1
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Huang P, Åbacka H, Wilson CJ, Wind ML, Rűtzler M, Hagström-Andersson A, Gourdon P, de Groot BL, Venskutonytė R, Lindkvist-Petersson K. Molecular basis for human aquaporin inhibition. Proc Natl Acad Sci U S A 2024; 121:e2319682121. [PMID: 38319972 PMCID: PMC10873552 DOI: 10.1073/pnas.2319682121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/04/2024] [Indexed: 02/08/2024] Open
Abstract
Cancer invasion and metastasis are known to be potentiated by the expression of aquaporins (AQPs). Likewise, the expression levels of AQPs have been shown to be prognostic for survival in patients and have a role in tumor growth, edema, angiogenesis, and tumor cell migration. Thus, AQPs are key players in cancer biology and potential targets for drug development. Here, we present the single-particle cryo-EM structure of human AQP7 at 3.2-Å resolution in complex with the specific inhibitor compound Z433927330. The structure in combination with MD simulations shows that the inhibitor binds to the endofacial side of AQP7. In addition, cancer cells treated with Z433927330 show reduced proliferation. The data presented here serve as a framework for the development of AQP inhibitors.
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Affiliation(s)
- Peng Huang
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Hannah Åbacka
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Carter J. Wilson
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Malene Lykke Wind
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Michael Rűtzler
- ApoGlyx, Lund22381, Sweden
- Division of Biochemistry and Structural Biology, Department of Chemistry, Lund University, Lund22100, Sweden
| | - Anna Hagström-Andersson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund22184, Sweden
| | - Pontus Gourdon
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Bert L. de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Raminta Venskutonytė
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
| | - Karin Lindkvist-Petersson
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
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2
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Hayes RL, Nixon CF, Marqusee S, Brooks CL. Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design. Proc Natl Acad Sci U S A 2024; 121:e2312029121. [PMID: 38194446 PMCID: PMC10801872 DOI: 10.1073/pnas.2312029121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024] Open
Abstract
Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.
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Affiliation(s)
- Ryan L. Hayes
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA92697
- Department of Chemistry, University of Michigan, Ann Arbor, MI48109
| | - Charlotte F. Nixon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
| | - Susan Marqusee
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA94720
- Department of Chemistry, University of California, Berkeley, CA94720
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, MI48109
- Biophysics Program, University of Michigan, Ann Arbor, MI48109
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3
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Martinez-Martin I, Crousilles A, Ochoa JP, Velazquez-Carreras D, Mortensen SA, Herrero-Galan E, Delgado J, Dominguez F, Garcia-Pavia P, de Sancho D, Wilmanns M, Alegre-Cebollada J. Titin domains with reduced core hydrophobicity cause dilated cardiomyopathy. Cell Rep 2023; 42:113490. [PMID: 38052212 DOI: 10.1016/j.celrep.2023.113490] [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: 05/25/2023] [Revised: 09/28/2023] [Accepted: 11/06/2023] [Indexed: 12/07/2023] Open
Abstract
The underlying genetic defect in most cases of dilated cardiomyopathy (DCM), a common inherited heart disease, remains unknown. Intriguingly, many patients carry single missense variants of uncertain pathogenicity targeting the giant protein titin, a fundamental sarcomere component. To explore the deleterious potential of these variants, we first solved the wild-type and mutant crystal structures of I21, the titin domain targeted by pathogenic variant p.C3575S. Although both structures are remarkably similar, the reduced hydrophobicity of deeply buried position 3575 strongly destabilizes the mutant domain, a scenario supported by molecular dynamics simulations and by biochemical assays that show no disulfide involving C3575. Prompted by these observations, we have found that thousands of similar hydrophobicity-reducing variants associate specifically with DCM. Hence, our results imply that titin domain destabilization causes DCM, a conceptual framework that not only informs pathogenicity assessment of gene variants but also points to therapeutic strategies counterbalancing protein destabilization.
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Affiliation(s)
- Ines Martinez-Martin
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain.
| | - Audrey Crousilles
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, 22607 Hamburg, Germany
| | - Juan Pablo Ochoa
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda, IDIPHIM, CIBERCV, 28222 Madrid, Spain; Health in Code, 15008 A Coruña, Spain
| | | | - Simon A Mortensen
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, 22607 Hamburg, Germany
| | - Elias Herrero-Galan
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Javier Delgado
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain
| | - Fernando Dominguez
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda, IDIPHIM, CIBERCV, 28222 Madrid, Spain
| | - Pablo Garcia-Pavia
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain; Heart Failure and Inherited Cardiac Diseases Unit, Department of Cardiology, Hospital Universitario Puerta de Hierro Majadahonda, IDIPHIM, CIBERCV, 28222 Madrid, Spain
| | - David de Sancho
- Polimero eta Material Aurreratuak: Fisika, Kimika eta Teknologia, Kimika Fakultatea, UPV/EHU, 20018 Donostia-San Sebastian, Euskadi, Spain; Donostia International Physics Center (DIPC), 20018 Donostia-San Sebastian, Euskadi, Spain
| | - Matthias Wilmanns
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, 22607 Hamburg, Germany
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4
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Eccleston RC, Manko E, Campino S, Clark TG, Furnham N. A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations. eLife 2023; 12:e84756. [PMID: 38132182 PMCID: PMC10807863 DOI: 10.7554/elife.84756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/21/2023] [Indexed: 12/23/2023] Open
Abstract
Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.
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Affiliation(s)
- Ruth Charlotte Eccleston
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Emilia Manko
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Susana Campino
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Taane G Clark
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
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5
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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6
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Long Y, Donald BR. Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567384. [PMID: 38014181 PMCID: PMC10680814 DOI: 10.1101/2023.11.16.567384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Accurate binding affinity prediction is crucial to structure-based drug design. Recent work used computational topology to obtain an effective representation of protein-ligand interactions. Although persistent homology encodes geometric features, previous works on binding affinity prediction using persistent homology employed uninterpretable machine learning models and failed to explain the underlying geometric and topological features that drive accurate binding affinity prediction. In this work, we propose a novel, interpretable algorithm for protein-ligand binding affinity prediction. Our algorithm achieves interpretability through an effective embedding of distances across bipartite matchings of the protein and ligand atoms into real-valued functions by summing Gaussians centered at features constructed by persistent homology. We name these functions internuclear persistent contours (IPCs) . Next, we introduce persistence fingerprints , a vector with 10 components that sketches the distances of different bipartite matching between protein and ligand atoms, refined from IPCs. Let the number of protein atoms in the protein-ligand complex be n , number of ligand atoms be m , and ω ≈ 2.4 be the matrix multiplication exponent. We show that for any 0 < ε < 1, after an 𝒪 ( mn log( mn )) preprocessing procedure, we can compute an ε -accurate approximation to the persistence fingerprint in 𝒪 ( m log 6 ω ( m/" )) time, independent of protein size. This is an improvement in time complexity by a factor of 𝒪 (( m + n ) 3 ) over any previous binding affinity prediction that uses persistent homology. We show that the representational power of persistence fingerprint generalizes to protein-ligand binding datasets beyond the training dataset. Then, we introduce PATH , Predicting Affinity Through Homology, an interpretable, small ensemble of shallow regression trees for binding affinity prediction from persistence fingerprints. We show that despite using 1,400-fold fewer features, PATH has comparable performance to a previous state-of-the-art binding affinity prediction algorithm that uses persistent homology features. Moreover, PATH has the advantage of being interpretable. Finally, we visualize the features captured by persistence fingerprint for variant HIV-1 protease complexes and show that persistence fingerprint captures binding-relevant structural mutations. The source code for PATH is released open-source as part of the osprey protein design software package.
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7
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Wan S, Bhati AP, Coveney PV. Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions. J Chem Theory Comput 2023; 19:7846-7860. [PMID: 37862058 PMCID: PMC10653111 DOI: 10.1021/acs.jctc.3c00842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Indexed: 10/21/2023]
Abstract
Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.
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Affiliation(s)
- Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
| | - Agastya P. Bhati
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U.K.
- Advanced
Research Computing Centre, University College
London, London WC1H 0AJ, U.K.
- Computational
Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam 1012 WP, Netherlands
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8
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Velasco-Saavedra MA, Mar-Antonio E, Aguayo-Ortiz R. Molecular Insights into the Covalent Binding of Zoxamide to the β-Tubulin of Botrytis cinerea. J Chem Inf Model 2023; 63:6386-6395. [PMID: 37802126 DOI: 10.1021/acs.jcim.3c00911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Botrytis cinerea is a fungal plant pathogen that causes significant economic losses in the agricultural industry worldwide. Fungicides that target microtubules, such as carbendazim (CBZ), diethofencarb (DEF), and zoxamide (ZOX), are widely used in crop protection against this pathogen. These groups of compounds exert their fungicidal activity by disrupting the microtubule assembly by binding to the β-tubulin subunit, provoking cell-cycle arrest and cell death. However, with the appearance of isolates resistant to these compounds, it is necessary to search for new alternatives to control this pathogenic fungus. In this work, we gained insight into the binding and stability of these fungicides in the benzimidazole binding site of B. cinerea β-tubulin through different computational approaches. Our molecular dynamics simulation replicas showed that R enantiomers of ZOX and its analog RH-4032 had better interaction profiles at the site compared to S enantiomers. The simulations also revealed that while the R-isomer fungicides formed H-bonds with the main chain carbonyl of V236 or the side chain residue of S314, only CBZ interacted with E198. Previous experimental data have identified key mutations in B. cinerea's β-tubulin gene that lead to the development of resistance or, on the contrary, increased sensitivity for treatment with these fungicide compounds. In agreement with experimental findings, alchemical free energy calculations showed that E198A and E198V mutations in B. cinerea β-tubulin have high sensitivity to (R)-ZOX, whereas the E198K mutation decreased its affinity. Similarly, the results obtained explain the resistance to CBZ of B. cinerea isolates with E198A/V/K mutations and the insensitivity of the wild-type organism to DEF. Our work provides a deeper insight into the molecular mechanism of action of these fungicides, highlighting the importance of understanding the interaction profiles to develop more effective antifungal agents.
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Affiliation(s)
- M Andrés Velasco-Saavedra
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Efrén Mar-Antonio
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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Yang G, Hu Z, Wang Y, Mo H, Liu S, Hou X, Wu X, Jiang H, Fang Y. Engineering chitin deacetylase AsCDA for improving the catalytic efficiency towards crystalline chitin. Carbohydr Polym 2023; 318:121123. [PMID: 37479438 DOI: 10.1016/j.carbpol.2023.121123] [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: 02/24/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 07/23/2023]
Abstract
Chitin deacetylase (CDA) catalyzing the deacetylation of crystal chitin is a crucial step in the biosynthesis of chitosan, and also a scientific problem to be solved, which restricts the high-value utilization of chitin resources. This study aims to improve the catalytic efficiency of AsCDA from Acinetobacter schindleri MCDA01 by a semi-rational design using alanine scanning mutagenesis and saturation mutagenesis. The quadruple mutant M11 displayed a 2.31 and 1.73-fold improvement in kcat/Km and specific activity over AsCDA, which can remove 68 % of the acetyl groups from α-chitin. Furthermore, structural analysis suggested that additional hydrogen bonds, contributing the flexibility of amino acids and increasing the negative charge in M11 increased the catalytic efficiency. The microstructure changes of α-chitin pretreated by the mutant M11 were observed and evaluated using 13C CP/MAS NMR spectroscopy, FT-IR spectroscopy, XRD and SEM, and the results showed that M11 more efficiently catalyzed the release of acetyl groups from α-chitin. This study would provide a theoretical basis for the molecular modification of CDAs and accelerate the process of industrial production of chitosan by CDAs.
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Affiliation(s)
- Guang Yang
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China; Jiangsu Marine Resources Development Research Institute, Jiangsu Ocean University, Lianyungang 222000, China; Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Zhihong Hu
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Yuhan Wang
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Hongjuan Mo
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Shu Liu
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China; Jiangsu Marine Resources Development Research Institute, Jiangsu Ocean University, Lianyungang 222000, China; Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Xiaoyue Hou
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China; Jiangsu Marine Resources Development Research Institute, Jiangsu Ocean University, Lianyungang 222000, China; Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Xudong Wu
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China
| | - Hong Jiang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China.
| | - Yaowei Fang
- College of Food Science and Engineering, Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China; Jiangsu Marine Resources Development Research Institute, Jiangsu Ocean University, Lianyungang 222000, China; Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang 222005, China.
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10
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Huang YQ, Wang S, Gong DH, Kumar V, Dong YW, Hao GF. In silico resources help combat cancer drug resistance mediated by target mutations. Drug Discov Today 2023; 28:103686. [PMID: 37379904 DOI: 10.1016/j.drudis.2023.103686] [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: 02/27/2023] [Revised: 05/31/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023]
Abstract
Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.
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Affiliation(s)
- Yuan-Qin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Shuang Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Dao-Hong Gong
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Vinit Kumar
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Ya-Wen Dong
- School of Pharmaceutical Sciences, Guizhou University, Guiyang 550025, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China.
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11
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Silvestri G, Arrigoni F, Persico F, Bertini L, Zampella G, De Gioia L, Vertemara J. Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation. Molecules 2023; 28:6016. [PMID: 37630271 PMCID: PMC10459689 DOI: 10.3390/molecules28166016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.
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Affiliation(s)
| | | | | | | | | | - Luca De Gioia
- Department of Biotechnology and Biosciences BtBs, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Jacopo Vertemara
- Department of Biotechnology and Biosciences BtBs, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
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12
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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13
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Narkhede YB, Bhardwaj A, Motsa BB, Saxena R, Sharma T, Chapagain PP, Stahelin RV, Wiest O. Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches. J Phys Chem B 2023; 127:6449-6461. [PMID: 37458567 DOI: 10.1021/acs.jpcb.3c01759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.
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Affiliation(s)
- Yogesh B Narkhede
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Atul Bhardwaj
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Balindile B Motsa
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Roopashi Saxena
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | | | | | - Robert V Stahelin
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
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14
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Yang Z, Ye Z, Qiu J, Feng R, Li D, Hsieh C, Allcock J, Zhang S. A mutation-induced drug resistance database (MdrDB). Commun Chem 2023; 6:123. [PMID: 37316673 DOI: 10.1038/s42004-023-00920-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/02/2023] [Indexed: 06/16/2023] Open
Abstract
Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.
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Affiliation(s)
- Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Jiezhong Qiu
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Rongjun Feng
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Danyu Li
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Changyu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | | | - Shengyu Zhang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China.
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15
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Clayton J, de Oliveira VM, Ibrahim MF, Sun X, Mahinthichaichan P, Shen M, Hilgenfeld R, Shen J. Integrative Approach to Dissect the Drug Resistance Mechanism of the H172Y Mutation of SARS-CoV-2 Main Protease. J Chem Inf Model 2023; 63:3521-3533. [PMID: 37199464 PMCID: PMC10237302 DOI: 10.1021/acs.jcim.3c00344] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Indexed: 05/19/2023]
Abstract
Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.
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Affiliation(s)
- Joseph Clayton
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Vinicius Martins de Oliveira
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | | | - Xinyuanyuan Sun
- Institute of Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
| | - Paween Mahinthichaichan
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Mingzhe Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
| | - Rolf Hilgenfeld
- Institute for Molecular Medicine, University of Lübeck, Lübeck 23562, Germany
- German Center for Infection Research (DZIF), Hamburg – Lübeck – Borstel – Riems Site, University of Lübeck, Lübeck 23562, Germany
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, USA
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16
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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17
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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. J Cheminform 2023; 15:31. [PMID: 36864534 PMCID: PMC9983232 DOI: 10.1186/s13321-023-00701-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/17/2023] [Indexed: 03/04/2023] Open
Abstract
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.
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18
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Jia C, Shi L, Li Y, Tian Y, Liu S, Wang S, Liao X, Wu H, Wang Z, Sun J, Zhang D, Zhu M, Ni Y, Wang J. "Potential Scalpel": A Bioassisted Ultrafast Staining Lateral Flow Immunoassay from De Novo to Results. Anal Chem 2023; 95:4095-4103. [PMID: 36780295 DOI: 10.1021/acs.analchem.2c04878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
It is of great importance to overcome potential incompatibility problems between dyestuffs and antibodies (mAbs) for extensive commercial application of a dyestuff-chemistry-based ultrafast colorimetric lateral flow immunoassay (cLFIA). Herein, inspired by traditional staining technologies, a basic dyestuff gallocyanine (GC)-assisted biogenic "potential scalpel"-based cLFIA (GC-ABPS-based cLFIA) by employing clenbuterol (CLE) as proof-of-concept was proposed to solve a high degree of incompatibility between the same potential dyestuffs and mAbs. Goat antimouse immunoglobulin (Ab2) could serve as the "potential scalpel" to form the positive potential value biomolecular network self-assemblers (BNSA) with anti-CLE mAbs (AbCLE) by noncovalent force. The cLFIA completed the entire detection process from de novo to detection results within 30 min thanks to the easy availability and ideal marking efficiency (≤1 min, saving 0.4-10 h) of GC. Encouragingly, the proposed ultrafast GC-ABPS-based cLFIA has also exhibited high sensitivity (0.411 ng mL-1) and low cost (300 times) compared with other cLFIAs. Also, the feasibility of the proposed cLFIA was demonstrated by detecting CLE in beef, pork ham, and skim milk. Finally, the proposed GC-ABPS-based cLFIA has broadened the application range of dyestuffs and provided an effective reference strategy for the application of dyestuffs in food safety monitoring.
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Affiliation(s)
- Conghui Jia
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Longhua Shi
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Yuechun Li
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Yanli Tian
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Sijie Liu
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Shaochi Wang
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Xingrui Liao
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Haofen Wu
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Ziqi Wang
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Jing Sun
- Qinghai Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, 23 Xinning Road, Xining 810008, Qinghai, China
| | - Daohong Zhang
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
| | - Mingqiang Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
| | - Yongsheng Ni
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230601, China
| | - Jianlong Wang
- College of Food Science and Engineering, Northwest A&F University, 22 Xinong Road, Yangling 712100, Shaanxi, China
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19
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Breznik M, Ge Y, Bluck JP, Briem H, Hahn DF, Christ CD, Mortier J, Mobley DL, Meier K. Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods. ChemMedChem 2023; 18:e202200425. [PMID: 36240514 PMCID: PMC9868080 DOI: 10.1002/cmdc.202200425] [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: 08/01/2022] [Revised: 10/10/2022] [Indexed: 01/26/2023]
Abstract
Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.
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Affiliation(s)
- Marko Breznik
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA
| | - Joseph P. Bluck
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Hans Briem
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Clara D. Christ
- Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - Jérémie Mortier
- Computational Molecular Design, Pharmaceuticals, R&D, Bayer AG, 13342 Berlin, Germany
| | - David L. Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, CA 92697, USA,Department of Chemistry, University of California, Irvine, CA 92697, USA
| | - Katharina Meier
- Computational Life Science Technology Functions, Crop Science, R&D, Bayer AG, 40789 Monheim, Germany
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20
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Burgin T, Pfaendtner J, Beck DAC. Quick and Accurate Estimates of Mutation Effects on Transition-State Stabilization of Enzymes from Molecular Simulations with Restrained Transition States. J Phys Chem B 2022; 126:9964-9970. [PMID: 36413982 DOI: 10.1021/acs.jpcb.2c04802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible.
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Affiliation(s)
- Tucker Burgin
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - David A C Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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21
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Miceli M, Deriu MA, Grasso G. Toward the design and development of peptidomimetic inhibitors of the Ataxin-1 aggregation pathway. Biophys J 2022; 121:4679-4688. [PMID: 36262042 PMCID: PMC9748251 DOI: 10.1016/j.bpj.2022.10.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 07/09/2022] [Accepted: 10/17/2022] [Indexed: 12/15/2022] Open
Abstract
Spinocerebellar ataxia type 1 is a degenerative disorder caused by polyglutamine expansions and aggregation of Ataxin-1. The interaction between Capicua (CIC) and the AXH domain of Ataxin-1 protein has been suggested as a possible driver of aggregation for the expanded Ataxin-1 protein and the subsequent onset of spinocerebellar ataxia 1. Experimental studies have demonstrated that short constructs of CIC may prevent such aggregation and suggested this as a possible candidate to inspire the rational design of peptidomimetics. In this work, molecular modeling techniques, namely the alchemical mutation and force field-based molecular dynamics, have been employed to propose a pipeline for the rational design of a CIC-inspired inhibitor of the ataxin-1 aggregation pathway. In particular, this study has shown that the alchemical mutation can estimate the affinity between AXH and CIC with good correlation with experimental data, while molecular dynamics shed light on molecular mechanisms that occur for stabilization of the interaction between the CIC-inspired construct and the AXH domain of Ataxin-1. This work lays the foundation for a rational methodology for the in silico screening and design of peptidomimetics, which can expedite and streamline experimental studies to identify strategies for inhibiting the ataxin-1 aggregation pathway.
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Affiliation(s)
- Marcello Miceli
- PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Marco A Deriu
- PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence Research, Scuola Universitaria Professionale della Svizzera Italiana, Lugano-Viganello, Switzerland.
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22
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Cui L, Cui A, Li Q, Yang L, Liu H, Shao W, Feng Y. Molecular Evolution of an Aminotransferase Based on Substrate–Enzyme Binding Energy Analysis for Efficient Valienamine Synthesis. ACS Catal 2022. [DOI: 10.1021/acscatal.2c03784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Li Cui
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Anqi Cui
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qitong Li
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lezhou Yang
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hao Liu
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, and Joint International Research Laboratory of Metabolic & Developmental Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
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23
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Silva AF, Guest EE, Falcone BN, Pickett SD, Rogers DM, Hirst JD. Free energy perturbation calculations of tetrahydroquinolines complexed to the first bromodomain of BRD4. Mol Phys 2022. [DOI: 10.1080/00268976.2022.2124201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
| | - Ellen E. Guest
- School of Chemistry, University of Nottingham, Nottingham, UK
| | | | - Stephen D. Pickett
- GlaxoSmithKline R&D Pharmaceuticals, Computational Chemistry, Stevenage, UK
| | - David M. Rogers
- School of Chemistry, University of Nottingham, Nottingham, UK
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24
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Brankin AE, Fowler PW. Predicting antibiotic resistance in complex protein targets using alchemical free energy methods. J Comput Chem 2022; 43:1771-1782. [PMID: 36054249 PMCID: PMC9545121 DOI: 10.1002/jcc.26979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022]
Abstract
Drug resistant Mycobacterium tuberculosis, which mostly results from single nucleotide polymorphisms in antibiotic target genes, poses a major threat to tuberculosis treatment outcomes. Relative binding free energy (RBFE) calculations can rapidly predict the effects of mutations, but this approach has not been tested on large, complex proteins. We use RBFE calculations to predict the effects of M. tuberculosis RNA polymerase and DNA gyrase mutations on rifampicin and moxifloxacin susceptibility respectively. These mutations encompass a range of amino acid substitutions with known effects and include large steric perturbations and charged moieties. We find that moderate numbers (n = 3-15) of short RBFE calculations can predict resistance in cases where the mutation results in a large change in the binding free energy. We show that the method lacks discrimination in cases with either a small change in energy or that involve charged amino acids, and we investigate how these calculation errors may be decreased.
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Affiliation(s)
- Alice E. Brankin
- Nuffield Department of Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Philip W. Fowler
- Nuffield Department of Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
- National Institute of Health Research Oxford Biomedical Research CentreJohn Radcliffe HospitalOxfordUK
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25
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Flores-León CD, Dominguez L, Aguayo-Ortiz R. Molecular basis of Toxoplasma gondii oryzalin resistance from a novel α-tubulin binding site model. Arch Biochem Biophys 2022; 730:109398. [PMID: 36116504 DOI: 10.1016/j.abb.2022.109398] [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: 08/18/2022] [Accepted: 09/12/2022] [Indexed: 11/18/2022]
Abstract
Oryzalin (ORY) is a dinitroaniline derivative that inhibits the microtubule polymerization in plants and parasitic protozoa by selectively binding to the α-tubulin subunit. This herbicidal agent exhibits good antiprotozoal activity against major human parasites, such as Toxoplasma gondii (toxoplasmosis), Leishmania mexicana (leishmaniasis), and Plasmodium falciparum (malaria). Previous chemical mutagenesis assays on T. gondii α-tubulin (TgAT) have identified key mutations that lead to ORY resistance. Herein, we employed alchemical free energy methods and molecular dynamics simulations to determine if the ORY resistance mutations either decrease the TgAT's affinity of the compound or increase the protein stability. Our results here suggest that L136F and V202F mutations significantly decrease the affinity of ORY to TgAT, while T239I and V252L mutations diminish TgAT's flexibility. On the other hand, protein stability predictors determined that R243S mutation reduces TgAT stability due to the loss of its salt bridge interaction with E27. Interestingly, molecular dynamics simulations confirm that the loss of this key interaction leads to ORY binding site closure. Our study provides a better insight into the TgAT-ORY interaction, further supporting our recently proposed ORY-binding site.
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Affiliation(s)
- Carlos D Flores-León
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Laura Dominguez
- Departamento de Fisicoquímica, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico.
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26
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Yu Y, Wang Z, Wang L, Tian S, Hou T, Sun H. Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses. J Cheminform 2022; 14:56. [PMID: 35987841 PMCID: PMC9392442 DOI: 10.1186/s13321-022-00639-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/08/2022] [Indexed: 12/04/2022] Open
Abstract
Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.
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27
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da Silva GM, Yang J, Leang B, Huang J, Weinreich DM, Rubenstein BM. Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase. PLoS Comput Biol 2022; 18:e1009944. [PMID: 35759512 PMCID: PMC9269908 DOI: 10.1371/journal.pcbi.1009944] [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: 02/22/2022] [Revised: 07/08/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon’s importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds’ negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.
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Affiliation(s)
- Gabriel Monteiro da Silva
- Department of Molecular and Cell Biology, Brown University, Providence, Rhode Island, United States of America
- * E-mail:
| | - Jordan Yang
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
| | - Bunlong Leang
- Department of Health and Human Biology, Brown University, Providence, Rhode Island, United States of America
| | - Jessie Huang
- Department of Chemistry, Wellesley College, Wellesley, Massachusetts, United States of America
| | - Daniel M. Weinreich
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
| | - Brenda M. Rubenstein
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
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28
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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29
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Wieczór M, Genna V, Aranda J, Badia RM, Gelpí JL, Gapsys V, de Groot BL, Lindahl E, Municoy M, Hospital A, Orozco M. Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2022; 13:e1622. [PMID: 35935573 PMCID: PMC9347456 DOI: 10.1002/wcms.1622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 06/15/2023]
Abstract
Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.
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Affiliation(s)
- Miłosz Wieczór
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Physical ChemistryGdansk University of TechnologyGdańskPoland
| | - Vito Genna
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Juan Aranda
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | | | - Josep Lluís Gelpí
- Barcelona Supercomputing CenterBarcelonaSpain
- Department of Biochemistry and BiomedicineUniversity of BarcelonaBarcelonaSpain
| | - Vytautas Gapsys
- Max Planck Institute for Multidisciplinary SciencesComputational Biomolecular Dynamics GroupGoettingenGermany
| | - Bert L. de Groot
- Max Planck Institute for Multidisciplinary SciencesComputational Biomolecular Dynamics GroupGoettingenGermany
| | - Erik Lindahl
- Department of Applied PhysicsSwedish e‐Science Research Center, KTH Royal Institute of TechnologyStockholmSweden
- Department of Biochemistry and Biophysics, Science for Life LaboratoryStockholm UniversityStockholmSweden
| | | | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Department of Biochemistry and BiomedicineUniversity of BarcelonaBarcelonaSpain
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30
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Wang Z, Pan H, Sun H, Kang Y, Liu H, Cao D, Hou T. fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation. Brief Bioinform 2022; 23:6587180. [PMID: 35580866 DOI: 10.1093/bib/bbac201] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 01/12/2023] Open
Abstract
Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.
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Affiliation(s)
- Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hong Pan
- Day Surgery Center, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, SAR, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058, China
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31
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Schöller A, Kearns F, Woodcock HL, Boresch S. Optimizing the Calculation of Free Energy Differences in Nonequilibrium Work SQM/MM Switching Simulations. J Phys Chem B 2022; 126:2798-2811. [PMID: 35404610 PMCID: PMC9036525 DOI: 10.1021/acs.jpcb.2c00696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/24/2022] [Indexed: 11/27/2022]
Abstract
A key step during indirect alchemical free energy simulations using quantum mechanical/molecular mechanical (QM/MM) hybrid potential energy functions is the calculation of the free energy difference ΔAlow→high between the low level (e.g., pure MM) and the high level of theory (QM/MM). A reliable approach uses nonequilibrium work (NEW) switching simulations in combination with Jarzynski's equation; however, it is computationally expensive. In this study, we investigate whether it is more efficient to use more shorter switches or fewer but longer switches. We compare results obtained with various protocols to reference free energy differences calculated with Crooks' equation. The central finding is that fewer longer switches give better converged results. As few as 200 sufficiently long switches lead to ΔAlow→high values in good agreement with the reference results. This optimized protocol reduces the computational cost by a factor of 40 compared to earlier work. We also describe two tools/ways of analyzing the raw data to detect sources of poor convergence. Specifically, we find it helpful to analyze the raw data (work values from the NEW switching simulations) in a quasi-time series-like manner. Principal component analysis helps to detect cases where one or more conformational degrees of freedom are different at the low and high level of theory.
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Affiliation(s)
- Andreas Schöller
- Faculty
of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Währingerstrasse 42, A-1090 Vienna, Austria
| | - Fiona Kearns
- Department
of Chemistry, University of South Florida, 4202 E. Fowler Avenue, CHE205, Tampa, Florida 33620-5250, United States
| | - H. Lee Woodcock
- Department
of Chemistry, University of South Florida, 4202 E. Fowler Avenue, CHE205, Tampa, Florida 33620-5250, United States
| | - Stefan Boresch
- Faculty
of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria
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32
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Mohr B, Shmilovich K, Kleinwächter IS, Schneider D, Ferguson AL, Bereau T. Data-driven discovery of cardiolipin-selective small molecules by computational active learning. Chem Sci 2022; 13:4498-4511. [PMID: 35656132 PMCID: PMC9019913 DOI: 10.1039/d2sc00116k] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/24/2022] [Indexed: 12/23/2022] Open
Abstract
Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.
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Affiliation(s)
- Bernadette Mohr
- Van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam Amsterdam 1098 XH The Netherlands
| | - Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA
| | - Isabel S Kleinwächter
- Department of Chemistry - Biochemistry, Johannes Gutenberg University Mainz 55128 Mainz Germany
| | - Dirk Schneider
- Department of Chemistry - Biochemistry, Johannes Gutenberg University Mainz 55128 Mainz Germany
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA
| | - Tristan Bereau
- Van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam Amsterdam 1098 XH The Netherlands .,Max Planck Institute for Polymer Research 55128 Mainz Germany
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33
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Probing the ligand-binding pocket of recombinant β-lactoglobulin: Calorimetric and spectroscopic studies. Biophys Chem 2022; 283:106770. [DOI: 10.1016/j.bpc.2022.106770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 11/23/2022]
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34
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Wu Z, Biggin PC. Correction Schemes for Absolute Binding Free Energies Involving Lipid Bilayers. J Chem Theory Comput 2022; 18:2657-2672. [PMID: 35315270 PMCID: PMC9082507 DOI: 10.1021/acs.jctc.1c01251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Absolute
binding free-energy (ABFE) calculations are playing an
increasing role in drug design, especially as they can be performed
on a range of disparate compounds and direct comparisons between them
can be made. It is, however, especially important to ensure that they
are as accurate as possible, as unlike relative binding free-energy
(RBFE) calculations, one does not benefit as much from a cancellation
of errors during the calculations. In most modern implementations
of ABFE calculations, a particle mesh Ewald scheme is typically used
to treat the electrostatic contribution to the free energy. A central
requirement of such schemes is that the box preserves neutrality throughout
the calculation. There are many ways to deal with this problem that
have been discussed over the years ranging from a neutralizing plasma
with a post hoc correction term through to a simple co-alchemical
ion within the same box. The post hoc correction approach is the most
widespread. However, the vast majority of these studies have been
applied to a soluble protein in a homogeneous solvent (water or salt
solution). In this work, we explore which of the more common approaches
would be the most suitable for a simulation box with a lipid bilayer
within it. We further develop the idea of the so-called Rocklin correction
for lipid-bilayer systems and show how such a correction could work.
However, we also show that it will be difficult to make this generalizable
in a practical way and thus we conclude that the use of a “co-alchemical
ion” is the most useful approach for simulations involving
lipid membrane systems.
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Affiliation(s)
- Zhiyi Wu
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
| | - Philip C Biggin
- Department of Biochemistry, South Parks Road, Oxford OX1 3QU, U.K
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35
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Yang ZY, Ye ZF, Xiao YJ, Hsieh CY, Zhang SY. SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance. Brief Bioinform 2022; 23:6543900. [PMID: 35262669 DOI: 10.1093/bib/bbac050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/17/2022] [Accepted: 01/31/2022] [Indexed: 12/25/2022] Open
Abstract
Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.
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Affiliation(s)
- Zi-Yi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Zhao-Feng Ye
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Yi-Jia Xiao
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China.,Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Sheng-Yu Zhang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
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36
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Gapsys V, Hahn DF, Tresadern G, Mobley DL, Rampp M, de Groot BL. Pre-Exascale Computing of Protein-Ligand Binding Free Energies with Open Source Software for Drug Design. J Chem Inf Model 2022; 62:1172-1177. [PMID: 35191702 PMCID: PMC8924919 DOI: 10.1021/acs.jcim.1c01445] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Nowadays, drug design projects benefit from highly accurate protein-ligand binding free energy predictions based on molecular dynamics simulations. While such calculations have been computationally expensive in the past, we now demonstrate that workflows built on open source software packages can efficiently leverage pre-exascale computing resources to screen hundreds of compounds in a matter of days. We report our results of free energy calculations on a large set of pharmaceutically relevant targets assembled to reflect industrial drug discovery projects.
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Affiliation(s)
- Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
| | - David F Hahn
- Computational Chemistry, Janssen Research and Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research and Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340 Beerse, Belgium
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - Markus Rampp
- Max-Planck Computing and Data Facility, Giessenbachstrasse 2, 85748 Garching, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
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37
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Zara L, Efrém NL, van Muijlwijk-Koezen JE, de Esch IJP, Zarzycka B. Progress in Free Energy Perturbation: Options for Evolving Fragments. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 40:36-42. [PMID: 34916020 DOI: 10.1016/j.ddtec.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 01/18/2023]
Abstract
One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.
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Affiliation(s)
- Lorena Zara
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Nina-Louisa Efrém
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Jacqueline E van Muijlwijk-Koezen
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Barbara Zarzycka
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands..
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Sun T, Chen Y, Wen Y, Zhu Z, Li M. PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions. Commun Biol 2021; 4:1311. [PMID: 34799678 PMCID: PMC8604987 DOI: 10.1038/s42003-021-02826-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/26/2021] [Indexed: 02/07/2023] Open
Abstract
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning. Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.
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Affiliation(s)
- Tingting Sun
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuhao Wen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Zefeng Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China.
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Blaber S, Louwerse MD, Sivak DA. Steps minimize dissipation in rapidly driven stochastic systems. Phys Rev E 2021; 104:L022101. [PMID: 34525515 DOI: 10.1103/physreve.104.l022101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/09/2021] [Indexed: 02/04/2023]
Abstract
Micro- and nanoscale systems driven by rapid changes in control parameters (control protocols) dissipate significant energy. In the fast-protocol limit, we find that protocols that minimize dissipation at fixed duration are universally given by a two-step process, jumping to and from a point that balances jump size with fast relaxation. Jump protocols could be exploited by molecular machines or thermodynamic computing to improve energetic efficiency, and implemented in nonequilibrium free-energy estimation to improve accuracy.
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Affiliation(s)
- Steven Blaber
- Department of Physics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
| | - Miranda D Louwerse
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
| | - David A Sivak
- Department of Physics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
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40
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King E, Aitchison E, Li H, Luo R. Recent Developments in Free Energy Calculations for Drug Discovery. Front Mol Biosci 2021; 8:712085. [PMID: 34458321 PMCID: PMC8387144 DOI: 10.3389/fmolb.2021.712085] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/27/2021] [Indexed: 01/11/2023] Open
Abstract
The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.
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Affiliation(s)
- Edward King
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
| | - Erick Aitchison
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
| | - Han Li
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, United States
| | - Ray Luo
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, United States
- Department of Materials Science and Engineering, University of California, Irvine, CA, United States
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
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41
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Scherer M, Fleishman SJ, Jones PR, Dandekar T, Bencurova E. Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals. Front Bioeng Biotechnol 2021; 9:673005. [PMID: 34211966 PMCID: PMC8239229 DOI: 10.3389/fbioe.2021.673005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/06/2021] [Indexed: 11/13/2022] Open
Abstract
To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
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Affiliation(s)
- Marc Scherer
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Patrik R Jones
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Thomas Dandekar
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, Julius-Maximilians University of Würzburg, Würzburg, Germany
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42
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Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches. Commun Chem 2021; 4:61. [PMID: 36697634 PMCID: PMC9814727 DOI: 10.1038/s42004-021-00498-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.
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43
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Xue T, Wu W, Guo N, Wu C, Huang J, Lai L, Liu H, Li Y, Wang T, Wang Y. Single point mutations can potentially enhance infectivity of SARS-CoV-2 revealed by in silico affinity maturation and SPR assay. RSC Adv 2021; 11:14737-14745. [PMID: 35423963 PMCID: PMC8697837 DOI: 10.1039/d1ra00426c] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/11/2021] [Indexed: 02/05/2023] Open
Abstract
The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to the cell receptor and affect the potency of vaccines and antibodies. Here we used an in silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay results show that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated the in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit the development of broadly neutralizing vaccine and antibody.
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Affiliation(s)
- Ting Xue
- Targeted Tracer Research and Development Laboratory, Precision Medicine Research Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University Chengdu 610041 P. R. China
| | - Weikun Wu
- XtalPi AI Research Center 7F, Tower B, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District Beijing 100083 P. R. China
| | - Ning Guo
- XtalPi AI Research Center 7F, Tower B, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District Beijing 100083 P. R. China
| | - Chengyong Wu
- Targeted Tracer Research and Development Laboratory, Precision Medicine Research Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University Chengdu 610041 P. R. China
| | - Jian Huang
- XtalPi AI Research Center 7F, Tower B, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District Beijing 100083 P. R. China
| | - Lipeng Lai
- XtalPi AI Research Center 7F, Tower B, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District Beijing 100083 P. R. China
| | - Hong Liu
- Targeted Tracer Research and Development Laboratory, Precision Medicine Research Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University Chengdu 610041 P. R. China
| | - Yalun Li
- Targeted Tracer Research and Development Laboratory, Precision Medicine Research Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University Chengdu 610041 P. R. China
| | - Tianyuan Wang
- XtalPi AI Research Center 7F, Tower B, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District Beijing 100083 P. R. China
| | - Yuxi Wang
- Targeted Tracer Research and Development Laboratory, Precision Medicine Research Center, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University Chengdu 610041 P. R. China
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Yang P, Cao R, Bao H, Wu X, Yang L, Zhu D, Zhang L, Peng L, Cai Y, Zhang W, Shao Y. Identification of Novel Alectinib-Resistant ALK Mutation G1202K with Sensitization to Lorlatinib: A Case Report and in silico Structural Modelling. Onco Targets Ther 2021; 14:2131-2138. [PMID: 33790576 PMCID: PMC8007639 DOI: 10.2147/ott.s293901] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/25/2021] [Indexed: 11/23/2022] Open
Abstract
Background Drug resistance caused by G1202R/G1202del mutation in anaplastic lymphoma kinase (ALK) represents a great challenge in the clinic. The effect of other mutation(s) at G1202 on the available tyrosine kinase inhibitors (TKIs) in the clinic remains unknown. Case Presentation A 50-year-old Chinese male non-smoker with lung adenocarcinoma progressed with spinal metastasis after receiving chest radiation together with Pemetrexed and Cisplatin as adjuvant chemotherapy. Targeted next generation sequencing (NGS) identified EML4-ALK gene fusion in the resected left lung tissue. Local radiation followed by Crizotinib were used in the following treatment and the spinal metastasis was found to shrink, but the progression free survival (PFS) only lasted for 2 months with the appearance of brain metastasis. Afterwards, the patient benefited from the therapy of Alectinib with a PFS of 8 months. Then he progressed with metastases in right lung and pleural, and did not show response to the chemotherapy with Docetaxel plus Bevacizumab. The targeted sequencing consistently identified EML4-ALK gene fusion in both plasma and pleural effusion (PE), as well as a novel ALK G1202K mutation (c.3604_3605delGGinsAA). Given the lack of established or known drug treatment for this novel mutation, we implemented molecular dynamics (MD) simulation-guided drug sensitivity prediction, which results suggested Lorlatinib remains potent against G1202K mutant ALK. Therefore, Lorlatinib was used as the fourth-line therapy, which lead to the considerable efficacy with improved performance status (PS) score and reduced lung metastases. The structural mechanism underlying G1202K-induced drug resistance to different ALK-TKIs was also discussed. Conclusion Our case suggested the ALK-G1202K mutation may serve as a novel mechanism underlying the resistance to Alectinib, and provide direct evidence to support its sensitization to Lorlatinib. Our work represented an example of integrating in silico predictions into clinical practice.
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Affiliation(s)
- Ping Yang
- Department of Radiation Oncology, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Ran Cao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Hua Bao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Xue Wu
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Lingling Yang
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Dongqin Zhu
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Lu Zhang
- Department of Medical Oncology, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Liming Peng
- Department of Respiratory Medicine, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Yuefei Cai
- Department of Intervention, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Weijun Zhang
- Departments of Radiation Oncology, Cancer Center of Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Yang Shao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China.,School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
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Planas-Iglesias J, Marques SM, Pinto GP, Musil M, Stourac J, Damborsky J, Bednar D. Computational design of enzymes for biotechnological applications. Biotechnol Adv 2021; 47:107696. [PMID: 33513434 DOI: 10.1016/j.biotechadv.2021.107696] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/14/2022]
Abstract
Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.
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Affiliation(s)
- Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Sérgio M Marques
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic; IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic.
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic.
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Erguven M, Karakulak T, Diril MK, Karaca E. How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations? ACS OMEGA 2021; 6:1254-1265. [PMID: 33490784 PMCID: PMC7818309 DOI: 10.1021/acsomega.0c04672] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/11/2020] [Indexed: 06/12/2023]
Abstract
In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, K d, and K i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K i dataset. When homology models are used instead of K i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).
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Affiliation(s)
- Mehmet Erguven
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Tülay Karakulak
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - M. Kasim Diril
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Ezgi Karaca
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
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47
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Blaber S, Sivak DA. Skewed thermodynamic geometry and optimal free energy estimation. J Chem Phys 2020; 153:244119. [PMID: 33380076 DOI: 10.1063/5.0033405] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Free energy differences are a central quantity of interest in physics, chemistry, and biology. We develop design principles that improve the precision and accuracy of free energy estimators, which have potential applications to screening for targeted drug discovery. Specifically, by exploiting the connection between the work statistics of time-reversed protocol pairs, we develop near-equilibrium approximations for moments of the excess work and analyze the dominant contributions to the precision and accuracy of standard nonequilibrium free-energy estimators. Within linear response, minimum-dissipation protocols follow the geodesics of the Riemannian metric induced by the Stokes friction tensor. We find that the next-order contribution arises from the rank-3 supra-Stokes tensor that skews the geometric structure such that minimum-dissipation protocols follow the geodesics of a generalized cubic Finsler metric. Thus, near equilibrium, the supra-Stokes tensor determines the leading-order contribution to the bias of bidirectional free-energy estimators.
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Affiliation(s)
- Steven Blaber
- Department of Physics, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - David A Sivak
- Department of Physics, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
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48
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Khalak Y, Tresadern G, de Groot BL, Gapsys V. Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software. J Comput Aided Mol Des 2020; 35:49-61. [PMID: 33230742 PMCID: PMC7862541 DOI: 10.1007/s10822-020-00359-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/07/2020] [Indexed: 11/24/2022]
Abstract
In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of \documentclass[12pt]{minimal}
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\begin{document}$${1.38}\,\hbox {kcal}/\hbox {mol}$$\end{document}1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations.
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Affiliation(s)
- Yuriy Khalak
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077, Göttingen, Germany.
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49
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Procacci P. Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics. Curr Opin Struct Biol 2020; 67:127-134. [PMID: 33220532 DOI: 10.1016/j.sbi.2020.08.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 08/02/2020] [Accepted: 08/02/2020] [Indexed: 12/13/2022]
Abstract
Computational approaches are becoming an essential tool in modern drug design and discovery, with fast compound triaging using a combination of machine learning and docking techniques followed by molecular dynamics binding free energies assessment using alchemical techniques. The traditional MD-based alchemical free energy perturbation (FEP) method faces severe sampling issues that may limits its reliability in automated workflows. Here we review the major sources of uncertainty in FEP protocols for drug discovery, showing how the sampling problem can be effectively tackled by switching to nonequilibrium alchemical techniques.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, dVia della Lastruccia 3, 50019 Sesto Fiorentino, Italy.
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50
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Kashefolgheta S, Oliveira MP, Rieder SR, Horta BAC, Acree WE, Hünenberger PH. Evaluating Classical Force Fields against Experimental Cross-Solvation Free Energies. J Chem Theory Comput 2020; 16:7556-7580. [DOI: 10.1021/acs.jctc.0c00688] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Sadra Kashefolgheta
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Marina P. Oliveira
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Salomé R. Rieder
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
| | - Bruno A. C. Horta
- Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, Brazil
| | - William E. Acree
- Department of Chemistry, University of North Texas, 1155 Union Circle Drive #305070, Denton, Texas 76203, United States
| | - Philippe H. Hünenberger
- Laboratorium für Physikalische Chemie, ETH Zürich, ETH-Hönggerberg, HCI, CH-8093 Zürich, Switzerland
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