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Honorato RV, Trellet ME, Jiménez-García B, Schaarschmidt JJ, Giulini M, Reys V, Koukos PI, Rodrigues JPGLM, Karaca E, van Zundert GCP, Roel-Touris J, van Noort CW, Jandová Z, Melquiond ASJ, Bonvin AMJJ. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nat Protoc 2024:10.1038/s41596-024-01011-0. [PMID: 38886530 DOI: 10.1038/s41596-024-01011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/11/2024] [Indexed: 06/20/2024]
Abstract
Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.
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Affiliation(s)
- Rodrigo V Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E Trellet
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Fluigent, Le Kremlin-Bicêtre, France
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Zymvol Biomodeling SL, Barcelona, Spain
| | - Jörg J Schaarschmidt
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Karlsruhe Institute of Technology (KIT), Institute of Nanotechnology, Eggenstein-Leopoldshafen, Germany
| | - Marco Giulini
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Victor Reys
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Schrödinger Inc., New York, NY, USA
| | - Ezgi Karaca
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Izmir Biomedicine and Genome Center, Izimir, Turkey
| | - Gydo C P van Zundert
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Schrödinger Inc., New York, NY, USA
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Protein Design and Modeling Lab, Department of Structural Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Barcelona, Spain
| | - Charlotte W van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandová
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Boehringer Ingelheim International GmbH, Vienna, Austria
| | - Adrien S J Melquiond
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Utrecht Medical Center, Utrecht, the Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
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Giulini M, Honorato RV, Rivera JL, Bonvin AMJJ. ARCTIC-3D: automatic retrieval and clustering of interfaces in complexes from 3D structural information. Commun Biol 2024; 7:49. [PMID: 38184711 PMCID: PMC10771469 DOI: 10.1038/s42003-023-05718-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024] Open
Abstract
The formation of a stable complex between proteins lies at the core of a wide variety of biological processes and has been the focus of countless experiments. The huge amount of information contained in the protein structural interactome in the Protein Data Bank can now be used to characterise and classify the existing biological interfaces. We here introduce ARCTIC-3D, a fast and user-friendly data mining and clustering software to retrieve data and rationalise the interface information associated with the protein input data. We demonstrate its use by various examples ranging from showing the increased interaction complexity of eukaryotic proteins, 20% of which on average have more than 3 different interfaces compared to only 10% for prokaryotes, to associating different functions to different interfaces. In the context of modelling biomolecular assemblies, we introduce the concept of "recognition entropy", related to the number of possible interfaces of the components of a protein-protein complex, which we demonstrate to correlate with the modelling difficulty in classical docking approaches. The identified interface clusters can also be used to generate various combinations of interface-specific restraints for integrative modelling. The ARCTIC-3D software is freely available at github.com/haddocking/arctic3d and can be accessed as a web-service at wenmr.science.uu.nl/arctic3d.
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Affiliation(s)
- Marco Giulini
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Rodrigo V Honorato
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Jesús L Rivera
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands.
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3
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Xu X, Bonvin AMJJ. DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model. BIOINFORMATICS ADVANCES 2024; 4:vbad191. [PMID: 38213822 PMCID: PMC10782804 DOI: 10.1093/bioadv/vbad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/19/2023] [Indexed: 01/13/2024]
Abstract
Motivation Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability. Results We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes). Availability and implementation DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.
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Affiliation(s)
- Xiaotong Xu
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
| | - Alexandre M J J Bonvin
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [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: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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Mungmunpuntipantip R, Wiwanitkit V. Molecular Mimicry between hPF4 and SARS-CoV-2 Spike Protein: Comment. Semin Thromb Hemost 2023; 49:105. [PMID: 35451043 DOI: 10.1055/s-0042-1744279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
| | - Viroj Wiwanitkit
- Department of Community Medicine, Dr. D.Y. Patil University, Pune, Maharashtra, India
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Oliva F, Musiani F, Giorgetti A, De Rubeis S, Sorokina O, Armstrong DJ, Carloni P, Ruggerone P. Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases. Front Chem 2023; 10:1059593. [PMID: 36700074 PMCID: PMC9868658 DOI: 10.3389/fchem.2022.1059593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 12/06/2022] [Indexed: 01/11/2023] Open
Abstract
The seamless integration of human disease-related mutation data into protein structures is an essential component of any attempt to correctly assess the impact of the mutation. The key step preliminary to any structural modelling is the identification of the isoforms onto which mutations should be mapped due to there being several functionally different protein isoforms from the same gene. To handle large sets of data coming from omics techniques, this challenging task needs to be automatized. Here we present the MoNvIso (Modelling eNvironment for Isoforms) code, which identifies the most useful isoform for computational modelling, balancing the coverage of mutations of interest and the availability of templates to build a structural model of both the wild-type isoform and the related variants.
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Affiliation(s)
- Francesco Oliva
- Department of Physics, University of Cagliari, Monserrato (CA), Italy,Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany
| | - Francesco Musiani
- Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Alejandro Giorgetti
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,Department of Biotechnology, University of Verona, Verona, Italy
| | - Silvia De Rubeis
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Oksana Sorokina
- The School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Douglas J. Armstrong
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,The School of Informatics, University of Edinburgh, Edinburgh, United Kingdom,Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom
| | - Paolo Carloni
- Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany,Department of Physics, RWTH Aachen University, Aachen, Germany,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Paolo Ruggerone
- Department of Physics, University of Cagliari, Monserrato (CA), Italy,*Correspondence: Paolo Ruggerone,
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Lanjanian H, Hosseini S, Narimani Z, Meknatkhah S, Riazi GH. A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition. J Biomol Struct Dyn 2023; 41:11700-11713. [PMID: 36622367 DOI: 10.1080/07391102.2022.2163425] [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: 10/10/2022] [Accepted: 12/22/2022] [Indexed: 01/10/2023]
Abstract
The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hossein Lanjanian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shadi Hosseini
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran, Iran
| | - Zahra Narimani
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Sogol Meknatkhah
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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Abstract
Antibiotic resistance has caused a serious threat to public health and human safety. Recently, the emergence of novel resistance gene tet(X4) and its variants threatens the clinical utility of tigecycline, one of the last-line antibiotics for multidrug-resistant (MDR) bacterial infections. It is highly promising to develop effective antibiotic adjuvants to restore the clinical efficacy of existing drugs and extend their life spans. Metal compounds, such as silver, have been widely used as potential antimicrobial agents for decades. However, the potentiating effect of metallo-agents on the existing antibiotics is not fully understood. Here, we found that five bismuth drugs, especially bismuth nitrate [Bi(NO3)3], commonly used in clinical treatment of stomach-associated diseases, effectively boost the antibacterial activity of tigecycline against tet(X)-positive bacteria by inhibiting the enzymatic activity of Tet(X) protein. Furthermore, the combination of Bi(NO3)3 and tigecycline prevents the development of higher-level resistance in Tet(X)-expressing Gram-negative bacteria. Using molecular docking and dynamics simulation assays, we revealed that Bi(NO3)3 can competitively bind to the active center of Tet(X4) protein, while the bismuth atom targets the Tet(X4) protein in a noncompetitive manner and changes the structure of the primary binding pocket. These two mechanisms of action both antagonize the enzymatic activity of Tet(X4) resistance protein on tigecycline. Collectively, these findings indicate the high potential of bismuth drugs as novel Tet(X) inhibitors to treat tet(X4)-positive bacteria-associated infections in combination with tigecycline. IMPORTANCE Recently, high-level tigecycline resistance mediated by tet(X4) and its variants represents a serious challenge for global public health. Antibiotic adjuvant strategy that enhances the activity of the existing antibiotics by using nonantibiotic drugs offers a distinct approach to combat the antibiotic resistance crisis. In this study, we found that bismuth drugs involve bismuth nitrate, a compound previously approved for treatment of stomach-associated diseases, remarkably potentiates tigecycline activity against tet(X)-positive bacteria. Mechanistic studies showed that bismuth drugs effectively suppress the enzymatic activity of Tet(X) resistance protein. Specifically, bismuth nitrate targets the active center of Tet(X4) protein, while bismuth binds to the resistance protein in a noncompetitive manner. Our data open up a new horizon for the treatment of infections caused by tet(X)-bearing superbugs.
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