrain 80-82 , A-6020 Innsbruck , Tyrol , Austria
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30
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Lu Q, Qi LW, Liu J. Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2019. [DOI: 10.1142/s0219633619500275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.
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Affiliation(s)
- Qiangna Lu
- State Key Laboratory of Natural Medicines, Department of Chinese Medicine, Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, P. R. China
| | - Lian-Wen Qi
- State Key Laboratory of Natural Medicines, Department of Chinese Medicine, Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, P. R. China
| | - Jinfeng Liu
- State Key Laboratory of Natural Medicines, Department of Chinese Medicine, Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, P. R. China
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31
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Lee MH, Lee DY, Balupuri A, Jeong JW, Kang NS. Pharmacophoric Site Identification and Inhibitor Design for Autotaxin. Molecules 2019; 24:molecules24152808. [PMID: 31374894 PMCID: PMC6696049 DOI: 10.3390/molecules24152808] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 07/25/2019] [Accepted: 07/29/2019] [Indexed: 02/06/2023] Open
Abstract
Autotaxin (ATX) is a potential drug target that is associated with inflammatory diseases and various cancers. In our previous studies, we have designed several inhibitors targeting ATX using computational and experimental approaches. Here, we have analyzed topological water networks (TWNs) in the binding pocket of ATX. TWN analysis revealed a pharmacophoric site inside the pocket. We designed and synthesized compounds considering the identified pharmacophoric site. Furthermore, we performed biological experiments to determine their ATX inhibitory activities. High potency of the designed compounds supports the predictions of the TWN analysis.
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Affiliation(s)
- Myeong Hwi Lee
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Dae-Yon Lee
- LegoChem Biosciences, Inc., 8-26 Munoyeongseo-ro, Daedeok-gu, Daejeon 34302, Korea
| | - Anand Balupuri
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Jong-Woo Jeong
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Nam Sook Kang
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
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32
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Fong P, Wong HK. Evaluation of Scoring Function Performance on DNA-ligand Complexes. THE OPEN MEDICINAL CHEMISTRY JOURNAL 2019. [DOI: 10.2174/1874104501913010040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Background:
DNA has been a pharmacological target for different types of treatment, such as antibiotics and chemotherapy agents, and is still a potential target in many drug discovery processes. However, most docking and scoring approaches were parameterised for protein-ligand interactions; their suitability for modelling DNA-ligand interactions is uncertain.
Objective:
This study investigated the performance of four scoring functions on DNA-ligand complexes.
Material & Methods:
Here, we explored the ability of four docking protocols and scoring functions to discriminate the native pose of 33 DNA-ligand complexes over a compiled set of 200 decoys for each DNA-ligand complexes. The four approaches were the AutoDock, ASP@GOLD, ChemScore@GOLD and GoldScore@GOLD.
Results:
Our results indicate that AutoDock performed the best when predicting binding mode and that ChemScore@GOLD achieved the best discriminative power. Rescoring of AutoDock-generated decoys with ChemScore@GOLD further enhanced their individual discriminative powers. All four approaches have no discriminative power in some DNA-ligand complexes, including both minor groove binders and intercalators.
Conclusion:
This study suggests that the evaluation for each DNA-ligand complex should be performed in order to obtain meaningful results for any drug discovery processes. Rescoring with different scoring functions can improve discriminative power.
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33
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Li J, Fu A, Zhang L. An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking. Interdiscip Sci 2019; 11:320-328. [PMID: 30877639 DOI: 10.1007/s12539-019-00327-w] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 02/06/2019] [Accepted: 03/06/2019] [Indexed: 12/17/2022]
Abstract
Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.
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Affiliation(s)
- Jin Li
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.,School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China
| | - Ailing Fu
- College of Pharmaceutical Sciences, Southwest University, Chongqing, 400715, China
| | - Le Zhang
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China. .,College of Computer Science, Sichuan University, Chengdu, 610065, China. .,Medical Big Data Center, Sichuan University, Chengdu, 610065, China. .,Zdmedical, Information Polytron Technologies Inc Chongqing, Chongqing, 401320, China.
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34
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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35
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Wahl J, Smieško M. Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations. ChemMedChem 2018; 13:1325-1335. [DOI: 10.1002/cmdc.201800093] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 04/07/2018] [Indexed: 01/11/2023]
Affiliation(s)
- Joel Wahl
- Molecular Modeling, Department of Pharmaceutical Sciences; University of Basel; Klingelbergstrasse 50 4056 Basel Switzerland
| | - Martin Smieško
- Molecular Modeling, Department of Pharmaceutical Sciences; University of Basel; Klingelbergstrasse 50 4056 Basel Switzerland
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36
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Abstract
Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.
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Affiliation(s)
- Naushad Velgy
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - George Hedger
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Philip C Biggin
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
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37
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Chen D, Li Y, Zhao M, Tan W, Li X, Savidge T, Guo W, Fan X. Effective lead optimization targeting the displacement of bridging receptor–ligand water molecules. Phys Chem Chem Phys 2018; 20:24399-24407. [DOI: 10.1039/c8cp04118k] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Enhancing the binding affinities of ligands by means of lead modifications that displace bridging water molecules at protein–ligand interfaces is an important and widely studied lead optimization strategy.
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Affiliation(s)
- Deliang Chen
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Yibao Li
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Mingming Zhao
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Wen Tan
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Xun Li
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Tor Savidge
- Department of Pathology & Immunology
- Baylor College of Medicine
- Houston
- USA
- Texas Children's Microbiome Center
| | - Wei Guo
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
| | - Xiaolin Fan
- Jiangxi Key Laboratory of Organo-Pharmaceutical Chemistry
- Chemistry and Chemical Engineering College
- Gannan Normal University
- Ganzhou
- P. R. China
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38
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Matsunaga S, Hano Y, Saito Y, Fujimoto KJ, Kumasaka T, Matsumoto S, Kataoka T, Shima F, Tanaka S. Structural transition of solvated H-Ras/GTP revealed by molecular dynamics simulation and local network entropy. J Mol Graph Model 2017; 77:51-63. [DOI: 10.1016/j.jmgm.2017.07.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 07/22/2017] [Accepted: 07/25/2017] [Indexed: 12/30/2022]
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39
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Wojciechowski M. Simplified AutoDock force field for hydrated binding sites. J Mol Graph Model 2017; 78:74-80. [PMID: 29054096 DOI: 10.1016/j.jmgm.2017.09.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 09/18/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
A set of high quality structures of protein-ligand complexes with experimentally determined binding affinities has been extracted from the Protein Data Bank and used to test and recalibrate AutoDock force field. Since for some binding sites water molecules are crucial for bridging the receptor-ligand interactions, they have to be included in the analysis. To simplify the process of incorporating water molecules into the binding sites and make it less ambiguous, new simple water model was created. After recalibration of the force field on the new dataset much better correlation between the computed and experimentally determined binding affinities was achieved and the quality of pose prediction improved even more.
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Affiliation(s)
- Marek Wojciechowski
- Department of Pharmaceutical Technology and Biochemistry, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland.
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40
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Testing inhomogeneous solvation theory in structure-based ligand discovery. Proc Natl Acad Sci U S A 2017; 114:E6839-E6846. [PMID: 28760952 DOI: 10.1073/pnas.1703287114] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory (IST) has become popular for treating this effect, but it has not been tested in controlled experiments at atomic resolution. To do so, we turned to a grid-based version of this method, GIST, readily implemented in molecular docking. Whereas the term only improves docking modestly in retrospective ligand enrichment, it could be added without disrupting performance. We thus turned to prospective docking of large libraries to investigate GIST's impact on ligand discovery, geometry, and water structure in a model cavity site well-suited to exploring these terms. Although top-ranked docked molecules with and without the GIST term often overlapped, many ligands were meaningfully prioritized or deprioritized; some of these were selected for testing. Experimentally, 13/14 molecules prioritized by GIST did bind, whereas none of the molecules that it deprioritized were observed to bind. Nine crystal complexes were determined. In six, the ligand geometry corresponded to that predicted by GIST, for one of these the pose without the GIST term was wrong, and three crystallographic poses differed from both predictions. Notably, in one structure, an ordered water molecule with a high GIST displacement penalty was observed to stay in place. Inclusion of this water-displacement term can substantially improve the hit rates and ligand geometries from docking screens, although the magnitude of its effects can be small and its impact in drug binding sites merits further controlled studies.
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