1
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Yehorova D, Crean RM, Kasson PM, Kamerlin SCL. Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN. Faraday Discuss 2024. [PMID: 38842247 DOI: 10.1039/d4fd00018h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Noncovalent interaction networks provide a powerful means to represent and analyze protein structure. Such networks can represent both static structures and dynamic conformational ensembles. We have recently developed two tools for analyzing such interaction networks and generating hypotheses for protein engineering. Here, we apply these tools to the conformational regulation of substrate specificity in class A β-lactamases, particularly the evolutionary development from generalist to specialist catalytic function and how that can be recapitulated or reversed by protein engineering. These tools, KIF and KIN, generate a set of prioritized residues and interactions as targets for experimental protein engineering.
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Affiliation(s)
- Dariia Yehorova
- School of Chemistry and Biochemistry, Georgia Institute of Technology, USA.
| | - Rory M Crean
- Department of Chemistry-BMC, Uppsala University, Sweden
| | - Peter M Kasson
- Department of Biomedical Engineering, University of Virginia, USA
- Department of Cell and Molecular Biology, Uppsala University, Sweden
- Departments of Chemistry & Biochemistry and Biomedical Engineering, Georgia Institute of Technology, USA.
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2
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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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3
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Balasco N, Damaggio G, Esposito L, Colonna V, Vitagliano L. A comprehensive analysis of SARS-CoV-2 missense mutations indicates that all possible amino acid replacements in the viral proteins occurred within the first two-and-a-half years of the pandemic. Int J Biol Macromol 2024; 266:131054. [PMID: 38522702 DOI: 10.1016/j.ijbiomac.2024.131054] [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: 01/25/2024] [Revised: 02/27/2024] [Accepted: 03/08/2024] [Indexed: 03/26/2024]
Abstract
The surveillance of COVID-19 pandemic has led to the determination of millions of genome sequences of the SARS-CoV-2 virus, with the accumulation of a wealth of information never collected before for an infectious disease. Exploring the information retrieved from the GISAID database reporting at that time >13 million genome sequences, we classified the 141,639 unique missense mutations detected in the first two-and-a-half years (up to October 2022) of the pandemic. Notably, our analysis indicates that 98.2 % of all possible conservative amino acid replacements occurred. Even non-conservative mutations were highly represented (73.9 %). For a significant number of residues (3 %), all possible replacements with the other nineteen amino acids have been observed. These observations strongly indicate that, in this time interval, the virus explored all possible alternatives in terms of missense mutations for all sites of its polypeptide chain and that those that are not observed severely affect SARS-CoV-2 integrity. The implications of the present findings go well beyond the structural biology of SARS-CoV-2 as the huge amount of information here collected and classified may be valuable for the elucidation of the sequence-structure-function relationships in proteins.
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Affiliation(s)
- Nicole Balasco
- Institute of Molecular Biology and Pathology, CNR c/o Dep. Chemistry, Sapienza University of Rome, Rome, Italy.
| | - Gianluca Damaggio
- Institute of Genetics and Biophysics, CNR, Naples, Italy; Laboratory of Stem Cell Biology and Pharmacology of Neurodegenerative Diseases, Department of Biosciences, University of Milan, Milan, Italy; University of Naples Federico II, Naples, Italy
| | | | - Vincenza Colonna
- Institute of Genetics and Biophysics, CNR, Naples, Italy; Department of Genetics, Genomics and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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4
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Humphreys IR, Zhang J, Baek M, Wang Y, Krishnakumar A, Pei J, Anishchenko I, Tower CA, Jackson BA, Warrier T, Hung DT, Peterson SB, Mougous JD, Cong Q, Baker D. Essential and virulence-related protein interactions of pathogens revealed through deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589144. [PMID: 38645026 PMCID: PMC11030334 DOI: 10.1101/2024.04.12.589144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.
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5
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Si Y, Yan C. Protein language model-embedded geometric graphs power inter-protein contact prediction. eLife 2024; 12:RP92184. [PMID: 38564241 PMCID: PMC10987090 DOI: 10.7554/elife.92184] [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: 04/04/2024] Open
Abstract
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.
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Affiliation(s)
- Yunda Si
- School of Physics, Huazhong University of Science and TechnologyWuhanChina
| | - Chengfei Yan
- School of Physics, Huazhong University of Science and TechnologyWuhanChina
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6
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Guo L, Wang J. GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking. Brief Bioinform 2024; 25:bbae201. [PMID: 38706316 PMCID: PMC11070652 DOI: 10.1093/bib/bbae201] [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: 07/08/2023] [Revised: 02/05/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024] Open
Abstract
Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.
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Affiliation(s)
- Linyuan Guo
- School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
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7
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Fang T, Szklarczyk D, Hachilif R, von Mering C. Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration. Sci Rep 2024; 14:6009. [PMID: 38472223 PMCID: PMC10933411 DOI: 10.1038/s41598-024-55655-9] [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: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Protein-protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates-thus reducing false positives as well as computation time.
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Affiliation(s)
- Tao Fang
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Radja Hachilif
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Christian von Mering
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
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8
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Martin J. AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility. J Chem Inf Model 2024; 64:1473-1480. [PMID: 38373070 DOI: 10.1021/acs.jcim.3c01805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Predicting whether two proteins physically interact is one of the holy grails of computational biology, galvanized by rapid advancements in deep learning. AlphaFold2, although not developed with this goal, is promising in this respect. Here, I test the prediction capability of AlphaFold2 on a very challenging data set, where proteins are structurally compatible, even when they do not interact. AlphaFold2 achieves high discrimination between interacting and non-interacting proteins, and the cases of misclassifications can either be rescued by revisiting the input sequences or can suggest false positives and negatives in the data set. AlphaFold2 is thus not impaired by the compatibility between protein structures and has the potential to be applied on a large scale.
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Affiliation(s)
- Juliette Martin
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS UMR 5239, Inserm U1293, University Claude Bernard Lyon 1, 69364, Lyon, France
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9
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Yehorova D, Crean RM, Kasson PM, Kamerlin SCL. Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families. Protein Sci 2024; 33:e4911. [PMID: 38358258 PMCID: PMC10868456 DOI: 10.1002/pro.4911] [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: 11/03/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.
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Affiliation(s)
- Dariia Yehorova
- School of Chemistry and Biochemistry, Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Rory M. Crean
- Department of Chemistry—BMCUppsala UniversityUppsalaSweden
| | - Peter M. Kasson
- Department of Molecular PhysiologyUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department of Cell and Molecular BiologyUppsala UniversityUppsalaSweden
| | - Shina C. L. Kamerlin
- School of Chemistry and Biochemistry, Georgia Institute of TechnologyAtlantaGeorgiaUSA
- Department of Chemistry—BMCUppsala UniversityUppsalaSweden
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10
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Xia Y, Zhao K, Liu D, Zhou X, Zhang G. Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning. Commun Biol 2023; 6:1221. [PMID: 38040847 PMCID: PMC10692239 DOI: 10.1038/s42003-023-05610-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023] Open
Abstract
Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
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Affiliation(s)
- Yuhao Xia
- College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China
| | - Kailong 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
| | - Xiaogen Zhou
- 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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11
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Bryant P, Elofsson A. Peptide binder design with inverse folding and protein structure prediction. Commun Chem 2023; 6:229. [PMID: 37880344 PMCID: PMC10600234 DOI: 10.1038/s42004-023-01029-7] [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: 04/25/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with ≥80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD ≤ 2 Å) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples.
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Affiliation(s)
- Patrick Bryant
- Science for Life Laboratory, 172 21, Solna, Sweden
- Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Arne Elofsson
- Science for Life Laboratory, 172 21, Solna, Sweden.
- Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden.
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12
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Jia K, Kilinc M, Jernigan RL. New alignment method for remote protein sequences by the direct use of pairwise sequence correlations and substitutions. FRONTIERS IN BIOINFORMATICS 2023; 3:1227193. [PMID: 37900964 PMCID: PMC10602800 DOI: 10.3389/fbinf.2023.1227193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/14/2023] [Indexed: 10/31/2023] Open
Abstract
Understanding protein sequences and how they relate to the functions of proteins is extremely important. One of the most basic operations in bioinformatics is sequence alignment and usually the first things learned from these are which positions are the most conserved and often these are critical parts of the structure, such as enzyme active site residues. In addition, the contact pairs in a protein usually correspond closely to the correlations between residue positions in the multiple sequence alignment, and these usually change in a systematic and coordinated way, if one position changes then the other member of the pair also changes to compensate. In the present work, these correlated pairs are taken as anchor points for a new type of sequence alignment. The main advantage of the method here is its combining the remote homolog detection from our method PROST with pairwise sequence substitutions in the rigorous method from Kleinjung et al. We show a few examples of some resulting sequence alignments, and how they can lead to improvements in alignments for function, even for a disordered protein.
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Affiliation(s)
- Kejue Jia
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA, United States
| | - Mesih Kilinc
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA, United States
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States
| | - Robert L. Jernigan
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA, United States
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States
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13
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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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14
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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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15
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O'Reilly FJ, Graziadei A, Forbrig C, Bremenkamp R, Charles K, Lenz S, Elfmann C, Fischer L, Stülke J, Rappsilber J. Protein complexes in cells by AI-assisted structural proteomics. Mol Syst Biol 2023; 19:e11544. [PMID: 36815589 PMCID: PMC10090944 DOI: 10.15252/msb.202311544] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.
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Affiliation(s)
- Francis J O'Reilly
- Chair of BioanalyticsTechnische Universität BerlinBerlinGermany
- Present address:
Center for Structural Biology, Center for Cancer ResearchNational Cancer Institute (NCI)FrederickMDUSA
| | | | | | - Rica Bremenkamp
- Department of General Microbiology, Institute of Microbiology and GeneticsAugust‐University GöttingenGöttingenGermany
| | | | - Swantje Lenz
- Chair of BioanalyticsTechnische Universität BerlinBerlinGermany
| | - Christoph Elfmann
- Department of General Microbiology, Institute of Microbiology and GeneticsAugust‐University GöttingenGöttingenGermany
| | - Lutz Fischer
- Chair of BioanalyticsTechnische Universität BerlinBerlinGermany
| | - Jörg Stülke
- Department of General Microbiology, Institute of Microbiology and GeneticsAugust‐University GöttingenGöttingenGermany
| | - Juri Rappsilber
- Chair of BioanalyticsTechnische Universität BerlinBerlinGermany
- Wellcome Centre for Cell BiologyUniversity of EdinburghEdinburghUK
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16
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Bryant P. Deep learning for protein complex structure prediction. Curr Opin Struct Biol 2023; 79:102529. [PMID: 36731337 DOI: 10.1016/j.sbi.2023.102529] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/10/2022] [Accepted: 12/20/2022] [Indexed: 02/04/2023]
Abstract
Recent developments in the structure prediction of protein complexes have resulted in accuracies rivalling experimental methods in many cases. The high accuracy is mainly observed in dimeric complexes and other problems such as protein disorder and predicting the structure of host-pathogen interactions remain. This review highlights the foundation for current accurate structure prediction of protein complexes and possible ways to address the remaining limitations.
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Affiliation(s)
- Patrick Bryant
- Science for Life Laboratory, 172 21 Solna, Sweden; Department of Biochemistry and Biophysics, Stockholm University, 106 91 Stockholm, Sweden.
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17
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Gandarilla-Pérez CA, Pinilla S, Bitbol AF, Weigt M. Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins. PLoS Comput Biol 2023; 19:e1011010. [PMID: 36996234 PMCID: PMC10089317 DOI: 10.1371/journal.pcbi.1011010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 04/11/2023] [Accepted: 03/08/2023] [Indexed: 04/01/2023] Open
Abstract
Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.
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Affiliation(s)
- Carlos A Gandarilla-Pérez
- Facultad de Física, Universidad de la Habana, San Lázaro y L, Vedado, Habana, Cuba
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative (LCQB, UMR 7238), Paris, France
| | - Sergio Pinilla
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative (LCQB, UMR 7238), Paris, France
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire Jean Perrin (UMR 8237), Paris, France
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martin Weigt
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative (LCQB, UMR 7238), Paris, France
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18
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Si Y, Yan C. Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models. Brief Bioinform 2023; 24:7033302. [PMID: 36759333 DOI: 10.1093/bib/bbad039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
The knowledge of contacting residue pairs between interacting proteins is very useful for the structural characterization of protein-protein interactions (PPIs). However, accurately identifying the tens of contacting ones from hundreds of thousands of inter-protein residue pairs is extremely challenging, and performances of the state-of-the-art inter-protein contact prediction methods are still quite limited. In this study, we developed a deep learning method for inter-protein contact prediction, which is referred to as DRN-1D2D_Inter. Specifically, we employed pretrained protein language models to generate structural information-enriched input features to residual networks formed by dimensional hybrid residual blocks to perform inter-protein contact prediction. Extensively bechmarking DRN-1D2D_Inter on multiple datasets, including both heteromeric PPIs and homomeric PPIs, we show DRN-1D2D_Inter consistently and significantly outperformed two state-of-the-art inter-protein contact prediction methods, including GLINTER and DeepHomo, although both the latter two methods leveraged the native structures of interacting proteins in the prediction, and DRN-1D2D_Inter made the prediction purely from sequences. We further show that applying the predicted contacts as constraints for protein-protein docking can significantly improve its performance for protein complex structure prediction.
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Affiliation(s)
- Yunda Si
- School of Physics, Huazhong University of Science and Technology, China
| | - Chengfei Yan
- School of Physics, Huazhong University of Science and Technology, China
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19
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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20
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Towards a structurally resolved human protein interaction network. Nat Struct Mol Biol 2023; 30:216-225. [PMID: 36690744 PMCID: PMC9935395 DOI: 10.1038/s41594-022-00910-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 12/14/2022] [Indexed: 01/25/2023]
Abstract
Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.
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21
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Dietler N, Lupo U, Bitbol AF. Impact of phylogeny on structural contact inference from protein sequence data. J R Soc Interface 2023; 20:20220707. [PMID: 36751926 PMCID: PMC9905998 DOI: 10.1098/rsif.2022.0707] [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] [Indexed: 02/09/2023] Open
Abstract
Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ancestry, their sequences also feature phylogenetic correlations, which can impair contact inference. We investigate this effect by generating controlled synthetic data from a minimal model where the importance of contacts and of phylogeny can be tuned. We demonstrate that global inference methods, specifically Potts models, are more resilient to phylogenetic correlations than local methods, based on covariance or mutual information. This holds whether or not phylogenetic corrections are used, and may explain the success of global methods. We analyse the roles of selection strength and of phylogenetic relatedness. We show that sites that mutate early in the phylogeny yield false positive contacts. We consider natural data and realistic synthetic data, and our findings generalize to these cases. Our results highlight the impact of phylogeny on contact prediction from protein sequences and illustrate the interplay between the rich structure of biological data and inference.
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Affiliation(s)
- Nicola Dietler
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Umberto Lupo
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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22
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Kim D, Ha D, Lee K, Lee H, Kim I, Kim S. An evolution-based machine learning to identify cancer type-specific driver mutations. Brief Bioinform 2023; 24:6961611. [PMID: 36575568 DOI: 10.1093/bib/bbac593] [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: 06/22/2022] [Revised: 11/18/2022] [Accepted: 12/03/2022] [Indexed: 12/29/2022] Open
Abstract
Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.
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Affiliation(s)
| | | | | | | | - Inhae Kim
- ImmunoBiome Inc., Pohang, South Korea
| | - Sanguk Kim
- Department of Life Sciences.,Artificial Intelligence Graduate Program, Pohang University of Science and Technology, Pohang 790-784, South Korea.,Institute of Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 120-149, South Korea
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23
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Launay R, Teppa E, Esque J, André I. Modeling Protein Complexes and Molecular Assemblies Using Computational Methods. Methods Mol Biol 2023; 2553:57-77. [PMID: 36227539 DOI: 10.1007/978-1-0716-2617-7_4] [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: 06/16/2023]
Abstract
Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.
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Affiliation(s)
- Romain Launay
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Elin Teppa
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Jérémy Esque
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
| | - Isabelle André
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
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24
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Wang L, Li FL, Ma XY, Cang Y, Bai F. PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method. J Chem Inf Model 2022; 62:6160-6171. [PMID: 36448715 DOI: 10.1021/acs.jcim.2c01033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.
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Affiliation(s)
| | | | | | | | - Fang Bai
- Shanghai Clinical Research and Trial Center, Shanghai201210, China
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25
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Guo Z, Liu J, Skolnick J, Cheng J. Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks. Nat Commun 2022; 13:6963. [PMID: 36379943 PMCID: PMC9666547 DOI: 10.1038/s41467-022-34600-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] Open
Abstract
Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo's 37.40% and 23.08% and GLINTER's 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER's 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.
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Affiliation(s)
- Zhiye Guo
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Jian Liu
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Jeffrey Skolnick
- grid.213917.f0000 0001 2097 4943School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-200 USA
| | - Jianlin Cheng
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA
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26
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Wu C, Guo D. Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants. Int J Mol Sci 2022; 23:ijms232012688. [PMID: 36293547 PMCID: PMC9604239 DOI: 10.3390/ijms232012688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/16/2022] Open
Abstract
Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein-protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis.
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27
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Pei J, Zhang J, Cong Q. Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling. Bioinformatics 2022; 38:4301-4311. [PMID: 35881696 DOI: 10.1093/bioinformatics/btac527] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 05/27/2022] [Accepted: 07/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. RESULTS We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. AVAILABILITY AND IMPLEMENTATION Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.,Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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28
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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29
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Zaydman MA, Little AS, Haro F, Aksianiuk V, Buchser WJ, DiAntonio A, Gordon JI, Milbrandt J, Raman AS. Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes. eLife 2022; 11:e74104. [PMID: 35976223 PMCID: PMC9427106 DOI: 10.7554/elife.74104] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 08/17/2022] [Indexed: 11/25/2022] Open
Abstract
Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.
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Affiliation(s)
- Mark A Zaydman
- Department of Pathology and Immunology, Washington University School of MedicineSt LouisUnited States
| | | | - Fidel Haro
- Duchossois Family Institute, University of ChicagoChicagoUnited States
| | | | - William J Buchser
- Department of Genetics, Washington University School of MedicineSt LouisUnited States
| | - Aaron DiAntonio
- Department of Developmental Biology, Washington University School of MedicineSt LouisUnited States
| | - Jeffrey I Gordon
- Department of Pathology and Immunology, Washington University School of MedicineSt LouisUnited States
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of MedicineSt LouisUnited States
| | - Jeffrey Milbrandt
- Department of Genetics, Washington University School of MedicineSt LouisUnited States
| | - Arjun S Raman
- Duchossois Family Institute, University of ChicagoChicagoUnited States
- Department of Pathology, University of Chicago, ChicagoChicagoUnited States
- Center for the Physics of Evolving Systems, University of Chicago, ChicagoChicagoUnited States
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30
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Modeling of protein conformational changes with Rosetta guided by limited experimental data. Structure 2022; 30:1157-1168.e3. [PMID: 35597243 PMCID: PMC9357069 DOI: 10.1016/j.str.2022.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/08/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022]
Abstract
Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.
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31
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Zhang W, Meng Q, Wang J, Guo F. HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy. Brief Bioinform 2022; 23:6599074. [PMID: 35653713 DOI: 10.1093/bib/bbac169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/07/2022] [Accepted: 04/16/2022] [Indexed: 11/12/2022] Open
Abstract
Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue-residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human-virus protein-protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein-protein interaction mechanisms.
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Affiliation(s)
- Wei Zhang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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32
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Gerardos A, Dietler N, Bitbol AF. Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences. PLoS Comput Biol 2022; 18:e1010147. [PMID: 35576238 PMCID: PMC9135348 DOI: 10.1371/journal.pcbi.1010147] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/26/2022] [Accepted: 04/27/2022] [Indexed: 11/19/2022] Open
Abstract
Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.
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Affiliation(s)
- Andonis Gerardos
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicola Dietler
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- * E-mail:
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33
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Ding D, Green AG, Wang B, Lite TLV, Weinstein EN, Marks DS, Laub MT. Co-evolution of interacting proteins through non-contacting and non-specific mutations. Nat Ecol Evol 2022; 6:590-603. [PMID: 35361892 PMCID: PMC9090974 DOI: 10.1038/s41559-022-01688-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 01/31/2022] [Indexed: 01/08/2023]
Abstract
Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact and promote tolerance non-specifically to many different antitoxin mutations, despite covariation in homologues occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.
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Affiliation(s)
- David Ding
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Anna G Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Boyuan Wang
- Department of Pharmacology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Thuy-Lan Vo Lite
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Michael T Laub
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
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34
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Li LP, Zhang B, Cheng L. CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information. Front Genet 2022; 13:857839. [PMID: 35360876 PMCID: PMC8963800 DOI: 10.3389/fgene.2022.857839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 02/10/2022] [Indexed: 11/22/2022] Open
Abstract
Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.
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Affiliation(s)
- Li-Ping Li
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, China
- Xinjiang Key Laboratory of Grassland Resources and Ecology, Urumqi, China
- *Correspondence: Li-Ping Li, ; Bo Zhang,
| | - Bo Zhang
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, China
- Xinjiang Key Laboratory of Grassland Resources and Ecology, Urumqi, China
- *Correspondence: Li-Ping Li, ; Bo Zhang,
| | - Li Cheng
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
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35
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Improved prediction of protein-protein interactions using AlphaFold2. Nat Commun 2022; 13:1265. [PMID: 35273146 PMCID: PMC8913741 DOI: 10.1038/s41467-022-28865-w] [Citation(s) in RCA: 270] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/11/2022] [Indexed: 01/02/2023] Open
Abstract
Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR. Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.
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36
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Guo L, He J, Lin P, Huang SY, Wang J. TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models. Bioinformatics 2022; 38:2444-2451. [PMID: 35199137 DOI: 10.1093/bioinformatics/btac120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 01/19/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. RESULTS We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations. AVAILABILITY Codes available at: https://github.com/BioinformaticsCSU/TRScore.
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Affiliation(s)
- Linyuan Guo
- School of Computer Science, Central South University, Changsha, Hunan 410083, China
| | - Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jianxin Wang
- School of Computer Science, Central South University, Changsha, Hunan 410083, China
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37
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Elhabashy H, Merino F, Alva V, Kohlbacher O, Lupas AN. Exploring protein-protein interactions at the proteome level. Structure 2022; 30:462-475. [DOI: 10.1016/j.str.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 02/08/2023]
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38
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Humphreys IR, Pei J, Baek M, Krishnakumar A, Anishchenko I, Ovchinnikov S, Zhang J, Ness TJ, Banjade S, Bagde SR, Stancheva VG, Li XH, Liu K, Zheng Z, Barrero DJ, Roy U, Kuper J, Femández IS, Szakal B, Branzei D, Rizo J, Kisker C, Greene EC, Biggins S, Keeney S, Miller EA, Fromme JC, Hendrickson TL, Cong Q, Baker D. Computed structures of core eukaryotic protein complexes. Science 2021; 374:eabm4805. [PMID: 34762488 PMCID: PMC7612107 DOI: 10.1126/science.abm4805] [Citation(s) in RCA: 239] [Impact Index Per Article: 79.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.
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Affiliation(s)
- 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
| | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Aditya Krishnakumar
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA, USA
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, 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
| | - Travis J. Ness
- Department of Chemistry, Wayne State University, Detroit, MI, USA
| | - Sudeep Banjade
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Saket R. Bagde
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | | | - Xiao-Han Li
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
| | - Kaixian Liu
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Zhi Zheng
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY
| | - Daniel J. Barrero
- Howard Hughes Medical Institute, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Upasana Roy
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Jochen Kuper
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
| | - Israel S. Femández
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Barnabas Szakal
- IFOM, the FIRC Institute of Molecular Oncology, Via Adamello 16, 20139, Milan, Italy
| | - Dana Branzei
- IFOM, the FIRC Institute of Molecular Oncology, Via Adamello 16, 20139, Milan, Italy
- Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (IGM-CNR), Via Abbiategrasso 207, 27100, Pavia, Italy
| | - Josep Rizo
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Caroline Kisker
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
| | - Eric C. Greene
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Sue Biggins
- Howard Hughes Medical Institute, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Scott Keeney
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - J. Christopher Fromme
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 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
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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39
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Pereira J, Schwede T. Interactomes in the era of deep learning. Science 2021; 374:1319-1320. [PMID: 34882469 DOI: 10.1126/science.abm8295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Joana Pereira
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Basel, Switzerland
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40
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Artzi L, Alon A, Brock KP, Green AG, Tam A, Ramírez-Guadiana FH, Marks D, Kruse A, Rudner DZ. Dormant spores sense amino acids through the B subunits of their germination receptors. Nat Commun 2021; 12:6842. [PMID: 34824238 PMCID: PMC8617281 DOI: 10.1038/s41467-021-27235-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Bacteria from the orders Bacillales and Clostridiales differentiate into stress-resistant spores that can remain dormant for years, yet rapidly germinate upon nutrient sensing. How spores monitor nutrients is poorly understood but in most cases requires putative membrane receptors. The prototypical receptor from Bacillus subtilis consists of three proteins (GerAA, GerAB, GerAC) required for germination in response to L-alanine. GerAB belongs to the Amino Acid-Polyamine-Organocation superfamily of transporters. Using evolutionary co-variation analysis, we provide evidence that GerAB adopts a structure similar to an L-alanine transporter from this superfamily. We show that mutations in gerAB predicted to disrupt the ligand-binding pocket impair germination, while mutations predicted to function in L-alanine recognition enable spores to respond to L-leucine or L-serine. Finally, substitutions of bulkier residues at these positions cause constitutive germination. These data suggest that GerAB is the L-alanine sensor and that B subunits in this broadly conserved family function in nutrient detection.
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Affiliation(s)
- Lior Artzi
- Department of Microbiology, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA
| | - Anna G Green
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA
| | - Amy Tam
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA
| | | | - Debora Marks
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA
| | - Andrew Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
| | - David Z Rudner
- Department of Microbiology, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
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41
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Pozzati G, Zhu W, Bassot C, Lamb J, Kundrotas P, Elofsson A. Limits and potential of combined folding and docking. Bioinformatics 2021; 38:954-961. [PMID: 34788800 PMCID: PMC8796369 DOI: 10.1093/bioinformatics/btab760] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/23/2021] [Accepted: 11/02/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta. RESULTS The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065. AVAILABILITY AND IMPLEMENTATION All scripts for predictions and analysis are available from https://github.com/ElofssonLab/bioinfo-toolbox/ and https://gitlab.com/ElofssonLab/benchmark5/. All models joined alignments, and evaluation results are available from the following figshare repository https://doi.org/10.6084/m9.figshare.14654886.v2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - John Lamb
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden
| | - Petras Kundrotas
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden,Center for Computational Biology, The University of Kansas, Lawrence, KS 66047, USA
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42
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Recruitment of the TolA protein to cell constriction sites in Escherichia coli via three separate mechanisms, and a critical role for FtsWI activity in recruitment of both TolA and TolQ. J Bacteriol 2021; 204:e0046421. [PMID: 34748387 DOI: 10.1128/jb.00464-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
The Tol-Pal system of Gram-negative bacteria helps maintain integrity of the cell envelope and ensures that invagination of the envelope layers during cell fission occurs in a well-coordinated manner. In E. coli, the five Tol-Pal proteins (TolQ, R, A, B and Pal) accumulate at cell constriction sites in a manner that normally requires the activity of the cell constriction initiation protein FtsN. While septal recruitment of TolR, TolB and Pal also requires the presence of TolQ and/or TolA, each of the the latter two can recognize constriction sites independently of the other system proteins. What attracts TolQ or TolA to these sites is unclear. We show that FtsN attracts both proteins in an indirect fashion, and that PBP1A, PBP1B and CpoB are dispensable for their septal recruitment. However, the β-lactam aztreonam readily interferes with septal accumulation of both TolQ and TolA, indicating that FtsN-stimulated production of septal peptidoglycan by the FtsWI synthase is critical to their recruitment. We also discovered that each of TolA's three domains can recognize division sites in a separate fashion. Notably, the middle domain (TolAII) is responsible for directing TolA to constriction sites in the absence of other Tol-Pal proteins and CpoB, while recruitment of TolAI and TolAIII requires TolQ and a combination of TolB, Pal, and CpoB, respectively. Additionally, we describe the construction and use of functional fluorescent sandwich fusions of the ZipA division protein, which should be more broadly valuable in future studies of the E. coli cell division machinery. IMPORTANCE Cell division (cytokinesis) is a fundamental biological process that is incompletely understood for any organism. Division of bacterial cells relies on a ring-like machinery called the septal ring or divisome that assembles along the circumference of the mother cell at the site where constriction will eventually occur. In the well-studied bacterium Escherichia coli, this machinery contains over thirty distinct proteins. We studied how two such proteins, TolA and TolQ, which also play a role in maintaining integrity of the outer-membrane, are recruited to the machinery. We find that TolA can be recruited by three separate mechanisms, and that both proteins rely on the activity of a well-studied cell division enzyme for their recruitment.
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43
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Mehrabiani KM, Cheng RR, Onuchic JN. Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data. J Phys Chem B 2021; 125:11408-11417. [PMID: 34618469 DOI: 10.1021/acs.jpcb.1c07145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Direct coupling analysis (DCA) is a global statistical approach that uses information encoded in protein sequence data to predict spatial contacts in a three-dimensional structure of a folded protein. DCA has been widely used to predict the monomeric fold at amino acid resolution and to identify biologically relevant interaction sites within a folded protein. Going beyond single proteins, DCA has also been used to identify spatial contacts that stabilize the interaction in protein complex formation. However, extracting this higher order information necessary to predict dimer contacts presents a significant challenge. A DCA evolutionary signal is much stronger at the single protein level (intraprotein contacts) than at the protein-protein interface (interprotein contacts). Therefore, if DCA-derived information is to be used to predict the structure of these complexes, there is a need to identify statistically significant DCA predictions. We propose a simple Z-score measure that can filter good predictions despite noisy, limited data. This new methodology not only improves our prediction ability but also provides a quantitative measure for the validity of the prediction.
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Affiliation(s)
- Kareem M Mehrabiani
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Ryan R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States.,Department of Physics & Astronomy, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States
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44
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Sledzieski S, Singh R, Cowen L, Berger B. D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions. Cell Syst 2021; 12:969-982.e6. [PMID: 34536380 PMCID: PMC8586911 DOI: 10.1016/j.cels.2021.08.010] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/01/2021] [Accepted: 08/19/2021] [Indexed: 11/29/2022]
Abstract
We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.
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Affiliation(s)
- Samuel Sledzieski
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rohit Singh
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, USA.
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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45
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Sanchez-Pulido L, Ponting CP. Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction. J Mol Biol 2021; 433:167106. [PMID: 34139218 PMCID: PMC8527833 DOI: 10.1016/j.jmb.2021.167106] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a "beads-on-a-string" model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.
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Affiliation(s)
- Luis Sanchez-Pulido
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.
| | - Chris P Ponting
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.
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46
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Laine E, Eismann S, Elofsson A, Grudinin S. Protein sequence-to-structure learning: Is this the end(-to-end revolution)? Proteins 2021; 89:1770-1786. [PMID: 34519095 DOI: 10.1002/prot.26235] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
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Affiliation(s)
- Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Stephan Eismann
- Department of Computer Science and Applied Physics, Stanford University, Stanford, California, USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
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47
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Quignot C, Postic G, Bret H, Rey J, Granger P, Murail S, Chacón P, Andreani J, Tufféry P, Guerois R. InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps. Nucleic Acids Res 2021; 49:W277-W284. [PMID: 33978743 PMCID: PMC8265070 DOI: 10.1093/nar/gkab358] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/09/2021] [Accepted: 04/23/2021] [Indexed: 12/19/2022] Open
Abstract
The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.
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Affiliation(s)
- Chloé Quignot
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Guillaume Postic
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Hélène Bret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Rey
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pierre Granger
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Samuel Murail
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Pierre Tufféry
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
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48
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van Noort CW, Honorato RV, Bonvin AMJJ. Information-driven modeling of biomolecular complexes. Curr Opin Struct Biol 2021; 70:70-77. [PMID: 34139639 DOI: 10.1016/j.sbi.2021.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/10/2021] [Indexed: 11/15/2022]
Abstract
Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.
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Affiliation(s)
- Charlotte W van Noort
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Rodrigo V Honorato
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584CH, Netherlands.
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Shin JE, Riesselman AJ, Kollasch AW, McMahon C, Simon E, Sander C, Manglik A, Kruse AC, Marks DS. Protein design and variant prediction using autoregressive generative models. Nat Commun 2021; 12:2403. [PMID: 33893299 PMCID: PMC8065141 DOI: 10.1038/s41467-021-22732-w] [Citation(s) in RCA: 119] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 12/11/2022] Open
Abstract
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.
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Affiliation(s)
- Jung-Eun Shin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Adam J Riesselman
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- insitro, South San Francisco, CA, USA
| | - Aaron W Kollasch
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Conor McMahon
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Elana Simon
- Harvard College, Cambridge, MA, USA
- Reverie Labs, Cambridge, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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