1
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Srinivasan S, Álvarez D, John Peter AT, Vanni S. Unbiased MD simulations identify lipid binding sites in lipid transfer proteins. J Cell Biol 2024; 223:e202312055. [PMID: 39105757 PMCID: PMC11303870 DOI: 10.1083/jcb.202312055] [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: 12/13/2023] [Revised: 05/29/2024] [Accepted: 07/16/2024] [Indexed: 08/07/2024] Open
Abstract
The characterization of lipid binding to lipid transfer proteins (LTPs) is fundamental to understand their molecular mechanism. However, several structures of LTPs, and notably those proposed to act as bridges between membranes, do not provide the precise location of their endogenous lipid ligands. To address this limitation, computational approaches are a powerful alternative methodology, but they are often limited by the high flexibility of lipid substrates. Here, we develop a protocol based on unbiased coarse-grain molecular dynamics simulations in which lipids placed away from the protein can spontaneously bind to LTPs. This approach accurately determines binding pockets in LTPs and provides a working hypothesis for the lipid entry pathway. We apply this approach to characterize lipid binding to bridge LTPs of the Vps13-Atg2 family, for which the lipid localization inside the protein is currently unknown. Overall, our work paves the way to determine binding pockets and entry pathways for several LTPs in an inexpensive, fast, and accurate manner.
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Affiliation(s)
| | - Daniel Álvarez
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Departamento de Química Física y Analítica, Universidad de Oviedo, Oviedo, España
| | - Arun T John Peter
- Department of Biology, University of Fribourg, Fribourg, Switzerland
| | - Stefano Vanni
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss National Center for Competence in Research Bio-inspired Materials, University of Fribourg , Fribourg, Switzerland
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2
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Hong Y, Ha J, Sim J, Lim CJ, Oh KS, Chandrasekaran R, Kim B, Choi J, Ko J, Shin WH, Lee J. Accurate prediction of protein-ligand interactions by combining physical energy functions and graph-neural networks. J Cheminform 2024; 16:121. [PMID: 39497201 PMCID: PMC11536843 DOI: 10.1186/s13321-024-00912-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 10/07/2024] [Indexed: 11/07/2024] Open
Abstract
We introduce an advanced model for predicting protein-ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein-ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein-ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein-ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model's efficiency and generalizability. The model's efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein-ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.
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Affiliation(s)
- Yiyu Hong
- Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea
| | - Junsu Ha
- Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea
| | - Jaemin Sim
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Chae Jo Lim
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea
| | - Kwang-Seok Oh
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea
| | | | - Bomin Kim
- College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jieun Choi
- College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea
| | - Junsu Ko
- Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea.
| | - Woong-Hee Shin
- Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea.
- Department of Medicine, Korea University College of Medicine, Seoul, 02841, Republic of Korea.
| | - Juyong Lee
- Arontier Co., 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea.
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea.
- Research Institute of Pharmaceutical Science, College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea.
- College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea.
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3
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Wróbel TM, Bartuzi D, Kaczor AA. Secondary Binding Site of CYP17A1 in Enhanced Sampling Simulations. J Chem Inf Model 2024; 64:7679-7686. [PMID: 39325660 PMCID: PMC11480979 DOI: 10.1021/acs.jcim.4c01293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 09/06/2024] [Accepted: 09/17/2024] [Indexed: 09/28/2024]
Abstract
Androgens like testosterone and dihydrotestosterone play a key role in prostate cancer progression, making the enzyme CYP17A1, essential for androgen synthesis, a crucial therapeutic target. Recent studies have revealed electron density at the substrate entry channel, suggesting the presence of a secondary binding site. In this study, we calculated the binding free energy landscape of known ligands at this site using Funnel Metadynamics. Our results characterize this binding site and indicate that nonheme-interacting ligands could effectively bind to CYP17A1, providing a novel approach to the design of CYP17A1 inhibitors.
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Affiliation(s)
- Tomasz M. Wróbel
- Department
of Synthesis and Chemical Technology of Pharmaceutical Substances
with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., 20093 Lublin, Poland
- Department
of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
| | - Damian Bartuzi
- Department
of Synthesis and Chemical Technology of Pharmaceutical Substances
with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., 20093 Lublin, Poland
- Science
for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, 75124 Uppsala, Sweden
| | - Agnieszka A. Kaczor
- Department
of Synthesis and Chemical Technology of Pharmaceutical Substances
with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, 4A Chodźki St., 20093 Lublin, Poland
- School
of Pharmacy, University of Eastern Finland,
Yliopistonranta 1, P.O.
Box 1627, 70211 Kuopio, Finland
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4
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Qian R, Xue J, Xu Y, Huang J. Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery. J Chem Inf Model 2024; 64:7214-7237. [PMID: 39360948 DOI: 10.1021/acs.jcim.4c01024] [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: 10/15/2024]
Abstract
Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.
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Affiliation(s)
- Runtong Qian
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Xue
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - You Xu
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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5
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Karrenbrock M, Borsatto A, Rizzi V, Lukauskis D, Aureli S, Luigi Gervasio F. Absolute Binding Free Energies with OneOPES. J Phys Chem Lett 2024; 15:9871-9880. [PMID: 39302888 PMCID: PMC11457222 DOI: 10.1021/acs.jpclett.4c02352] [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: 08/09/2024] [Revised: 09/13/2024] [Accepted: 09/16/2024] [Indexed: 09/22/2024]
Abstract
The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.
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Affiliation(s)
- Maurice Karrenbrock
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, CH
- Swiss
Bioinformatics Institute, University of
Geneva, CH-1206 Geneva, CH
| | - Alberto Borsatto
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, CH
- Swiss
Bioinformatics Institute, University of
Geneva, CH-1206 Geneva, CH
| | - Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, CH
- Swiss
Bioinformatics Institute, University of
Geneva, CH-1206 Geneva, CH
| | - Dominykas Lukauskis
- Chemistry
Department, University College London (UCL), WC1E 6BT London, U.K.
| | - Simone Aureli
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, CH
- Swiss
Bioinformatics Institute, University of
Geneva, CH-1206 Geneva, CH
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel-Servet 1, CH-1206 Geneva, CH
- Institute
of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CH-1206 Geneva, CH
- Swiss
Bioinformatics Institute, University of
Geneva, CH-1206 Geneva, CH
- Chemistry
Department, University College London (UCL), WC1E 6BT London, U.K.
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6
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Li J, Liao L, Zhang C, Huang K, Zhang P, Zhang JZH, Wan X, Zhang H. Development and experimental validation of computational methods for human antibody affinity enhancement. Brief Bioinform 2024; 25:bbae488. [PMID: 39358035 PMCID: PMC11446602 DOI: 10.1093/bib/bbae488] [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: 06/11/2024] [Revised: 09/01/2024] [Accepted: 09/18/2024] [Indexed: 10/04/2024] Open
Abstract
High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.
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Affiliation(s)
- Junxin Li
- Center for Protein and Cell-based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
| | - Linbu Liao
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, United States
| | - Chao Zhang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
| | - Kaifang Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
- School of Chemistry and Molecular Engineering, East China Normal University, 3663 Zhongshan North Road, Putuo District, Shanghai 200062, China
| | - Pengfei Zhang
- Guangdong Key Laboratory of Nanomedicine, Shenzhen Engineering Laboratory of Nanomedicine and Nanoformulations, CAS Key Lab for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
| | - John Z H Zhang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
- Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518055, China
| | - Xiaochun Wan
- Center for Protein and Cell-based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
| | - Haiping Zhang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China
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7
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Vats S, Bobrovs R, Söderhjelm P, Bhakat S. AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics. PLoS One 2024; 19:e0307226. [PMID: 39190764 DOI: 10.1371/journal.pone.0307226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 07/02/2024] [Indexed: 08/29/2024] Open
Abstract
Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.
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Affiliation(s)
- Shray Vats
- Department of Computer Science, University of Texas at Austin, Austin, TX, United States of America
| | | | - Pär Söderhjelm
- Division of Biophysical Chemistry, Chemical Center, Lund University, Lund, Sweden
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8
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Borowska AM, Chiariello MG, Garaeva AA, Rheinberger J, Marrink SJ, Paulino C, Slotboom DJ. Structural basis of the obligatory exchange mode of human neutral amino acid transporter ASCT2. Nat Commun 2024; 15:6570. [PMID: 39095408 PMCID: PMC11297037 DOI: 10.1038/s41467-024-50888-8] [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: 11/16/2023] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
ASCT2 is an obligate exchanger of neutral amino acids, contributing to cellular amino acid homeostasis. ASCT2 belongs to the same family (SLC1) as Excitatory Amino Acid Transporters (EAATs) that concentrate glutamate in the cytosol. The mechanism that makes ASCT2 an exchanger rather than a concentrator remains enigmatic. Here, we employ cryo-electron microscopy and molecular dynamics simulations to elucidate the structural basis of the exchange mechanism of ASCT2. We establish that ASCT2 binds three Na+ ions per transported substrate and visits a state that likely acts as checkpoint in preventing Na+ ion leakage, both features shared with EAATs. However, in contrast to EAATs, ASCT2 retains one Na+ ion even under Na+-depleted conditions. We demonstrate that ASCT2 cannot undergo the structural transition in TM7 that is essential for the concentrative transport cycle of EAATs. This structural rigidity and the high-affinity Na+ binding site effectively confine ASCT2 to an exchange mode.
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Affiliation(s)
- Anna M Borowska
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
| | - Maria Gabriella Chiariello
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology Institute, Molecular Dynamics Group, University of Groningen, Groningen, the Netherlands
| | - Alisa A Garaeva
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
| | - Jan Rheinberger
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands
- Biochemistry Center Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Siewert J Marrink
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology Institute, Molecular Dynamics Group, University of Groningen, Groningen, the Netherlands
| | - Cristina Paulino
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands.
- Biochemistry Center Heidelberg, Heidelberg University, Heidelberg, Germany.
| | - Dirk J Slotboom
- Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Membrane Enzymology Group, University of Groningen, Groningen, the Netherlands.
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9
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Muscat S, Errico S, Danani A, Chiti F, Grasso G. Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols. J Chem Theory Comput 2024. [PMID: 38979909 PMCID: PMC11447954 DOI: 10.1021/acs.jctc.4c00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of the drug mechanism of action, pathogen identification, and novel antibiotic development. However, estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO), and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes, protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, in both their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via on-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterol entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently labeled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our understanding of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the computational methods deployed in this study further demonstrate considerable potential for investigating membrane binding processes.
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Affiliation(s)
- Stefano Muscat
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, Via la Santa 1 ,Lugano-Viganello 6962, Switzerland
| | - Silvia Errico
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence 50134, Italy
| | - Andrea Danani
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, Via la Santa 1 ,Lugano-Viganello 6962, Switzerland
| | - Fabrizio Chiti
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence 50134, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, Via la Santa 1 ,Lugano-Viganello 6962, Switzerland
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10
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Jiang P, Zhang C, Wang H, Li P, Du X, Wang Y, Lyukmanova E, Lin C, Wang X. Nicotine Enantioselectively Targets Myeloid Differentiation Protein 2 and Inhibits the Toll-like Receptor 4 Signaling. J Chem Inf Model 2024; 64:5253-5261. [PMID: 38973303 DOI: 10.1021/acs.jcim.4c00591] [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: 07/09/2024]
Abstract
Psychoactive substances, including morphine and methamphetamine, have been shown to interact with the classic innate immune receptor Toll-like receptor 4 (TLR4) and its partner protein myeloid differentiation protein 2 (MD2) in a nonenantioselective manner. (-)-Nicotine, the primary alkaloid in tobacco and a key component of highly addictive cigarettes, targets the TLR4/MD2, influencing TLR4 signaling pathways. Existing as two enantiomers, the stereoselective recognition of nicotine by TLR4/MD2 in the context of the innate immune response remains unclear. In this study, we synthesized (+)-nicotine and investigated its effects alongside (-)-nicotine on lipopolysaccharide (LPS)-induced TLR4 signaling. (-)-Nicotine dose-dependently inhibited proinflammatory factors such as tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), and cyclooxygenase-2 (COX-2). In contrast, (+)-nicotine showed no such inhibitory effects. Molecular dynamics simulations revealed that (-)-nicotine exhibited a stronger affinity with the TLR4 coreceptor MD2 than (+)-nicotine. Additionally, in silico simulations revealed that both nicotine enantiomers initially attach to the entrance of the MD2 cavity, creating a metastable state before they fully enter the cavity. In the metastable state, (-)-nicotine established more stable interactions with the surrounding residues at the entrance of the MD2 cavity compared to those of (+)-nicotine. This highlights the crucial role of the MD2 cavity entrance in the chiral recognition of nicotine. These findings provide valuable insights into the distinct interactions between nicotine enantiomers and the TLR4 coreceptor MD2, underscoring the enantioselective effect of nicotine on modulating TLR4 signaling.
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Affiliation(s)
- Pu Jiang
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
| | - Cong Zhang
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
| | - Hongshuang Wang
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
| | - Penghui Li
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
| | - Xiubo Du
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
| | - Yibo Wang
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
| | - Ekaterina Lyukmanova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 119997, Russia
- Biological Department, Shenzhen MSU-BIT University, Shenzhen 518172, China
| | - Cong Lin
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
| | - Xiaohui Wang
- Laboratory of Chemical Biology, , Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
- Beijing National Laboratory for Molecular Sciences, Beijing 100190, China
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11
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Gao J, Zhang C, Xu H, Zhang T, Wang H, Wang Y, Wang X. Dissecting the Role of the Hydroxyl Moiety at C14 in (+)-Opioid-Based TLR4 Antagonists via Wet-Lab Experiments and Molecular Dynamics Simulations. J Chem Inf Model 2024; 64:5273-5284. [PMID: 38921627 DOI: 10.1021/acs.jcim.4c00692] [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: 06/27/2024]
Abstract
Toll-like receptor 4 (TLR4) is pivotal as an innate immune receptor, playing a critical role in mediating neuropathic pain and drug addiction through its regulation of the neuroinflammatory response. The nonclassical (+)-opioid isomers represent a unique subset of TLR4 antagonists known for their effective blood-brain barrier permeability. Despite growing interest in the structure-activity relationship of these (+)-opioid-based TLR4 antagonists, the specific impact of heteroatoms on their TLR4 antagonistic activities has not been fully explored. This study investigated the influence of the hydroxyl group at C14 in six (+)-opioid TLR4 antagonists (1-6) using wet-lab experiments and in silico simulations. The corresponding C14-deoxy derivatives (7-12) were synthesized, and upon comparison with their corresponding counterparts (1-6), it was discovered that their TLR4 antagonistic activities were significantly diminished. Molecular dynamics simulations showed that the (+)-opioid TLR4 antagonists (1-6) possessed more negative binding free energies to the TLR4 coreceptor MD2, which was responsible for ligand recognition. This was primarily attributed to the formation of a hydrogen bond between the hydroxyl group at the C-14 position of the antagonists (1-6) and the R90 residue of MD2 during the binding process. Such an interaction facilitated the entry and subsequent binding of these molecules within the MD2 cavity. In contrast, the C14-deoxy derivatives (7-12), lacking the hydroxyl group at the C-14 position, missed this crucial hydrogen bond interaction with the R90 residue of MD2, leading to their egression from the MD2 cavity during simulations. This study underscores the significant role of the C14 hydroxyl moiety in enhancing the effectiveness of (+)-opioid TLR4 antagonists, which provides insightful guidance for designing future (+)-isomer opioid-derived TLR4 antagonists.
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Affiliation(s)
- Jingwei Gao
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Cong Zhang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Hangyu Xu
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
| | - Tianshu Zhang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Hongshuang Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
| | - Yibo Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
| | - Xiaohui Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, Anhui, China
- Beijing National Laboratory for Molecular Sciences, Beijing 100190, China
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12
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Gong FQ, Liu YP, Wang Y, E W, Tian ZQ, Cheng J. Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters. Angew Chem Int Ed Engl 2024; 63:e202405379. [PMID: 38639181 DOI: 10.1002/anie.202405379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 04/20/2024]
Abstract
Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.
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Affiliation(s)
- Fu-Qiang Gong
- College of Chemistry and Chemical Engineering, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surface, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Xiamen, 361005, China
| | - Yun-Pei Liu
- College of Chemistry and Chemical Engineering, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surface, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Xiamen, 361005, China
| | - Ye Wang
- College of Chemistry and Chemical Engineering, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surface, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Xiamen, 361005, China
| | - Weinan E
- School of Mathematical Sciences, Peking University, Center for Machine Learning Research, Beijing, 100084, China
- AI for Science Institute, Beijing, 100080, China
| | - Zhong-Qun Tian
- College of Chemistry and Chemical Engineering, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surface, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Xiamen, 361005, China
- Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory (IKKEM), Xiamen, 361005, China
| | - Jun Cheng
- College of Chemistry and Chemical Engineering, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surface, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Xiamen, 361005, China
- Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory (IKKEM), Xiamen, 361005, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, 361005, China
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13
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Nepravishta R, Ramírez-Cárdenas J, Rocha G, Walpole S, Hicks T, Monaco S, Muñoz-García JC, Angulo J. Fast Quantitative Validation of 3D Models of Low-Affinity Protein-Ligand Complexes by STD NMR Spectroscopy. J Med Chem 2024; 67:10025-10034. [PMID: 38848103 PMCID: PMC11215723 DOI: 10.1021/acs.jmedchem.4c00204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024]
Abstract
Low-affinity protein-ligand interactions are important for many biological processes, including cell communication, signal transduction, and immune responses. Structural characterization of these complexes is also critical for the development of new drugs through fragment-based drug discovery (FBDD), but it is challenging due to the low affinity of fragments for the binding site. Saturation transfer difference (STD) NMR spectroscopy has revolutionized the study of low-affinity receptor-ligand interactions enabling binding detection and structural characterization. Comparison of relaxation and exchange matrix calculations with 1H STD NMR experimental data is essential for the validation of 3D structures of protein-ligand complexes. In this work, we present a new approach based on the calculation of a reduced relaxation matrix, in combination with funnel metadynamics MD simulations, that allows a very fast generation of experimentally STD-NMR-validated 3D structures of low-affinity protein-ligand complexes.
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Affiliation(s)
- Ridvan Nepravishta
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
- Cancer Research Horizons, CRUK Scotland Institute, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, U.K
| | - Jonathan Ramírez-Cárdenas
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Gabriel Rocha
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Samuel Walpole
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Thomas Hicks
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Serena Monaco
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
| | - Juan C Muñoz-García
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
| | - Jesús Angulo
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, U.K
- Institute for Chemical Research (IIQ), CSIC - University of Seville, 49 Américo Vespucio, 41092 Seville, Spain
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14
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Bosio S, Bernetti M, Rocchia W, Masetti M. Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin. J Chem Inf Model 2024; 64:4570-4586. [PMID: 38800845 DOI: 10.1021/acs.jcim.4c00420] [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: 05/29/2024]
Abstract
It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.
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Affiliation(s)
- Stefano Bosio
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Mattia Bernetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Walter Rocchia
- Computational mOdelling of NanosCalE and bioPhysical sysTems (CONCEPT) Lab, Istituto Italiano di Tecnologia, Via Melen - 83, B Block, 16152 Genova, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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15
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Ghosh D, Biswas A, Radhakrishna M. Advanced computational approaches to understand protein aggregation. BIOPHYSICS REVIEWS 2024; 5:021302. [PMID: 38681860 PMCID: PMC11045254 DOI: 10.1063/5.0180691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.
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Affiliation(s)
- Deepshikha Ghosh
- Department of Biological Sciences and Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
| | - Anushka Biswas
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
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16
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Zhang H, Fan H, Wang J, Hou T, Saravanan KM, Xia W, Kan HW, Li J, Zhang JZH, Liang X, Chen Y. Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery. Brief Bioinform 2024; 25:bbae281. [PMID: 38864340 PMCID: PMC11167311 DOI: 10.1093/bib/bbae281] [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: 02/28/2024] [Revised: 05/05/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.
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Affiliation(s)
- Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Hongjie Fan
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
| | - Jixia Wang
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Tao Hou
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Agharam Road 173, Selaiyur, Chennai, Tamil Nadu 600073, India
| | - Wei Xia
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Hei Wun Kan
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - John Z H Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Xinmiao Liang
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Yang Chen
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
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17
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Marquardt AV, Farshad M, Whitmer JK. Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling. J Chem Theory Comput 2024; 20:3927-3934. [PMID: 38634733 DOI: 10.1021/acs.jctc.3c01186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Host-guest interactions are important to the design of pharmaceuticals and, more broadly, to soft materials as they can enable targeted, strong, and specific interactions between molecules. The binding process between the host and guest may be classified as a "rare event" when viewing the system at atomic scales, such as those explored in molecular dynamics simulations. To obtain equilibrium binding conformations and dissociation constants from these simulations, it is essential to resolve these rare events. Advanced sampling methods such as the adaptive biasing force (ABF) promote the occurrence of less probable configurations in a system, therefore facilitating the sampling of essential collective variables that characterize the host-guest interactions. Here, we present the application of ABF to a rod-cavitand coarse-grained model of host-guest systems to acquire the potential of mean force. We show that the employment of ABF enables the computation of the configurational and thermodynamic properties of bound and unbound states, including the free energy landscape. Moreover, we identify important dynamic bottlenecks that limit sampling and discuss how these may be addressed in more general systems.
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Affiliation(s)
- Andrew V Marquardt
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Mohsen Farshad
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Jonathan K Whitmer
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
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18
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Chen H, Zhou Y, Wang X, Chai X, Wang Z, Wang E, Xu L, Hou T, Li D, Duan M. Discovery of Novel Anti-Resistance AR Antagonists Guided by Funnel Metadynamics Simulation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309261. [PMID: 38481034 PMCID: PMC11109662 DOI: 10.1002/advs.202309261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/18/2024] [Indexed: 05/23/2024]
Abstract
Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metadynamics is employed to elucidate the inherent regulation mechanisms of three AR antagonists (hydroxyflutamide, enzalutamide, and darolutamide) on AR. For the first time it is observed that the binding of antagonists significantly disturbed the C-terminus of AR helix-11, thereby disrupting the specific internal hydrophobic contacts of AR-LBD and correspondingly the communication between AR ligand binding pocket (AR-LBP), activation function 2 (AF2), and binding function 3 (BF3). The subsequent bioassays verified the necessity of the hydrophobic contacts for AR function. Furthermore, it is found that darolutamide, a newly approved AR antagonist capable of fighting almost all reported drug resistant AR mutants, can induce antagonistic binding structure. Subsequently, docking-based virtual screening toward the dominant binding conformation of AR for darolutamide is conducted, and three novel AR antagonists with favorable binding affinity and strong capability to combat drug resistance are identified by in vitro bioassays. This work provides a novel rational strategy for the development of anti-resistant AR antagonists.
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Affiliation(s)
- Haiyi Chen
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- National Centre for Magnetic Resonance in WuhanState Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhanHubei430071China
- Liangzhu LaboratoryZhejiang University Medical CenterHangzhouZhejiang311121China
| | - Yuxin Zhou
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xinyue Wang
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Xin Chai
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
- Liangzhu LaboratoryZhejiang University Medical CenterHangzhouZhejiang311121China
| | - Zhe Wang
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | | | - Lei Xu
- Institute of Bioinformatics and Medical EngineeringSchool of Electrical and Information EngineeringJiangsu University of TechnologyChangzhou213001China
| | - Tingjun Hou
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Dan Li
- College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang310058China
| | - Mojie Duan
- National Centre for Magnetic Resonance in WuhanState Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhanHubei430071China
- NMR and Molecular Sciences, School of Chemistry and Chemical Engineering, The State Key Laboratory of Refractories and MetallurgyWuhan University of Science and TechnologyWuhan430081China
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19
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Chen X, Lu Z, Xiao J, Xia W, Pan Y, Xia H, Chen YH, Zhang H. Small-Molecule Inhibitors of TIPE3 Protein Identified through Deep Learning Suppress Cancer Cell Growth In Vitro. Cells 2024; 13:771. [PMID: 38727307 PMCID: PMC11082981 DOI: 10.3390/cells13090771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human cancers and has been established to significantly promote tumor cell proliferation, migration, and invasion and inhibit the apoptosis of cancer cells. Thus, inhibiting the function of TIPE3 is expected to be an effective strategy against cancer. The advancement of artificial intelligence (AI)-driven drug development has recently invigorated research in anti-cancer drug development. In this work, we incorporated DFCNN, Autodock Vina docking, DeepBindBC, MD, and metadynamics to efficiently identify inhibitors of TIPE3 from a ZINC compound dataset. Six potential candidates were selected for further experimental study to validate their anti-tumor activity. Among these, three small-molecule compounds (K784-8160, E745-0011, and 7238-1516) showed significant anti-tumor activity in vitro, leading to reduced tumor cell viability, proliferation, and migration and enhanced apoptotic tumor cell death. Notably, E745-0011 and 7238-1516 exhibited selective cytotoxicity toward tumor cells with high TIPE3 expression while having little or no effect on normal human cells or tumor cells with low TIPE3 expression. A molecular docking analysis further supported their interactions with TIPE3, highlighting hydrophobic interactions and their shared interaction residues and offering insights for designing more effective inhibitors. Taken together, this work demonstrates the feasibility of incorporating deep learning and MD simulations in virtual drug screening and provides inhibitors with significant potential for anti-cancer drug development against TIPE3-.
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Affiliation(s)
- Xiaodie Chen
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhen Lu
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
| | - Jin Xiao
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Wei Xia
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Yi Pan
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
| | - Houjun Xia
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518055, China
| | - Youhai H. Chen
- Center for Cancer Immunology, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (X.C.); (Z.L.); (H.X.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518055, China
| | - Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (J.X.); (W.X.); (Y.P.)
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20
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Hasegawa S, Yoshida M, Nagao H, Sugiyama H, Sawa M, Kinoshita T. Distinct binding modes of a benzothiazole derivative confer structural bases for increasing ERK2 or p38α MAPK selectivity. Biochem Biophys Res Commun 2024; 704:149707. [PMID: 38428305 DOI: 10.1016/j.bbrc.2024.149707] [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: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/03/2024]
Abstract
Mitogen-activated protein kinases (MAPKs), including extracellular signal-regulated kinase 2 (ERK2) and p38α MAP kinase (p38α MAPK), regulate various cellular responses. ERK2 is a drug target for treating many diseases, such as cancer, whereas p38α has attracted much attention as a promising drug target for treating inflammatory disorders. ERK2 is a critical off-target for p38α MAPK and vice versa. In this study, an allosteric ERK2 inhibitor with a benzothiazole moiety (compound 1) displayed comparable inhibitory activity against p38α MAPK. Crystal structures of these MAPKs showed that compound 1 bound to the allosteric site of ERK2 and p38α MAPK in distinct manners. Compound 1 formed a covalent bond with Cys162 of p38α MAPK, whereas this covalent bond was absent in the ERK2 complex even though the corresponding cysteine is conserved in ERK2. Structural dissection combined with computational simulations indicated that an amino acid difference in the allosteric site is responsible for the distinct binding modes of compound 1 with ERK2 and p38α MAPK. These structural insights underline the feasibility of developing highly selective and potent ERK2 and p38α MAPK inhibitors.
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Affiliation(s)
- Seisuke Hasegawa
- Graduate School of Science, Osaka Metropolitan University, Osaka, 599-8570, Japan
| | - Mayu Yoshida
- Graduate School of Science, Osaka Metropolitan University, Osaka, 599-8570, Japan
| | | | | | | | - Takayoshi Kinoshita
- Graduate School of Science, Osaka Metropolitan University, Osaka, 599-8570, Japan.
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21
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Croney K, McCarty J. Exploring Product Release from Yeast Cytosine Deaminase with Metadynamics. J Phys Chem B 2024; 128:3102-3112. [PMID: 38516924 PMCID: PMC11000218 DOI: 10.1021/acs.jpcb.3c07972] [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: 12/05/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
The yeast cytosine deaminase (yCD) enzyme/5-fluorocytosine prodrug system is a promising candidate for targeted chemotherapeutics. After conversion of the prodrug into the toxic chemotherapeutic drug, 5-fluorouracil (5-FU), the slow product release from the enzyme limits the overall catalytic efficiency of the enzyme/prodrug system. Here, we present a computational study of the product release of the anticancer drug, 5-FU, from yCD using metadynamics. We present a comparison of the 5-FU drug to the natural enzyme product, uracil. We use volume-based metadynamics to compute the free energy landscape for product release and show a modest binding affinity for the product to the enzyme, consistent with experiments. Next, we use infrequent metadynamics to estimate the unbiased release rate from Kramers time-dependent rate theory and find a favorable comparison to experiment with a slower rate of product release for the 5-FU system. Our work demonstrates how adaptive sampling methods can be used to study the protein-ligand unbinding process for engineering enzyme/prodrug systems and gives insights into the molecular mechanism of product release for the yCD/5-FU system.
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Affiliation(s)
- Kayla
A. Croney
- Department of Chemistry, Western
Washington University, Bellingham, Washington 98225, United States
| | - James McCarty
- Department of Chemistry, Western
Washington University, Bellingham, Washington 98225, United States
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22
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Gao J, Lin C, Zhang C, Zhang X, Wang Y, Xu H, Zhang T, Li H, Wang H, Wang X. Exploring the Function of (+)-Naltrexone Precursors: Their Activity as TLR4 Antagonists and Potential in Treating Morphine Addiction. J Med Chem 2024; 67:3127-3143. [PMID: 38306598 DOI: 10.1021/acs.jmedchem.3c02316] [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: 02/04/2024]
Abstract
Disruptions in the toll-like receptor 4 (TLR4) signaling pathway are linked to chronic inflammation, neuropathic pain, and drug addiction. (+)-Naltrexone, an opioid-derived TLR4 antagonist with a (+)-isomer configuration, does not interact with classical opioid receptors and has moderate blood-brain barrier permeability. Herein, we developed a concise 10-step synthesis for (+)-naltrexone and explored its precursors, (+)-14-hydroxycodeinone (1) and (+)-14-hydroxymorphinone (3). These precursors exhibited TLR4 antagonistic activities 100 times stronger than (+)-naltrexone, particularly inhibiting the TLR4-TRIF pathway. In vivo studies showed that these precursors effectively reduced behavioral effects of morphine, like sensitization and conditioned place preference by suppressing microglial activation and TNF-α expression in the medial prefrontal cortex and ventral tegmental area. Additionally, 3 displayed a longer half-life and higher oral bioavailability than 1. Overall, this research optimized (+)-naltrexone synthesis and identified its precursors as potent TLR4 antagonists, offering potential treatments for morphine addiction.
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Affiliation(s)
- Jingwei Gao
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Cong Lin
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Cong Zhang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiaozheng Zhang
- Department of Biochemistry and Molecular Biology, Shanxi Key Laboratory of Birth Defect and Cell Regeneration, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Yibo Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Hangyu Xu
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Tianshu Zhang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Haohong Li
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang 311121, China
| | - Hongshuang Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Xiaohui Wang
- Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
- Beijing National Laboratory for Molecular Sciences, Beijing 100190, China
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23
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DeLuca M, Sensale S, Lin PA, Arya G. Prediction and Control in DNA Nanotechnology. ACS APPLIED BIO MATERIALS 2024; 7:626-645. [PMID: 36880799 DOI: 10.1021/acsabm.2c01045] [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] [Indexed: 03/08/2023]
Abstract
DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.
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Affiliation(s)
- Marcello DeLuca
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Sebastian Sensale
- Department of Physics, Cleveland State University, Cleveland, Ohio 44115, United States
| | - Po-An Lin
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Gaurav Arya
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
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24
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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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25
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Beyerle ER, Tiwary P. Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation. J Phys Chem B 2024; 128:755-767. [PMID: 38205806 DOI: 10.1021/acs.jpcb.3c08304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Ligand unbinding is mediated by its free energy change, which has intertwined contributions from both energy and entropy. It is important, but not easy, to quantify their individual contributions to the free energy profile. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. Using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe the hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as the ligand and pocket sizes change. In both cases, we observe that the free-energy barrier to unbinding is dominated by entropy considerations. Furthermore, the process of methane unbinding is driven by methane solvation, while fullerene unbinding is driven first by pocket wetting and then fullerene wetting. For both solutes, the direct importance of the distance from the binding pocket to the learned reaction coordinate is present, but low. Our framework and subsequent feature important analysis thus give useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean.
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Affiliation(s)
- Eric R Beyerle
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Pratyush Tiwary
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
- Department of Chemistry, University of Maryland, College Park, Maryland 20742, United States
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26
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Chen L, Wu Y, Wu C, Silveira A, Sherman W, Xu H, Gallicchio E. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. J Chem Inf Model 2024; 64:250-264. [PMID: 38147877 DOI: 10.1021/acs.jcim.3c01705] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
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Affiliation(s)
- Lieyang Chen
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Yujie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Chuanjie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Ana Silveira
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Huafeng Xu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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27
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Calderón JC, Plut E, Keller M, Cabrele C, Reiser O, Gervasio FL, Clark T. Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. J Chem Inf Model 2024; 64:205-218. [PMID: 38150388 DOI: 10.1021/acs.jcim.3c01574] [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: 12/29/2023]
Abstract
A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.
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Affiliation(s)
- Jacqueline C Calderón
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
| | - Eva Plut
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Max Keller
- Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg D-93040, Germany
| | - Chiara Cabrele
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | - Oliver Reiser
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, University of Regensburg, Regensburg 93040, Germany
| | | | - Timothy Clark
- Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nuernberg, Naegelsbachstr. 25, Erlangen 91052, Germany
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28
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Gasparello J, Verona M, Chilin A, Gambari R, Marzaro G. Assessing the interaction between hemoglobin and the receptor binding domain of SARS-CoV-2 spike protein through MARTINI coarse-grained molecular dynamics. Int J Biol Macromol 2023; 253:127088. [PMID: 37774812 DOI: 10.1016/j.ijbiomac.2023.127088] [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: 07/10/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/01/2023]
Abstract
The emergence of different coronavirus-related diseases in the 2000's (SARS, MERS, and Covid-19) warrants the need of a complete understanding of the pathological, biological, and biochemical behavior of this class of pathogens. Great attention has been paid to the SARS-CoV-2 Spike protein, and its interaction with the human ACE2 has been thoroughly investigated. Recent findings suggested that the SARS-CoV-2 components may interact with different human proteins, and hemoglobin has very recently been demonstrated as a potential target for the Spike protein. Here we have investigated the interaction between either adult or fetal hemoglobin and the receptor binding domain of the Spike protein at molecular level through advanced molecular dynamics techniques and proposed rational binding modes and energy estimations. Our results agree with biochemical data previously reported in literature. We also demonstrated that co-incubation of pulmonary epithelial cells with hemoglobin strongly reduces the pro-inflammatory effects exerted by the concomitant administration of Spike protein.
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Affiliation(s)
- Jessica Gasparello
- Department of Life Sciences and Biotechnology, University of Ferrara, via Fossato di Mortara 74, 44121 Ferrara, Italy
| | - Marco Verona
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35313 Padova, Italy
| | - Adriana Chilin
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35313 Padova, Italy
| | - Roberto Gambari
- Department of Life Sciences and Biotechnology, University of Ferrara, via Fossato di Mortara 74, 44121 Ferrara, Italy
| | - Giovanni Marzaro
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35313 Padova, Italy.
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29
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Rubina, Moin ST. Attempting Well-Tempered Funnel Metadynamics Simulations for the Evaluation of the Binding Kinetics of Methionine Aminopeptidase-II Inhibitors. J Chem Inf Model 2023; 63:7729-7743. [PMID: 38059911 DOI: 10.1021/acs.jcim.3c01130] [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: 12/08/2023]
Abstract
Understanding the unbinding kinetics of protein-ligand complexes is considered a significant approach for the design of ligands with desired specificity and safety. In recent years, enhanced sampling methods have emerged as effective tools for studying the unbinding kinetics of protein-ligand complexes at the atomistic level. MetAP-II is a target for the treatment of cancer for which not a single effective drug is available yet. The identification of the dissociation rate of ligands from the complexes often serves as a better predictor for in vivo efficacy than the ligands' binding affinity. Here, funnel-based restraint well-tempered metadynamics simulations were applied to predict the residence time of two ligands bound to MetAP-II, along with the ligand association and dissociation mechanism involving the identification of the binding hotspot during ligand egress. The ligand-egressing route revealed by metadynamics simulations also correlated with the identified pathways from the CAVER analysis and by the enhanced sampling simulation using PLUMED. Ligand 1 formed a strong H-bond interaction with GLU364 estimating a higher residence time of 28.22 ± 5.29 ns in contrast to ligand 2 with a residence time of 19.05 ± 3.58 ns, which easily dissociated from the binding pocket of MetAP-II. The results obtained from the simulations were consistent to reveal ligand 1 being superior to ligand 2; however, the experimental data related to residence time were close for both ligands, and no kinetic data were available for ligand 2. The current study could be considered the first attempt to apply an enhanced sampling method for the evaluation of the binding kinetics and thermodynamics of two different classes of ligands to a binuclear metalloprotein.
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Affiliation(s)
- Rubina
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
| | - Syed Tarique Moin
- Third World Center for Science and Technology H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Science University of Karachi, Karachi 75270, Pakistan
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30
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Liu R, Li W, Yao Y, Wu Y, Luo HB, Li Z. Accelerating and Automating the Free Energy Perturbation Absolute Binding Free Energy Calculation with the RED-E Function. J Chem Inf Model 2023; 63:7755-7767. [PMID: 38048439 DOI: 10.1021/acs.jcim.3c01670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.
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Affiliation(s)
- Runduo Liu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Wenchao Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yufen Yao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Yinuo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Hai-Bin Luo
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
- Song Li' Academician Workstation of Hainan University (School of Pharmaceutical Sciences), Yazhou Bay, Sanya 572000, China
| | - Zhe Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
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31
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Fu H, Chipot C, Shao X, Cai W. Standard Binding Free-Energy Calculations: How Far Are We from Automation? J Phys Chem B 2023; 127:10459-10468. [PMID: 37824848 DOI: 10.1021/acs.jpcb.3c04370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.
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Affiliation(s)
- Haohao Fu
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Christophe Chipot
- Laboratoire International Associé CNRS and University of Illinois at Urbana-Champaign, UMR no. 7019, Université de Lorraine, BP 70239, F-54506 Vandoeuvre-lès-Nancy, France
- Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, Illinois 61801, United States
- Department of Chemistry, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States
- Department of Chemistry, The University of Hawai'i at Ma̅noa, 2545 McCarthy Mall, Honolulu, Hawaii 96822, United States
| | - Xueguang Shao
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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32
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Procacci P. Dealing with Induced Fit, Conformational Selection, and Secondary Poses in Molecular Dynamics Simulations for Reliable Free Energy Predictions. J Chem Theory Comput 2023; 19:8942-8954. [PMID: 38037326 PMCID: PMC10720345 DOI: 10.1021/acs.jctc.3c00867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023]
Abstract
In this study, we have tested the performance of standard molecular dynamics (MD) simulations, replicates of shorter standard MD simulations, and Hamiltonian Replica Exchange (HREM) simulations for the sampling of two macrocyclic hosts for guest delivery, characterized by induced fit (phenyl-based host) and conformation selection (naphthyl-based host) and of the ODR-BRD4(I) drug-receptor system where the ligand can assume two main poses. For the optimization of the HREM simulation, we have proposed and tested an on-the-fly iterative scheme for equalizing the acceptance ratio along the replica progression at a constant replica number resulting in a moderate impact of the sampling efficiency. Concerning standard MD, we have found that, while splitting the total allocated simulation time in short MD replicates can reproduce the sampling efficiency of HREM in the phenyl-based host and in the ODR-BRD4(I) complex, in the naphthyl-based macrocycle, characterized by long-lived metastable states, enhanced sampling techniques are the only viable alternative for a reliable canonical sampling of the rugged conformational landscape.
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Affiliation(s)
- Piero Procacci
- Dipartimento di Chimica “Ugo
Schiff”, Università degli
Studi di Firenze, Via
della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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33
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Behera S, Balasubramanian S. Lipase A from Bacillus subtilis: Substrate Binding, Conformational Dynamics, and Signatures of a Lid. J Chem Inf Model 2023; 63:7545-7556. [PMID: 37989487 DOI: 10.1021/acs.jcim.3c01681] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Protein-ligand binding studies are crucial for understanding the molecular basis of biological processes and for further advancing industrial biocatalysis and drug discovery. Using computational modeling and molecular dynamics simulations, we investigated the binding of a butyrate ester substrate to the lipase A (LipA) enzyme of Bacillus subtilis. Besides obtaining a close agreement of the binding free energy with the experimental value, the study reveals a remarkable reorganization of the catalytic triad upon substrate binding, leading to increased essential hydrogen bond populations. The investigation shows the distortion of the oxyanion hole in both the substrate-bound and unbound states of LipA and highlights the strengthening of the same in the tetrahedral intermediate complex. Principal component analysis of the unbound ensemble reveals the dominant motion in LipA to be the movement of Loop-1 (Tyr129-Arg142) between two states that cover and uncover the active site, mirroring that of a lid prevalent in several lipases. This lid-like motion of Loop-1 is also supported by its tendency to spontaneously open up at an oil-water interface. Overall, this study provides valuable insights into the impact of substrate binding on the structure, flexibility, and conformational dynamics of the LipA enzyme.
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Affiliation(s)
- Sudarshan Behera
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
| | - Sundaram Balasubramanian
- Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India
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34
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Champion C, Gall R, Ries B, Rieder SR, Barros EP, Riniker S. Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases. J Chem Inf Model 2023; 63:7133-7147. [PMID: 37948537 PMCID: PMC10685456 DOI: 10.1021/acs.jcim.3c01469] [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: 09/13/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Alchemical free-energy methods based on molecular dynamics (MD) simulations have become important tools to identify modifications of small organic molecules that improve their protein binding affinity during lead optimization. The routine application of pairwise free-energy methods to rank potential binders from best to worst is impacted by the combinatorial increase in calculations to perform when the number of molecules to assess grows. To address this fundamental limitation, our group has developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method, enabling the calculation of alchemical free-energy differences between multiple ligands (N > 2) from a single MD simulation. In this work, we apply the method to a set of four kinases with diverse binding pockets and their corresponding inhibitors (42 in total), chosen to showcase the general applicability of RE-EDS in prospective drug design campaigns. We show that for the targets studied, RE-EDS is able to model up to 13 ligands simultaneously with high sampling efficiency, leading to a substantial decrease in computational cost when compared to pairwise methods.
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Affiliation(s)
- Candide Champion
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - René Gall
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | | | - Salomé R. Rieder
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Emilia P. Barros
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and
Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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35
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Kuzovlev AS, Zybalov MD, Golovin AV, Gureev MA, Kasatkina MA, Biryukov MV, Belik AR, Silonov SA, Yunin MA, Zigangirova NA, Reshetnikov VV, Isakova YE, Porozov YB, Ivanov RA. Naphthyl-Substituted Indole and Pyrrole Carboxylic Acids as Effective Antibiotic Potentiators-Inhibitors of Bacterial Cystathionine γ-Lyase. Int J Mol Sci 2023; 24:16331. [PMID: 38003521 PMCID: PMC10671052 DOI: 10.3390/ijms242216331] [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: 10/17/2023] [Revised: 11/08/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Over the past decades, the problem of bacterial resistance to most antibiotics has become a serious threat to patients' survival. Nevertheless, antibiotics of a novel class have not been approved since the 1980s. The development of antibiotic potentiators is an appealing alternative to the challenging process of searching for new antimicrobials. Production of H2S-one of the leading defense mechanisms crucial for bacterial survival-can be influenced by the inhibition of relevant enzymes: bacterial cystathionine γ-lyase (bCSE), bacterial cystathionine β-synthase (bCBS), or 3-mercaptopyruvate sulfurtransferase (MST). The first one makes the main contribution to H2S generation. Herein, we present data on the synthesis, in silico analyses, and enzymatic and microbiological assays of novel bCSE inhibitors. Combined molecular docking and molecular dynamics analyses revealed a novel binding mode of these ligands to bCSE. Lead compound 2a manifested strong potentiating activity when applied in combination with some commonly used antibiotics against multidrug-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. The compound was found to have favorable in vitro absorption, distribution, metabolism, excretion, and toxicity parameters. The high effectiveness and safety of compound 2a makes it a promising candidate for enhancing the activity of antibiotics against high-priority pathogens.
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Affiliation(s)
- Andrey S. Kuzovlev
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Mikhail D. Zybalov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Andrey V. Golovin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1/73 Leninskie gori St., 119234 Moscow, Russia;
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
| | - Maxim A. Gureev
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
- Laboratory of Bio- and Chemoinformatics, Institute of Biodesign and Modeling of Complex Systems, I.M. Sechenov First Moscow State Medical University, 8/2 Trubetskaya, 119991 Moscow, Russia
| | - Mariia A. Kasatkina
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Mikhail V. Biryukov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Faculty of Biology, Lomonosov Moscow State University, 1/12 Leninskie gori St., 119234 Moscow, Russia
| | - Albina R. Belik
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Sergey A. Silonov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, 4 Tikhoretsky Ave., 194064 St. Petersburg, Russia
| | - Maxim A. Yunin
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Nailya A. Zigangirova
- Medical Microbiology Department, Laboratory of Chlamydiosis, National Research Center for Epidemiology and Microbiology Named after N. F. Gamaleya, 18 Gamaleya St., 123098 Moscow, Russia;
| | - Vasiliy V. Reshetnikov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
- Institute of Cytology and Genetics, Siberian Branch of RAS, 10 Akademika Lavrentyeva, 630090 Novosibirsk, Russia
| | - Yulia E. Isakova
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
| | - Yuri B. Porozov
- Laboratory of Bioinformatics, Center of AI and Information Technologies, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.A.G.); (Y.B.P.)
- Laboratory of Bio- and Chemoinformatics, Institute of Biodesign and Modeling of Complex Systems, I.M. Sechenov First Moscow State Medical University, 8/2 Trubetskaya, 119991 Moscow, Russia
| | - Roman A. Ivanov
- Translational Medicine Research Center, Sirius University of Science and Technology, Olympic Ave. 1, 354340 Sochi, Russia; (M.D.Z.); (M.A.K.); (M.V.B.); (A.R.B.); (S.A.S.); (M.A.Y.); (V.V.R.); (Y.E.I.); (R.A.I.)
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36
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Bobrovs R, Drunka L, Kanepe I, Jirgensons A, Caflisch A, Salvalaglio M, Jaudzems K. Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. J Chem Inf Model 2023; 63:6890-6899. [PMID: 37801405 DOI: 10.1021/acs.jcim.3c00826] [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
Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.
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Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Matteo Salvalaglio
- Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
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37
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Siddiqui GA, Stebani JA, Wragg D, Koutsourelakis PS, Casini A, Gagliardi A. Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study. Chemistry 2023; 29:e202302375. [PMID: 37555841 DOI: 10.1002/chem.202302375] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/09/2023] [Indexed: 08/10/2023]
Abstract
In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.
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Affiliation(s)
- Gohar Ali Siddiqui
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
| | - Julia A Stebani
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Darren Wragg
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Phaedon-Stelios Koutsourelakis
- Professorship for Data-driven Materials Modeling School of Engineering and Design, Technical University of Munich (TUM), Boltzmannstr. 15, 85748, Garching b. München, Germany
| | - Angela Casini
- Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany
| | - Alessio Gagliardi
- Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany
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38
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Bajpai S, Petkov BK, Tong M, Abreu CRA, Nair NN, Tuckerman ME. An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package. J Comput Chem 2023; 44:2166-2183. [PMID: 37464902 DOI: 10.1002/jcc.27182] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 07/20/2023]
Abstract
Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.
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Affiliation(s)
- Shitanshu Bajpai
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Brian K Petkov
- Department of Chemistry, New York University (NYU), New York, New York, USA
| | - Muchen Tong
- Department of Chemistry, New York University (NYU), New York, New York, USA
| | - Charlles R A Abreu
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur (IITK), Kanpur, India
| | - Mark E Tuckerman
- Department of Chemistry, New York University (NYU), New York, New York, USA
- Courant Institute of Mathematical Sciences, New York University (NYU), New York, New York, USA
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China
- Simons Center for Computational Physical Chemistry, New York University, New York, New York, USA
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39
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Pasarkar AP, Bencomo GM, Olsson S, Dieng AB. Vendi sampling for molecular simulations: Diversity as a force for faster convergence and better exploration. J Chem Phys 2023; 159:144108. [PMID: 37823459 DOI: 10.1063/5.0166172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023] Open
Abstract
Molecular dynamics (MD) is the method of choice for understanding the structure, function, and interactions of molecules. However, MD simulations are limited by the strong metastability of many molecules, which traps them in a single conformation basin for an extended amount of time. Enhanced sampling techniques, such as metadynamics and replica exchange, have been developed to overcome this limitation and accelerate the exploration of complex free energy landscapes. In this paper, we propose Vendi Sampling, a replica-based algorithm for increasing the efficiency and efficacy of the exploration of molecular conformation spaces. In Vendi sampling, replicas are simulated in parallel and coupled via a global statistical measure, the Vendi Score, to enhance diversity. Vendi sampling allows for the recovery of unbiased sampling statistics and dramatically improves sampling efficiency. We demonstrate the effectiveness of Vendi sampling in improving molecular dynamics simulations by showing significant improvements in coverage and mixing between metastable states and convergence of free energy estimates for four common benchmarks, including Alanine Dipeptide and Chignolin.
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Affiliation(s)
- Amey P Pasarkar
- Vertaix, Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey 08544, USA
| | - Gianluca M Bencomo
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey 08544, USA
| | - Simon Olsson
- Department of Computer Science and Engineering, Chalmers University of Technology, Rännvägen 6, 41258 Gothenburg, Sweden
| | - Adji Bousso Dieng
- Vertaix, Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey 08544, USA
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40
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Zantza I, Pyrris Y, Raniolo S, Papadaki GF, Lambrinidis G, Limongelli V, Diallinas G, Mikros E. Uracil/H + Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters. J Mol Biol 2023; 435:168226. [PMID: 37544358 DOI: 10.1016/j.jmb.2023.168226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H+ symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H+ by FurE using path collective variable, funnel metadynamics and rational mutational analysis. Our study reveals a stepwise, induced-fit, mechanism of ordered sequential transport of proton and uracil, which in turn suggests that FurE, functions as a multi-step gated pore, rather than employing 'rocking' of compact domains, as often proposed for APC transporters. Finally, our work supports that specific residues of the cytoplasmic N-tail are involved in substrate translocation, in line with their essentiality for FurE function.
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Affiliation(s)
- Iliana Zantza
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Yiannis Pyrris
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece.
| | - Stefano Raniolo
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland.
| | - Georgia F Papadaki
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece
| | - George Lambrinidis
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece.
| | - Vittorio Limongelli
- Faculty of Biomedical Sciences, Euler Institute, Università della Svizzera italiana (USI), Lugano 6900, Switzerland; Department of Pharmacy, University of Naples "Federico II", Naples 80131, Italy.
| | - George Diallinas
- Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15781, Greece; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Heraklion 70013, Greece.
| | - Emmanuel Mikros
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens 15771, Greece; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Marousi 15125, Greece.
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41
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Ray D, Parrinello M. Kinetics from Metadynamics: Principles, Applications, and Outlook. J Chem Theory Comput 2023; 19:5649-5670. [PMID: 37585703 DOI: 10.1021/acs.jctc.3c00660] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.
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Affiliation(s)
- Dhiman Ray
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
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42
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Rizzi V, Aureli S, Ansari N, Gervasio FL. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. J Chem Theory Comput 2023; 19:5731-5742. [PMID: 37603295 PMCID: PMC10500989 DOI: 10.1021/acs.jctc.3c00254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 08/22/2023]
Abstract
Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.
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Affiliation(s)
- Valerio Rizzi
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Simone Aureli
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
| | - Narjes Ansari
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Francesco Luigi Gervasio
- School
of Pharmaceutical Sciences, University of
Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland
- Institute
of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, 1206 Genève, Switzerland
- Swiss
Institute of Bioinformatics, University
of Geneva, 1206 Genève, Switzerland
- Department
of Chemistry, University College London, WC1E 6BT London, U.K.
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43
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Tripathi S, Nair NN. Temperature Accelerated Sliced Sampling to Probe Ligand Dissociation from Protein. J Chem Inf Model 2023; 63:5182-5191. [PMID: 37540828 DOI: 10.1021/acs.jcim.3c00376] [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: 08/06/2023]
Abstract
Modeling ligand unbinding in proteins to estimate the free energy of binding and probing the mechanism presents several challenges. They primarily pertain to the entropic bottlenecks resulting from protein and solvent conformations. While exploring the unbinding processes using enhanced sampling techniques, very long simulations are required to sample all of the conformational states as the system gets trapped in local free energy minima along transverse coordinates. Here, we demonstrate that temperature accelerated sliced sampling (TASS) is an ideal approach to overcome some of the difficulties faced by conventional sampling methods in studying ligand unbinding. Using TASS, we study the unbinding of avibactam inhibitor molecules from the Class C β-lactamase (CBL) active site. Extracting CBL-avibactam unbinding free energetics, unbinding pathways, and identifying critical interactions from the TASS simulations are demonstrated.
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Affiliation(s)
- Shubhandra Tripathi
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Nisanth N Nair
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
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44
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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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45
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Seiferth D, Tucker SJ, Biggin PC. Limitations of non-polarizable force fields in describing anion binding poses in non-polar synthetic hosts. Phys Chem Chem Phys 2023. [PMID: 37365974 DOI: 10.1039/d3cp00479a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Transmembrane anion transport by synthetic ionophores has received increasing interest not only because of its relevance for understanding endogenous anion transport, but also because of potential implications for therapeutic routes in disease states where chloride transport is impaired. Computational studies can shed light on the binding recognition process and can deepen our mechanistic understanding of them. However, the ability of molecular mechanics methods to properly capture solvation and binding properties of anions is known to be challenging. Consequently, polarizable models have been suggested to improve the accuracy of such calculations. In this study, we calculate binding free energies for different anions to the synthetic ionophore, biotin[6]uril hexamethyl ester in acetonitrile and to biotin[6]uril hexaacid in water by employing non-polarizable and polarizable force fields. Anion binding shows strong solvent dependency consistent with experimental studies. In water, the binding strengths are iodide > bromide > chloride, and reversed in acetonitrile. These trends are well captured by both classes of force fields. However, the free energy profiles obtained from potential of mean force calculations and preferred binding positions of anions depend on the treatment of electrostatics. Results from simulations using the AMOEBA force-field, which recapitulate the observed binding positions, suggest strong effects from multipoles dominate with a smaller contribution from polarization. The oxidation status of the macrocycle was also found to influence anion recognition in water. Overall, these results have implications for the understanding of anion host interactions not just in synthetic ionophores, but also in narrow cavities of biological ion channels.
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Affiliation(s)
- David Seiferth
- Clarendon Laboratory, Department of Physics, University of Oxford, Oxford, OX1 3PU, UK
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK.
| | - Stephen J Tucker
- Clarendon Laboratory, Department of Physics, University of Oxford, Oxford, OX1 3PU, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK.
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46
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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] [Grants] [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 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 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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47
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Blazhynska M, Goulard Coderc de Lacam E, Chen H, Chipot C. Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning. J Chem Theory Comput 2023. [PMID: 37196198 DOI: 10.1021/acs.jctc.3c00141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 μs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.
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Affiliation(s)
- Marharyta Blazhynska
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France
| | - Emma Goulard Coderc de Lacam
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France
| | - Haochuan Chen
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France
- Theoretical and Computational Biophysics Group, Beckman Institute, and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Biochemistry and Molecular Biology, The University of Chicago, 929 E. 57th Street W225, Chicago, Illinois 60637, United States
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48
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Zuo K, Kranjc A, Capelli R, Rossetti G, Nechushtai R, Carloni P. Metadynamics simulations of ligands binding to protein surfaces: a novel tool for rational drug design. Phys Chem Chem Phys 2023; 25:13819-13824. [PMID: 37184538 DOI: 10.1039/d3cp01388j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces.
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Affiliation(s)
- Ke Zuo
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
- Department of Physics, Università degli Studi di Ferrara, Ferrara 44121, Italy
| | - Agata Kranjc
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
| | - Riccardo Capelli
- Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, Milan 20133, Italy
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Jülich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen 52074, Germany
| | - Rachel Nechushtai
- The Alexander Silberman Institute of Life Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 91904, Israel
| | - Paolo Carloni
- Computational Biomedicine, Institute of Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
- Department of Physics, RWTH Aachen University, Aachen 52074, Germany
- JARA Institute: Molecular Neuroscience and Imaging, Institute of Neuroscience and Medicine INM-11, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
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49
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Motta S, Siani P, Donadoni E, Frigerio G, Bonati L, Di Valentin C. Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles. NANOSCALE 2023; 15:7909-7919. [PMID: 37066796 DOI: 10.1039/d3nr00397c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO2 nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.
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Affiliation(s)
- Stefano Motta
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Paulo Siani
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Edoardo Donadoni
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Giulia Frigerio
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
| | - Laura Bonati
- Dipartimento di Scienze dell'Ambiente e del Territorio, Università di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Cristiana Di Valentin
- Dipartimento di Scienza dei Materiali, Università di Milano Bicocca, via R. Cozzi 55, 20125 Milano, Italy.
- BioNanoMedicine Center NANOMIB, University of Milano-Bicocca, Italy
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50
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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