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Liang F, Sun M, Xie L, Zhao X, Liu D, Zhao K, Zhang G. Recent advances and challenges in protein complex model accuracy estimation. Comput Struct Biotechnol J 2024; 23:1824-1832. [PMID: 38707538 PMCID: PMC11066466 DOI: 10.1016/j.csbj.2024.04.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.
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Affiliation(s)
| | | | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xuanfeng Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Dong Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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2
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Araujo-Abad S, Rizzuti B, Soto-Conde L, Vidal M, Abian O, Velazquez-Campoy A, Neira JL, de Juan Romero C. Citrullinating enzyme PADI4 and transcriptional repressor RING1B bind in cancer cells. Int J Biol Macromol 2024; 274:133163. [PMID: 38878927 DOI: 10.1016/j.ijbiomac.2024.133163] [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: 04/23/2024] [Revised: 05/26/2024] [Accepted: 06/12/2024] [Indexed: 06/27/2024]
Abstract
Polycomb groups (PcGs) are transcriptional repressors, formed by a complex of several proteins, involved in multicellular development and cancer epigenetics. One of these proteins is the E3 ubiquitin-protein ligase RING1 (or RING1B), associated with the regulation of transcriptional repression and responsible for monoubiquitylation of the histone H2A. On the other hand, PADI4 is one of the human isoforms of a family of enzymes implicated in the conversion of arginine to citrulline, and it is also involved in the development of glioblastoma, among other types of cancers. In this work, we showed the association of PADI4 and RING1B in the nucleus and cytosol in several cancer cell lines by using immunofluorescence and proximity ligation assays. Furthermore, we demonstrated that binding was hampered in the presence of GSK484, an enzymatic PADI4 inhibitor, suggesting that RING1B could bind to the active site of PADI4, as confirmed by protein-protein docking simulations. In vitro and in silico findings showed that binding to PADI4 occurred for the isolated fragments corresponding to both the N-terminal (residues 1-221) and C-terminal (residues 228-336) regions of RING1B. Binding to PADI4 was also hampered by GSK484, as shown by isothermal titration calorimetry (ITC) experiments for the sole N-terminal region, and by both NMR and ITC for the C-terminal one. The dissociation constants between PADI4 and any of the two isolated RING1B fragments were in the low micromolar range (~2-10 μM), as measured by fluorescence and ITC. The interaction between RING1B and PADI4 might imply citrullination of the former, leading to several biological consequences, as well as being of potential therapeutic relevance for improving cancer treatment with the generation of new antigens.
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Affiliation(s)
- Salome Araujo-Abad
- Cancer Research Group, Faculty of Engineering and Applied Sciences, Universidad de Las Américas, 170124 Quito, Ecuador; IDIBE, Universidad Miguel Hernández, 03202 Elche (Alicante), Spain.
| | - Bruno Rizzuti
- CNR-NANOTEC, SS Rende (CS), Department of Physics, University of Calabria, 87036 Rende, Italy; Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain
| | | | - Miguel Vidal
- Centro de Investigaciones Biológicas Margarita Salas (CSIC), Calle Ramiro de Maeztu, 9, 28040 Madrid, Spain
| | - Olga Abian
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain; Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain; Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
| | - Adrian Velazquez-Campoy
- Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain; Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain; Centro de Investigación Biomédica en Red en el Área Temática de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain; Departamento de Bioquímica y Biología Molecular y Celular, Universidad de Zaragoza, 50009 Zaragoza, Spain
| | - José L Neira
- IDIBE, Universidad Miguel Hernández, 03202 Elche (Alicante), Spain; Institute of Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain.
| | - Camino de Juan Romero
- IDIBE, Universidad Miguel Hernández, 03202 Elche (Alicante), Spain; Unidad de Investigación, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO), Hospital General Universitario de Elche, Camí de l'Almazara 11, 03203 Elche (Alicante), Spain.
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3
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Gaglione SA, Rosales TJ, Schmidt-Hong L, Baker BM, Birnbaum ME. SARS-CoV-2 spike does not interact with the T cell receptor or directly activate T cells. Proc Natl Acad Sci U S A 2024; 121:e2406615121. [PMID: 39042676 PMCID: PMC11295079 DOI: 10.1073/pnas.2406615121] [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: 04/02/2024] [Accepted: 06/25/2024] [Indexed: 07/25/2024] Open
Abstract
Suggested edit: SARS-CoV-2infection can induce multisystem inflammatory syndrome in children, which resembles superantigen-induced toxic shock syndrome. Recent work has suggested that the SARS-CoV-2 spike (S) protein could act as a superantigen by binding T cell receptors (TCRs) and inducing broad antigen-independent T cell responses. Structure-based computational modeling identified potential TCR-binding sites near the S receptor-binding domain, in addition to a site with homology to known neurotoxins. We experimentally examined the mechanism underpinning this theory-the direct interaction between the TCR and S protein. Surface plasmon resonance of recombinantly expressed S protein and TCR revealed no detectable binding. Orthogonally, we pseudotyped lentiviruses with SARS-CoV-2 S in both wild-type and prefusion-stabilized forms, demonstrated their functionality in a cell line assay, and observed no transduction, activation, or stimulation of proliferation of CD8+ T cells. We conclude that it is unlikely that the SARS-CoV-2 spike protein engages nonspecifically with TCRs or has superantigenic character.
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Affiliation(s)
- Stephanie A. Gaglione
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Tatiana J. Rosales
- Department of Chemistry and Biochemistry, University of Notre Dame, South Bend, IN46556
- Harper Cancer Research Institute, University of Notre Dame, South Bend, IN46556
| | - Laura Schmidt-Hong
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Brian M. Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, South Bend, IN46556
- Harper Cancer Research Institute, University of Notre Dame, South Bend, IN46556
| | - Michael E. Birnbaum
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA02139
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4
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Mabry AR, Singh A, Mulrooney B, Gorman J, Thielbar AR, Wolf ER, Mayo LD. Induction of the Mdm2 gene and protein by kinase signaling pathways is repressed by the pVHL tumor suppressor. Proc Natl Acad Sci U S A 2024; 121:e2400935121. [PMID: 39047034 PMCID: PMC11295014 DOI: 10.1073/pnas.2400935121] [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: 01/15/2024] [Accepted: 06/14/2024] [Indexed: 07/27/2024] Open
Abstract
The tumor suppressor von Hippel-Lindau, pVHL, is a multifaceted protein. One function is to dock to the hypoxia-inducible transcription factor (HIF) and recruit a larger protein complex that destabilizes HIF via ubiquitination, preventing angiogenesis and tumor development. pVHL also binds to the tumor suppressor p53 to activate specific p53 target genes. The oncogene Mdm2 impairs the formation of the p53-pVHL complex and activation of downstream genes by conjugating nedd8 to pVHL. While Mdm2 can impact p53 and pVHL, how pVHL may impact Mdm2 is unclear. Like p53 somatic mutations, point mutations are evident in pVHL that are common in renal clear cell carcinomas (RCC). In patients with RCC, Mdm2 levels are elevated, and we examined whether there was a relationship between Mdm2 and pVHL. TCGA and DepMap analysis revealed that mdm2 gene expression was elevated in RCC with vhl point mutations or copy number loss. In pVHL reconstituted or deleted isogenetically match RCC or MEF cell lines, Mdm2 was decreased in the presence of pVHL. Furthermore, through analysis using genetic and pharmacological approaches, we show that pVHL represses Mdm2 gene expression by blocking the MAPK-Ets signaling pathway and blocks Akt-mediated phosphorylation and stabilization of Mdm2. Mdm2 inhibition results in an increase in the p53-p21 pathway to impede cell growth. This finding shows how pVHL can indirectly impact the function of Mdm2 by regulating signaling pathways to restrict cell growth.
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Affiliation(s)
- Alexander R. Mabry
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
| | - Arnima Singh
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
| | - Brianna Mulrooney
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
| | - James Gorman
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
| | - Abigail R. Thielbar
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
| | - Eric R. Wolf
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
- Department of Biochemistry and Molecular Biology, Indiana University, Indianapolis, IN46022
| | - Lindsey D. Mayo
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University, Indianapolis, IN46022
- Indiana University School of Medicine, Indianapolis, IN46022
- Indiana University Indianapolis, Indianapolis, IN46022
- Department of Biochemistry and Molecular Biology, Indiana University, Indianapolis, IN46022
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5
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Csikász-Nagy A, Fichó E, Noto S, Reguly I. Computational tools to predict context-specific protein complexes. Curr Opin Struct Biol 2024; 88:102883. [PMID: 38986166 DOI: 10.1016/j.sbi.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/21/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
Abstract
Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.
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Affiliation(s)
- Attila Csikász-Nagy
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | | | - Santiago Noto
- Cytocast Hungary Kft, Budapest, Hungary; Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - István Reguly
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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6
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Dapkūnas J, Timinskas A, Olechnovič K, Tomkuvienė M, Venclovas Č. PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions. Nucleic Acids Res 2024; 52:W264-W271. [PMID: 38619046 PMCID: PMC11223826 DOI: 10.1093/nar/gkae278] [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: 02/03/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 04/16/2024] Open
Abstract
Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologs and (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Albertas Timinskas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Miglė Tomkuvienė
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio av. 7, Vilnius LT-10257, Lithuania
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7
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Kousaka S, Ishikawa T. Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters. J Chem Theory Comput 2024; 20:5164-5175. [PMID: 38845143 DOI: 10.1021/acs.jctc.4c00531] [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: 06/26/2024]
Abstract
This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.
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Affiliation(s)
- Sumire Kousaka
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
| | - Takeshi Ishikawa
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
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8
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Rodríguez LR, Vicente de Julián-Ortiz J, Rubio de la Rúa F, Juste-Dolz A, Maquieira Á, Mohammad-Salim HA, Benmetir S, Pallardó FV, González-Cabo P, Gimenez-Romero D. Unveiling the Ro60-Ro52 complex. EXCLI JOURNAL 2024; 23:888-903. [PMID: 38983778 PMCID: PMC11231564 DOI: 10.17179/excli2024-7141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 06/04/2024] [Indexed: 07/11/2024]
Abstract
The coexistence within a subcellular complex of inter-cellular proteins Ro60, responsible for preserving ncRNA quality, and Ro52, involved in intracellular proteolysis, has been a subject of ongoing debate. Employing molecular docking in tandem with experimental methods like Quartz Crystal Microbalance with Dissipation (QCM-D), Proximity Ligation Assay (PLA), and Indirect Immunofluorescence (IIF), we reveal the presence of Ro60 associating with Ro52 within the cytoplasm. This result unveils the formation of a weak transient complex with a Ka ≈ (3.7 ± 0.3) x 106 M-1, where the toroid-shaped Ro60 structure interacts with the Ro52's Fc receptor, aligning horizontally within the PRY-SPRY domains of the Ro52's homodimer. The stability of this complex relies on the interaction between Ro52 chain A and specific Ro60 residues, such as K133, W177, or L185, vital in the Ro60-YRNA bond. These findings bridge the role of Ro60 in YRNA management with Ro52's function in intracellular proteolysis, emphasizing the potential impact of transient complexes on cellular pathways. See also the graphical abstract(Fig. 1).
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Affiliation(s)
- Laura R Rodríguez
- Department of Physiology, Faculty of Medicine and Dentistry. University of Valencia-INCLIVA, 46010 Valencia, Spain
- CIBER Rare Diseases (CIBERER), 46010 Valencia, Spain
- Present address: Stem Cells, Aging and Neurodegeneration Group, Department of Experimental Medical Science, Faculty of Medicine, Lund Stem Cell Center, Lund University, 22184, Lund, Sweden
| | - Jesus Vicente de Julián-Ortiz
- Molecular Topology and Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Av. Vicent Andrés Estellés s/n, 46100 Valencia, Spain
| | - Fernando Rubio de la Rúa
- Department of Physical Chemistry, Faculty of Chemistry, University of Valencia, C/ Doctor Moliner 50, 46100, Burjassot, Spain
| | - Augusto Juste-Dolz
- Center for Research and Innovation on Bioengineering (Ci2B), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Ángel Maquieira
- Departamento de Química, Polytechnic University of Valencia, Camino de Vera s/n 46022, Valencia, Spain
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Camino de Vera s/n, 46022 Valencia, Spain
| | - Haydar A Mohammad-Salim
- Molecular Topology and Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Av. Vicent Andrés Estellés s/n, 46100 Valencia, Spain
- Faculty of Science, Department of Chemistry, University of Zakho, Zakho, Duhok 42001, Kurdistan Region, Iraq
| | - Sofiane Benmetir
- Molecular Topology and Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Av. Vicent Andrés Estellés s/n, 46100 Valencia, Spain
- Process and Environmental Engineering Laboratory (LIPE), Faculty of Chemistry, University of Science and Technology of Oran Mohamed BOUDIAF, P.O. Box 1503, El Mnaouer, 31000 Oran, Algeria
| | - Federico V Pallardó
- Department of Physiology, Faculty of Medicine and Dentistry. University of Valencia-INCLIVA, 46010 Valencia, Spain
- CIBER Rare Diseases (CIBERER), 46010 Valencia, Spain
| | - Pilar González-Cabo
- Department of Physiology, Faculty of Medicine and Dentistry. University of Valencia-INCLIVA, 46010 Valencia, Spain
- CIBER Rare Diseases (CIBERER), 46010 Valencia, Spain
| | - David Gimenez-Romero
- Department of Physical Chemistry, Faculty of Chemistry, University of Valencia, C/ Doctor Moliner 50, 46100, Burjassot, Spain
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9
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Zhao N, Wu T, Wang W, Zhang L, Gong X. Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure. Interdiscip Sci 2024; 16:261-288. [PMID: 38955920 DOI: 10.1007/s12539-024-00626-x] [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: 10/26/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/04/2024]
Abstract
Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.
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Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Tong Wu
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Wenda Wang
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Lunchuan Zhang
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
| | - Xinqi Gong
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China.
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China.
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10
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Yang Q, Jin X, Zhou H, Ying J, Zou J, Liao Y, Lu X, Ge S, Yu H, Min X. SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models. Comput Biol Chem 2024; 110:108067. [PMID: 38714420 DOI: 10.1016/j.compbiolchem.2024.108067] [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: 01/18/2024] [Revised: 03/18/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.
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Affiliation(s)
- Qianli Yang
- Institute of Artifical Intelligence, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
| | - Xiaocheng Jin
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Haixia Zhou
- School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Junjie Ying
- Institute of Artifical Intelligence, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - JiaJun Zou
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Yiyang Liao
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Xiaoli Lu
- Information and Networking Center, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Shengxiang Ge
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China
| | - Hai Yu
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; School of Public Health, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
| | - Xiaoping Min
- School of Informatics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; National Institute of Diagnostics and Vaccine Development in Infectious Diseases, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
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11
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Zhao H, Petrey D, Murray D, Honig B. ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models. Proc Natl Acad Sci U S A 2024; 121:e2400260121. [PMID: 38743624 PMCID: PMC11127014 DOI: 10.1073/pnas.2400260121] [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: 01/08/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.
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Affiliation(s)
- Haiqing Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Donald Petrey
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
- Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY10032
- Department of Medicine, Columbia University, New York, NY10032
- Zuckerman Institute, Columbia University, New York, NY10027
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12
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Wesołowski P, Wales DJ, Pracht P. Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation. J Phys Chem B 2024; 128:3145-3156. [PMID: 38512062 PMCID: PMC11000224 DOI: 10.1021/acs.jpcb.4c00104] [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: 01/05/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents a distinct level of theory, where the initial layer, encompassing the entire protein, is modeled by a general all-atom force-field GFN-FF. An intermediate electronic structure layer consisting of three multicenter fragments is introduced with the state-of-the-art semiempirical tight-binding method GFN2-xTB. Higher accuracy, specifically addressing the breaking and formation of the three disulfide bonds, is achieved at the innermost layer using the composite DFT method r2SCAN-3c. Our analysis sheds light on the structural stability of BPTI, particularly the significance of interlinking disulfide bonds. The accuracy and efficiency of the multicenter QM/SQM/MM approach are benchmarked using the oxidative formation of cystine. For the folding pathway of BPTI, relative stabilities are investigated through the calculation of free energy contributions for selected intermediates, focusing on the impact of the disulfide bond. Our results highlight the intricate trade-off between accuracy and computational cost, demonstrating that the multicenter ONIOM approach provides a well-balanced and comprehensive solution to describe electronic structure effects in biomolecular systems. We conclude that multiscale energy landscape exploration provides a robust methodology for the study of intriguing biological targets.
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Affiliation(s)
- Patryk
A. Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - David J. Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Philipp Pracht
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
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13
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Lin P, Li H, Huang SY. Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches. Curr Opin Struct Biol 2024; 85:102789. [PMID: 38402744 DOI: 10.1016/j.sbi.2024.102789] [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: 09/30/2023] [Revised: 01/16/2024] [Accepted: 02/06/2024] [Indexed: 02/27/2024]
Abstract
Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in monomer protein structure prediction, deep learning has widely been applied to modeling protein-protein complex structures through inter-protein contact prediction and end-to-end approaches in the past few years. This article reviews the recent advances of deep-learning-based approaches in modeling protein-protein complex structures as well as their advantages and limitations. Challenges and possible future directions are also briefly discussed in applying deep learning for the prediction of protein complex structures.
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Affiliation(s)
- Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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14
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints. J Mol Biol 2024; 436:168486. [PMID: 38336197 PMCID: PMC10942765 DOI: 10.1016/j.jmb.2024.168486] [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: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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15
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [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: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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16
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Morehead A, Liu J, Cheng J. Protein structure accuracy estimation using geometry-complete perceptron networks. Protein Sci 2024; 33:e4932. [PMID: 38380738 PMCID: PMC10880424 DOI: 10.1002/pro.4932] [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: 07/22/2023] [Revised: 01/05/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024]
Abstract
Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally-predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry-complete perceptron network for protein structure EMA (GCPNet-EMA), where we demonstrate through rigorous computational benchmarks that GCPNet-EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground-truth measures of per-residue (per-target) structural accuracy compared to baseline state-of-the-art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet-EMA are available on GitHub, and a public web server implementation is freely available.
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Affiliation(s)
- Alex Morehead
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Jian Liu
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
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17
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Michalik I, Kuder KJ. Machine Learning Methods in Protein-Protein Docking. Methods Mol Biol 2024; 2780:107-126. [PMID: 38987466 DOI: 10.1007/978-1-0716-3985-6_7] [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: 07/12/2024]
Abstract
An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.
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Affiliation(s)
- Ilona Michalik
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland
| | - Kamil J Kuder
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
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18
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Zięba A, Matosiuk D. Sampling and Scoring in Protein-Protein Docking. Methods Mol Biol 2024; 2780:15-26. [PMID: 38987461 DOI: 10.1007/978-1-0716-3985-6_2] [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: 07/12/2024]
Abstract
Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.
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Affiliation(s)
- Agata Zięba
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland.
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
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19
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Olechnovič K, Valančauskas L, Dapkūnas J, Venclovas Č. Prediction of protein assemblies by structure sampling followed by interface-focused scoring. Proteins 2023; 91:1724-1733. [PMID: 37578163 DOI: 10.1002/prot.26569] [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: 03/08/2023] [Revised: 07/12/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Abstract
Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.
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Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lukas Valančauskas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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20
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Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 2023; 91:1747-1770. [PMID: 37876231 PMCID: PMC10841292 DOI: 10.1002/prot.26602] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/26/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
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Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
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21
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Lensink MF, Brysbaert G, Raouraoua N, Bates PA, Giulini M, Honorato RV, van Noort C, Teixeira JMC, Bonvin AMJJ, Kong R, Shi H, Lu X, Chang S, Liu J, Guo Z, Chen X, Morehead A, Roy RS, Wu T, Giri N, Quadir F, Chen C, Cheng J, Del Carpio CA, Ichiishi E, Rodriguez‐Lumbreras LA, Fernandez‐Recio J, Harmalkar A, Chu L, Canner S, Smanta R, Gray JJ, Li H, Lin P, He J, Tao H, Huang S, Roel‐Touris J, Jimenez‐Garcia B, Christoffer CW, Jain AJ, Kagaya Y, Kannan H, Nakamura T, Terashi G, Verburgt JC, Zhang Y, Zhang Z, Fujuta H, Sekijima M, Kihara D, Khan O, Kotelnikov S, Ghani U, Padhorny D, Beglov D, Vajda S, Kozakov D, Negi SS, Ricciardelli T, Barradas‐Bautista D, Cao Z, Chawla M, Cavallo L, Oliva R, Yin R, Cheung M, Guest JD, Lee J, Pierce BG, Shor B, Cohen T, Halfon M, Schneidman‐Duhovny D, Zhu S, Yin R, Sun Y, Shen Y, Maszota‐Zieleniak M, Bojarski KK, Lubecka EA, Marcisz M, Danielsson A, Dziadek L, Gaardlos M, Gieldon A, Liwo A, Samsonov SA, Slusarz R, Zieba K, Sieradzan AK, Czaplewski C, Kobayashi S, Miyakawa Y, Kiyota Y, Takeda‐Shitaka M, Olechnovic K, Valancauskas L, Dapkunas J, Venclovas C, Wallner B, Yang L, Hou C, He X, Guo S, Jiang S, Ma X, Duan R, Qui L, Xu X, Zou X, Velankar S, Wodak SJ. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment. Proteins 2023; 91:1658-1683. [PMID: 37905971 PMCID: PMC10841881 DOI: 10.1002/prot.26609] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 11/02/2023]
Abstract
We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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Affiliation(s)
- Marc F. Lensink
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Guillaume Brysbaert
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Nessim Raouraoua
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Paul A. Bates
- Biomolecular Modeling LaboratoryThe Francis Crick InstituteLondonUK
| | - Marco Giulini
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Rodrigo V. Honorato
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Charlotte van Noort
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Joao M. C. Teixeira
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Jian Liu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Zhiye Guo
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Xiao Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Alex Morehead
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Raj S. Roy
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Tianqi Wu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Nabin Giri
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Farhan Quadir
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Chen Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Jianlin Cheng
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | | | - Eichiro Ichiishi
- International University of Health and Welfare (IUHV Hospital)Nasushiobara‐CityJapan
| | - Luis A. Rodriguez‐Lumbreras
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Juan Fernandez‐Recio
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Ameya Harmalkar
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Lee‐Shin Chu
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Sam Canner
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Rituparna Smanta
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Hao Li
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Peicong Lin
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jiahua He
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Huanyu Tao
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Sheng‐You Huang
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jorge Roel‐Touris
- Protein Design and Modeling Lab, Dept. of Structural BiologyMolecular Biology Institute of Barcelona (IBMB‐CSIC)BarcelonaSpain
| | | | | | - Anika J. Jain
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuki Kagaya
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Harini Kannan
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | - Tsukasa Nakamura
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Genki Terashi
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Jacob C. Verburgt
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuanyuan Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Zicong Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Hayato Fujuta
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | | | - Daisuke Kihara
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | | | | | | | | | | | | | | | - Surendra S. Negi
- Sealy Center for Structural Biology and Molecular BiophysicsUniversity of Texas Medical BranchGalvestonTexasUSA
| | | | | | - Zhen Cao
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Mohit Chawla
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
- Department of Chemistry and BiologyUniversity of SalernoFiscianoItaly
| | | | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Chemistry and BiochemistryUniversity of MarylandCollege ParkMarylandUSA
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Jessica Lee
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Ben Shor
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Tomer Cohen
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Matan Halfon
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | | | - Shaowen Zhu
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Rujie Yin
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yuanfei Sun
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yang Shen
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTexasUSA
- Institute of Biosciences and Technology and Department of Translational Medical SciencesTexas A&M UniversityHoustonTexasUSA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yuta Miyakawa
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | - Yasuomi Kiyota
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | | | - Kliment Olechnovic
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Lukas Valancauskas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Justas Dapkunas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Ceslovas Venclovas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Bjorn Wallner
- Bioinformatics Division, Department of Physics, Chemistry, and BiologyLinkoping UniversityLinköpingSweden
| | - Lin Yang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- School of Aerospace, Mechanical and Mechatronic EngineeringThe University of SydneyNew South WalesAustralia
| | - Chengyu Hou
- School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
| | - Xiaodong He
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- Shenzhen STRONG Advanced Materials Research Institute Col, LtdShenzhenPeople's Republic of China
| | - Shuai Guo
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Shenda Jiang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Xiaoliang Ma
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Rui Duan
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Liming Qui
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xianjin Xu
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
- Dept. of Physics and AstronomyUniversity of MissouriColumbiaMissouriUSA
- Dept. of BiochemistryUniversity of MissouriColumbiaMissouriUSA
- Institute for Data Science and InformaticsUniversity of MissouriColumbiaMissouriUSA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonCambridgeUK
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22
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Wesołowski PA, Sieradzan AK, Winnicki MJ, Morgan JWR, Wales DJ. Energy landscapes for proteins described by the UNRES coarse-grained potential. Biophys Chem 2023; 303:107107. [PMID: 37862761 DOI: 10.1016/j.bpc.2023.107107] [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: 06/08/2023] [Revised: 08/27/2023] [Accepted: 09/04/2023] [Indexed: 10/22/2023]
Abstract
The self-assembly of proteins is encoded in the underlying potential energy surface (PES), from which we can predict structure, dynamics, and thermodynamic properties. However, the corresponding analysis becomes increasingly challenging with larger protein sizes, due to the computational time required, which grows significantly with the number of atoms. Coarse-grained models offer an attractive approach to reduce the computational cost. In this Feature Article, we describe our implementation of the UNited RESidue (UNRES) coarse-grained potential in the Cambridge energy landscapes software. We have applied this framework to explore the energy landscapes of four proteins that exhibit native states involving different secondary structures. Here we have tested the ability of the UNRES potential to represent the global energy landscape of proteins containing up to 100 amino acid residues. The resulting potential energy landscapes exhibit good agreement with experiment, with low-lying minima close to the PDB geometries and to results obtained using the all-atom AMBER force field. The new program interfaces will allow us to investigate larger biomolecules in future work, using the UNRES potential in combination with all the methodology available in the computational energy landscapes framework.
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Affiliation(s)
- Patryk A Wesołowski
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Adam K Sieradzan
- Faculty of Chemistry, Gdansk University, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Michał J Winnicki
- Faculty of Chemistry, Gdansk University, Wita Stwosza 63, 80-308 Gdańsk, Poland; Oklahoma Medical Research Foundation, 825 NE 13th St., Oklahoma City, OK 73104, USA; Intercollegiate Faculty of Biotechnology, University of Gdańsk and the Medical University of Gdańsk, Abrahama 58, 80-307 Gdańsk, Poland
| | - John W R Morgan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Downing College, University of Cambridge, Regent St., Cambridge CB2 1DQ, UK
| | - David J Wales
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
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23
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Roy RS, Liu J, Giri N, Guo Z, Cheng J. Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15. Proteins 2023; 91:1889-1902. [PMID: 37357816 PMCID: PMC10749984 DOI: 10.1002/prot.26542] [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: 03/11/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/27/2023]
Abstract
Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.
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Affiliation(s)
- Raj S. Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
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24
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Ozden B, Kryshtafovych A, Karaca E. The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15. Proteins 2023; 91:1636-1657. [PMID: 37861057 PMCID: PMC10873090 DOI: 10.1002/prot.26598] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
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Affiliation(s)
- Burcu Ozden
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
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25
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Pang M, He W, Lu X, She Y, Xie L, Kong R, Chang S. CoDock-Ligand: combined template-based docking and CNN-based scoring in ligand binding prediction. BMC Bioinformatics 2023; 24:444. [PMID: 37996806 PMCID: PMC10668353 DOI: 10.1186/s12859-023-05571-y] [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: 09/21/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
For ligand binding prediction, it is crucial for molecular docking programs to integrate template-based modeling with a precise scoring function. Here, we proposed the CoDock-Ligand docking method that combines template-based modeling and the GNINA scoring function, a Convolutional Neural Network-based scoring function, for the ligand binding prediction in CASP15. Among the 21 targets, we obtained successful predictions in top 5 submissions for 14 targets and partially successful predictions for 4 targets. In particular, for the most complicated target, H1114, which contains 56 metal cofactors and small molecules, our docking method successfully predicted the binding of most ligands. Analysis of the failed systems showed that the predicted receptor protein presented conformational changes in the backbone and side chains of the binding site residues, which may cause large structural deviations in the ligand binding prediction. In summary, our hybrid docking scheme was efficiently adapted to the ligand binding prediction challenges in CASP15.
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Affiliation(s)
- Mingwei Pang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China
| | - Wangqiu He
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China
| | - Xufeng Lu
- Primary Biotechnology Inc., Changzhou, 213125, Jiangsu, China
| | - Yuting She
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China.
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China.
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26
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Liu J, Guo Z, Wu T, Roy RS, Quadir F, Chen C, Cheng J. Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15. Commun Biol 2023; 6:1140. [PMID: 37949999 PMCID: PMC10638423 DOI: 10.1038/s42003-023-05525-3] [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: 06/14/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023] Open
Abstract
To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. MULTICOM_qa ranked 3rd among 26 CASP15 server predictors and MULTICOM_human ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first predictions submitted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 predictions submitted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the Foldseek Structure Alignment-based Multimer structure Generation (FSAMG) method outperforms the widely used sequence alignment-based multimer structure generation.
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Affiliation(s)
- Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Tianqi Wu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Chen Chen
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA.
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27
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563303. [PMID: 37961264 PMCID: PMC10634698 DOI: 10.1101/2023.10.20.563303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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28
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Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538330. [PMID: 37162955 PMCID: PMC10168427 DOI: 10.1101/2023.04.25.538330] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
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Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine,University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
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29
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Sarkar D, Lee H, Vant JW, Turilli M, Vermaas JV, Jha S, Singharoy A. Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting. J Chem Inf Model 2023; 63:5834-5846. [PMID: 37661856 DOI: 10.1021/acs.jcim.3c00350] [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: 09/05/2023]
Abstract
Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge-driven or artificial intelligence-driven and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models that contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly while refining them against the density maps. We introduce such an adaptive decision-making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two prototypical protein systems, adenylate kinase and carbon monoxide dehydrogenase. For these test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2-3 Å map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that are inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from the chimpanzee adenovirus. The overhead for decision making remains low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install R-MDFF on different computing environments, from local Linux-based workstations to high-performance computing environments.
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Affiliation(s)
- Daipayan Sarkar
- MSU-DOE Plant Research Laboratory, East Lansing, Michigan 48824, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Hyungro Lee
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
| | - John W Vant
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Matteo Turilli
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Josh V Vermaas
- MSU-DOE Plant Research Laboratory, East Lansing, Michigan 48824, United States
| | - Shantenu Jha
- Electrical & Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85281, United States
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30
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Ozden B, Kryshtafovych A, Karaca E. The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548341. [PMID: 37503072 PMCID: PMC10369898 DOI: 10.1101/2023.07.10.548341] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
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Affiliation(s)
- Burcu Ozden
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir, Türkiye
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Türkiye
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31
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Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LÁ, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin A, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson H, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Kihara D, Marchand A, Correia B, Zou X, Dey S, Dunbrack R, Levy E, Wodak S. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 2023; 23:e2200323. [PMID: 37365936 PMCID: PMC10937251 DOI: 10.1002/pmic.202200323] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Julia K. Varga
- Hebrew University of Jerusalem Institute for Medical Research Israel-Canada
| | | | | | | | | | | | | | - Jérôme Tubiana
- Tel Aviv University Blavatnik School of Computer Science
| | - Haim Wolfson
- Tel Aviv University Blavatnik School of Computer Science
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Institute for Data Science and Informatics, University of Missouri
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32
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Chen Z, Liu N, Huang Y, Min X, Zeng X, Ge S, Zhang J, Xia N. PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3128-3138. [PMID: 37220029 DOI: 10.1109/tcbb.2023.3279019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.
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33
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Christoffer C, Kihara D. Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD. Proteomics 2023; 23:e2200322. [PMID: 36529945 PMCID: PMC10448949 DOI: 10.1002/pmic.202200322] [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: 11/01/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
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34
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Hashemi N, Hao B, Ignatov M, Paschalidis IC, Vakili P, Vajda S, Kozakov D. Improved prediction of MHC-peptide binding using protein language models. FRONTIERS IN BIOINFORMATICS 2023; 3:1207380. [PMID: 37663788 PMCID: PMC10469926 DOI: 10.3389/fbinf.2023.1207380] [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: 04/17/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
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Affiliation(s)
- Nasser Hashemi
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Ioannis Ch. Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Pirooz Vakili
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Sandor Vajda
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Department of Chemistry, Boston University, Boston, MA, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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35
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Petrey D, Zhao H, Trudeau SJ, Murray D, Honig B. PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions. J Mol Biol 2023; 435:168052. [PMID: 36933822 PMCID: PMC10293085 DOI: 10.1016/j.jmb.2023.168052] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023]
Abstract
We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.
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Affiliation(s)
- Donald Petrey
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Haiqing Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Stephen J Trudeau
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Medicine, Columbia University, New York, NY 10032, USA; Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA.
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36
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Puławski W, Koliński A, Koliński M. Integrative modeling of diverse protein-peptide systems using CABS-dock. PLoS Comput Biol 2023; 19:e1011275. [PMID: 37405984 DOI: 10.1371/journal.pcbi.1011275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/15/2023] [Indexed: 07/07/2023] Open
Abstract
The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.
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Affiliation(s)
- Wojciech Puławski
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | | | - Michał Koliński
- Bioinformatics Laboratory, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
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37
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Choi J. Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner. iScience 2023; 26:106911. [PMID: 37305691 PMCID: PMC10250834 DOI: 10.1016/j.isci.2023.106911] [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: 04/14/2023] [Revised: 04/28/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023] Open
Abstract
Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.
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Affiliation(s)
- Juyoung Choi
- Department of Life Science, Sogang University, Seoul 04017, South Korea
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38
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Liu J, Guo Z, Wu T, Roy RS, Quadir F, Chen C, Cheng J. Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541055. [PMID: 37293073 PMCID: PMC10245707 DOI: 10.1101/2023.05.16.541055] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditional sequence alignments and new Foldseek-based structure alignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. Our server (MULTICOM_qa) ranked 3rd among 26 CASP15 server predictors and our human predictor (MULTICOM_human) ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first models predicted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 models predicted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the novel Foldseek Structure Alignment-based Model Generation (FSAMG) method based on AlphaFold-Multimer outperforms the widely used sequence alignment-based model generation. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3.
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Affiliation(s)
- Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Tianqi Wu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Raj S. Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Chen Chen
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
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39
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Wodak SJ, Vajda S, Lensink MF, Kozakov D, Bates PA. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annu Rev Biophys 2023; 52:183-206. [PMID: 36626764 PMCID: PMC10885158 DOI: 10.1146/annurev-biophys-102622-084607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
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Affiliation(s)
- Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium;
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA;
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Marc F Lensink
- Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France;
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA;
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom;
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40
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Krapp LF, Abriata LA, Cortés Rodriguez F, Dal Peraro M. PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces. Nat Commun 2023; 14:2175. [PMID: 37072397 PMCID: PMC10113261 DOI: 10.1038/s41467-023-37701-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/28/2023] [Indexed: 04/20/2023] Open
Abstract
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their binding interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model-the Protein Structure Transformer, PeSTo-surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology.
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Affiliation(s)
- Lucien F Krapp
- Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, 1015, Switzerland
| | - Luciano A Abriata
- Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, 1015, Switzerland
| | - Fabio Cortés Rodriguez
- Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, 1015, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne, 1015, Switzerland.
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41
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Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
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Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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Varadi M, Bordin N, Orengo C, Velankar S. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr Opin Struct Biol 2023; 79:102543. [PMID: 36807079 DOI: 10.1016/j.sbi.2023.102543] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/21/2023]
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
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Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK. https://twitter.com/nicolabordin
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Roy RS, Liu J, Giri N, Guo Z, Cheng J. Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.08.531814. [PMID: 36945536 PMCID: PMC10028888 DOI: 10.1101/2023.03.08.531814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA. The source code of MULTICOM_qa is available at https://github.com/BioinfoMachineLearning/MULTICOM_qa .
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Affiliation(s)
- Raj S. Roy
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Nabin Giri
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA
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Computational prediction of disordered binding regions. Comput Struct Biotechnol J 2023; 21:1487-1497. [PMID: 36851914 PMCID: PMC9957716 DOI: 10.1016/j.csbj.2023.02.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.
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Petrey D, Zhao H, Trudeau S, Murray D, Honig B. PrePPI: A structure informed proteome-wide database of protein-protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530276. [PMID: 36909476 PMCID: PMC10002632 DOI: 10.1101/2023.02.27.530276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome. Graphic Abstract
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Rogers JR, Nikolényi G, AlQuraishi M. Growing ecosystem of deep learning methods for modeling protein-protein interactions. Protein Eng Des Sel 2023; 36:gzad023. [PMID: 38102755 DOI: 10.1093/protein/gzad023] [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: 10/10/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.
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Affiliation(s)
- Julia R Rogers
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Gergő Nikolényi
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
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Kurisaki I, Suzuki M. Simulation toolkits at the molecular scale for trans-scale thermal signaling. Comput Struct Biotechnol J 2023; 21:2547-2557. [PMID: 37102156 PMCID: PMC10123322 DOI: 10.1016/j.csbj.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/28/2023] Open
Abstract
Thermogenesis is a physiological activity of releasing heat that originates from intracellular biochemical reactions. Recent experimental studies discovered that externally applied heat changes intracellular signaling locally, resulting in global changes in cell morphology and signaling. Therefore, we hypothesize an inevitable contribution of thermogenesis in modulating biological system functions throughout the spatial scales from molecules to individual organisms. One key issue examining the hypothesis, namely, the "trans-scale thermal signaling," resides at the molecular scale on the amount of heat released via individual reactions and by which mechanism the heat is employed for cellular function operations. This review introduces atomistic simulation tool kits for studying the mechanisms of thermal signaling processes at the molecular scale that even state-of-the-art experimental methodologies of today are hardly accessible. We consider biological processes and biomolecules as potential heat sources in cells, such as ATP/GTP hydrolysis and multiple biopolymer complex formation and disassembly. Microscopic heat release could be related to mesoscopic processes via thermal conductivity and thermal conductance. Additionally, theoretical simulations to estimate these thermal properties in biological membranes and proteins are introduced. Finally, we envisage the future direction of this research field.
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Affiliation(s)
- Ikuo Kurisaki
- Waseda Research Institute for Science and Engineering, Waseda University, Bldg. No.55, S Tower, 4th Floor, 3–4-1 Okubo Shinjuku-ku, Tokyo 169–8555, Japan
- Corresponding authors.
| | - Madoka Suzuki
- Institute for Protein Research, Osaka University, 3–2 Yamadaoka, Suita, Osaka 565–0871, Japan
- Corresponding authors.
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Harini K, Christoffer C, Gromiha MM, Kihara D. Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server. Methods Mol Biol 2023; 2690:355-373. [PMID: 37450159 PMCID: PMC10561630 DOI: 10.1007/978-1-0716-3327-4_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.
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Affiliation(s)
- Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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Réau M, Renaud N, Xue LC, Bonvin AMJJ. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. Bioinformatics 2022; 39:6845451. [PMID: 36420989 PMCID: PMC9805592 DOI: 10.1093/bioinformatics/btac759] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 10/19/2022] [Accepted: 11/23/2022] [Indexed: 11/25/2022] Open
Abstract
MOTIVATION Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. RESULTS We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. AVAILABILITY AND IMPLEMENTATION DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Li C Xue
- Center for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen 6525 GA, The Netherlands
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Christoffer C, Kihara D. Domain-Based Protein Docking with Extremely Large Conformational Changes. J Mol Biol 2022; 434:167820. [PMID: 36089054 PMCID: PMC9992458 DOI: 10.1016/j.jmb.2022.167820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
Abstract
Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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