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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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Schmitz B, Frieg B, Homeyer N, Jessen G, Gohlke H. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Arch Pharm (Weinheim) 2024; 357:e2300612. [PMID: 38319801 DOI: 10.1002/ardp.202300612] [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: 10/20/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.
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Affiliation(s)
- Birte Schmitz
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Benedikt Frieg
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gisela Jessen
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany
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Herman RA, Ayepa E, Zhang WX, Li ZN, Zhu X, Ackah M, Yuan SS, You S, Wang J. Molecular modification and biotechnological applications of microbial aspartic proteases. Crit Rev Biotechnol 2024; 44:388-413. [PMID: 36842994 DOI: 10.1080/07388551.2023.2171850] [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: 06/22/2022] [Revised: 12/13/2022] [Accepted: 01/07/2023] [Indexed: 02/28/2023]
Abstract
The growing preference for incorporating microbial aspartic proteases in industries is due to their high catalytic function and high degree of substrate selectivity. These properties, however, are attributable to molecular alterations in their structure and a variety of other characteristics. Molecular tools, functional genomics, and genome editing technologies coupled with other biotechnological approaches have aided in improving the potential of industrially important microbial proteases by addressing some of their major limitations, such as: low catalytic efficiency, low conversion rates, low thermostability, and less enzyme yield. However, the native folding within their full domain is dependent on a surrounding structure which challenges their functionality in substrate conversion, mainly due to their mutual interactions in the context of complex systems. Hence, manipulating their structure and controlling their expression systems could potentially produce enzymes with high selectivity and catalytic functions. The proteins produced by microbial aspartic proteases are industrially capable and far-reaching in regulating certain harmful distinctive industrial processes and the benefits of being eco-friendly. This review provides: an update on current trends and gaps in microbial protease biotechnology, exploring the relevant recombinant strategies and molecular technologies widely used in expression platforms for engineering microbial aspartic proteases, as well as their potential industrial and biotechnological applications.
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Affiliation(s)
- Richard Ansah Herman
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
- School of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China
| | - Ellen Ayepa
- Oil Palm Research Institute, Council for Scientific and Industrial Research, Kusi, Ghana
| | - Wen-Xin Zhang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
| | - Zong-Nan Li
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
| | - Xuan Zhu
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
| | - Michael Ackah
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
| | - Shuang-Shuang Yuan
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
| | - Shuai You
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agricultural and Rural Affairs, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, P.R. China
| | - Jun Wang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, P.R. China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agricultural and Rural Affairs, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, P.R. China
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Srivastava AK, Srivastava S, Kumar V, Ghosh S, Yadav S, Malik R, Roy P, Prasad R. Identification and mechanistic exploration of structural and conformational dynamics of NF-kB inhibitors: rationale insights from in silico and in vitro studies. J Biomol Struct Dyn 2024; 42:1485-1505. [PMID: 37054525 DOI: 10.1080/07391102.2023.2200490] [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: 01/06/2023] [Accepted: 04/02/2023] [Indexed: 04/15/2023]
Abstract
Increased expression of target genes that code for proinflammatory chemical mediators results from a series of intracellular cascades triggered by activation of dysregulated NF-κB signaling pathway. Dysfunctional NF-kB signaling amplifies and perpetuates autoimmune responses in inflammatory diseases, including psoriasis. This study aimed to identify therapeutically relevant NF-kB inhibitors and elucidate the mechanistic aspects behind NF-kB inhibition. After virtual screening and molecular docking, five hit NF-kB inhibitors opted, and their therapeutic efficacy was examined using cell-based assays in TNF-α stimulated human keratinocyte cells. To investigate the conformational changes of target protein and inhibitor-protein interaction mechanisms, molecular dynamics (MD) simulations, binding free energy calculations together with principal component (PC) analysis, dynamics cross-correlation matrix analysis (DCCM), free energy landscape (FEL) analysis and quantum mechanical calculations were carried out. Among identified NF-kB inhibitors, myricetin and hesperidin significantly scavenged intracellular ROS and inhibited NF-kB activation. Analysis of the MD simulation trajectories of ligand-protein complexes revealed that myricetin and hesperidin formed energetically stabilized complexes with the target protein and were able to lock NF-kB in a closed conformation. Myricetin and hesperidin binding to the target protein significantly impacted conformational changes and internal dynamics of amino acid residues in protein domains. Tyr57, Glu60, Lys144 and Asp239 residues majorly contributed to locking the NF-kB in a closed conformation. The combinatorial approach employing in silico tools integrated with cell-based approaches substantiated the binding mechanism and NF-kB active site inhibition by the lead molecule myricetin, which can be explored as a viable antipsoriatic drug candidate associated with dysregulated NF-kB.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amit Kumar Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Shubham Srivastava
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, Rajasthan, India
| | - Viney Kumar
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Souvik Ghosh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Siddharth Yadav
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Ruchi Malik
- Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, Rajasthan, India
| | - Partha Roy
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Ramasare Prasad
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
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Meller A, Bhakat S, Solieva S, Bowman GR. Accelerating Cryptic Pocket Discovery Using AlphaFold. J Chem Theory Comput 2023. [PMID: 36948209 PMCID: PMC10373493 DOI: 10.1021/acs.jctc.2c01189] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Medical Scientist Training Program, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Soumendranath Bhakat
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Shahlo Solieva
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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Chen Y, Jin T, Li M, Yun X, Huan F, Liu Q, Hu M, Wei X, Zheng P, Liu G. Crystal Structure Analysis of Sarcoplasmic-Calcium-Binding Protein: An Allergen in Scylla paramamosain. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:1214-1223. [PMID: 36602420 DOI: 10.1021/acs.jafc.2c07267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The structure of allergenic proteins provides important information about the binding of allergens to antibodies. In this study, the crystal structure of Scy p 4 with a resolution of 1.60 Å was obtained by X-ray diffraction. Epitope mapping of Scy p 4 revealed that linear epitopes are located on the surface of Scy p 4. Also, conformational epitopes are mostly located in the structural conservative region. Further structural comparison, surface electrostatic potential, and hydrogen bond force analysis showed that mutation of Asp70 and Asp18/20/70 would lead to calcium-binding capacity being lost and destruction of allergenicity. Furthermore, a comparative analysis of structure showed that sarcoplasmic-calcium-binding protein (SCP) had high sequence, secondary, and spatial structural identity in crustaceans, which may be an important factor leading to cross-reactivity among crustaceans. The structure of Scy p 4 provides a template for epitope evaluation and localization of SCPs, which will help to reveal cross-reactivity among species.
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Affiliation(s)
- Yiyu Chen
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
- College of Materials Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Tengchuan Jin
- Hefei National Laboratory for Physical Sciences at Microscale, CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science & Technology of China, Hefei, Anhui 230026, China
| | - Mengsi Li
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
| | - Xiao Yun
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
| | - Fei Huan
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
| | - Qingmei Liu
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
| | - Mengjun Hu
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
| | - Xiaofeng Wei
- College of Materials Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Peiyi Zheng
- Hefei National Laboratory for Physical Sciences at Microscale, CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science & Technology of China, Hefei, Anhui 230026, China
| | - Guangming Liu
- College of Ocean Food and Biological Engineering, Xiamen Key Laboratory of Marine Functional Food, Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Jimei University, Xiamen, Fujian 361021, China
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Bhakat S. Collective variable discovery in the age of machine learning: reality, hype and everything in between. RSC Adv 2022; 12:25010-25024. [PMID: 36199882 PMCID: PMC9437778 DOI: 10.1039/d2ra03660f] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/20/2022] [Indexed: 11/21/2022] Open
Abstract
Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.
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Affiliation(s)
- Soumendranath Bhakat
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania Pennsylvania 19104-6059 USA +1 30549 32620
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