1
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Aslan A, Ari Yuka S. Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives. Amino Acids 2024; 56:37. [PMID: 38822212 PMCID: PMC11143054 DOI: 10.1007/s00726-024-03397-3] [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] [Accepted: 05/06/2024] [Indexed: 06/02/2024]
Abstract
Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.
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Affiliation(s)
- Ayca Aslan
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey
| | - Selcen Ari Yuka
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey.
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2
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Gao B, Zhu S. The evolutionary novelty of insect defensins: from bacterial killing to toxin neutralization. Cell Mol Life Sci 2024; 81:230. [PMID: 38780625 PMCID: PMC11116330 DOI: 10.1007/s00018-024-05273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024]
Abstract
Insect host defense comprises two complementary dimensions, microbial killing-mediated resistance and microbial toxin neutralization-mediated resilience, both jointly providing protection against pathogen infections. Insect defensins are a class of effectors of innate immunity primarily responsible for resistance to Gram-positive bacteria. Here, we report a newly originated gene from an ancestral defensin via genetic deletion following gene duplication in Drosophila virilis, which confers an enhanced resilience to Gram-positive bacterial infection. This gene encodes an 18-mer arginine-rich peptide (termed DvirARP) with differences from its parent gene in its pattern of expression, structure and function. DvirARP specifically expresses in D. virilis female adults with a constitutive manner. It adopts a novel fold with a 310 helix and a two CXC motif-containing loop stabilized by two disulfide bridges. DvirARP exhibits no activity on the majority of microorganisms tested and only a weak activity against two Gram-positive bacteria. DvirARP knockout flies are viable and have no obvious defect in reproductivity but they are more susceptible to the DvirARP-resistant Staphylococcus aureus infection than the wild type files, which can be attributable to its ability in neutralization of the S. aureus secreted toxins. Phylogenetic distribution analysis reveals that DvirARP is restrictedly present in the Drosophila subgenus, but independent deletion variations also occur in defensins from the Sophophora subgenus, in support of the evolvability of this class of immune effectors. Our work illustrates for the first time how a duplicate resistance-mediated gene evolves an ability to increase the resilience of a subset of Drosophila species against bacterial infection.
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Affiliation(s)
- Bin Gao
- Group of Peptide Biology and Evolution, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Shunyi Zhu
- Group of Peptide Biology and Evolution, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
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3
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Zheng W, Wuyun Q, Li Y, Zhang C, Freddolino PL, Zhang Y. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data. Nat Methods 2024; 21:279-289. [PMID: 38167654 PMCID: PMC10864179 DOI: 10.1038/s41592-023-02130-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.
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Affiliation(s)
- Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Qiqige Wuyun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Yang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - P Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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4
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Legueux-Cajgfinger Y, Velusamy M, Fathallah S, Vallin B, Duca L, Dauchez M, Vincent P, Limon I, Blaise R. Unraveling the inhibitory mechanism of adenylyl cyclase 8E: New insights into regulatory pathways of cAMP signal integration. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119645. [PMID: 38016490 DOI: 10.1016/j.bbamcr.2023.119645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/09/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Adenylyl Cyclase 8E (AC8E), which lacks part of M1 transmembrane domain, has been previously shown to dimerize with AC3 and down-regulate its activity, but the molecular mechanism of this inhibitory effect has remained elusive. Here, we first show that AC8E also inhibits AC2 and AC6, highlighting the functional importance of this novel regulatory mechanism in the cAMP signaling pathway across AC families. We then completed the partial structure of Bos taurus AC9 using combinations of comparative modeling and fold recognition methods, and used this as a template to build the first full 3D-models of AC8 and AC8E. These models evidenced that the lack of M1 transmembrane domain of AC8E shifts the N-terminal domain, which impacts the orientation of the helical domains, thus affecting the catalytic site. This was confirmed in living cells with cAMP imaging, where we showed that the N-terminal domain is required for reducing cAMP production. Our data also show that AC8E prevents the translocation of other ACs towards the plasma membrane, further reducing the cAMP responsiveness to extracellular signals. This newly discovered dual inhibitory mechanism provides an additional level of regulation of cAMP-dependent signals integration.
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Affiliation(s)
- Yohan Legueux-Cajgfinger
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France
| | - Mahesh Velusamy
- CNRS UMR 7369, Matrice Extracellulaire et Dynamique Cellulaire (MEDyC), Université de Reims Champagne Ardenne, F-51095 Reims, France
| | - Samar Fathallah
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France
| | - Benjamin Vallin
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France
| | - Laurent Duca
- CNRS UMR 7369, Matrice Extracellulaire et Dynamique Cellulaire (MEDyC), Université de Reims Champagne Ardenne, F-51095 Reims, France
| | - Manuel Dauchez
- CNRS UMR 7369, Matrice Extracellulaire et Dynamique Cellulaire (MEDyC), Université de Reims Champagne Ardenne, F-51095 Reims, France
| | - Pierre Vincent
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France; IGF, Univ. Montpellier, CNRS, INSERM, Montpellier, France
| | - Isabelle Limon
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France
| | - Régis Blaise
- Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), CNRS UMR 8256, Biological Adaptation and Ageing, 7 quai St-Bernard, F-75005 Paris, France.
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5
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Chu L, Ruffolo JA, Harmalkar A, Gray JJ. Flexible protein-protein docking with a multitrack iterative transformer. Protein Sci 2024; 33:e4862. [PMID: 38148272 PMCID: PMC10804679 DOI: 10.1002/pro.4862] [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: 07/09/2023] [Revised: 11/17/2023] [Accepted: 12/06/2023] [Indexed: 12/28/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee‐Shin Chu
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey A. Ruffolo
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
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6
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Kant R, Kaushik R, Chopra M, Saluja D. Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors. J Biomol Struct Dyn 2024:1-19. [PMID: 38174578 DOI: 10.1080/07391102.2023.2300060] [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: 07/15/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
After the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ravi Kant
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research &Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Rahul Kaushik
- Biotechology Research Center, Technology Innovation Institute, Masdar City, UAE
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Madhu Chopra
- Laboratory of Molecular Modeling and Drug Development, Dr. B. R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Daman Saluja
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research &Delhi School of Public Health, IoE, University of Delhi, Delhi, India
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7
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Chen Z, Liu N, Huang Y, Min X, Zeng X, Ge S, Zhang J, Xia N. PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3128-3138. [PMID: 37220029 DOI: 10.1109/tcbb.2023.3279019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.
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8
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Chu LS, Ruffolo JA, Harmalkar A, Gray JJ. Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.29.547134. [PMID: 37425754 PMCID: PMC10327054 DOI: 10.1101/2023.06.29.547134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi-track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. For a benchmark set of rigid targets, GeoDock obtains a 41% success rate, outperforming all the other tested methods. For a more challenging benchmark set of flexible targets, GeoDock achieves a similar number of top-model successes as the traditional method ClusPro [1], but fewer than ReplicaDock2 [2]. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey A Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
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9
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Miller LG, Demny M, Tamamis P, Contreras LM. Characterization of epitranscriptome reader proteins experimentally and in silico: Current knowledge and future perspectives beyond the YTH domain. Comput Struct Biotechnol J 2023; 21:3541-3556. [PMID: 37501707 PMCID: PMC10371769 DOI: 10.1016/j.csbj.2023.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 07/29/2023] Open
Abstract
To date, over 150 chemical modifications to the four canonical RNA bases have been discovered, known collectively as the epitranscriptome. Many of these modifications have been implicated in a variety of cellular processes and disease states. Additional work has been done to identify proteins known as "readers" that selectively interact with RNAs that contain specific chemical modifications. Protein interactomes with N6-methyladenosine (m6A), N1-methyladenosine (m1A), N5-methylcytosine (m5C), and 8-oxo-7,8-dihydroguanosine (8-oxoG) have been determined, mainly through experimental advances in proteomics techniques. However, relatively few proteins have been confirmed to bind directly to RNA containing these modifications. Furthermore, for many of these protein readers, the exact binding mechanisms as well as the exclusivity for recognition of modified RNA species remain elusive, leading to questions regarding their roles within different cellular processes. In the case of the YT-521B homology (YTH) family of proteins, both experimental and in silico techniques have been leveraged to provide valuable biophysical insights into the mechanisms of m6A recognition at atomic resolution. To date, the YTH family is one of the best characterized classes of readers. Here, we review current knowledge about epitranscriptome recognition of the YTH domain proteins from previously published experimental and computational studies. We additionally outline knowledge gaps for proteins beyond the well-studied human YTH domains and the current in silico techniques and resources that can enable investigation of protein interactions with modified RNA outside of the YTH-m6A context.
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Affiliation(s)
- Lucas G. Miller
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Madeline Demny
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Phanourios Tamamis
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Department of Materials Science & Engineering, Texas A&M University, College Station, TX, USA
| | - Lydia M. Contreras
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, USA
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10
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Dhusia K, Su Z, Wu Y. Computational analyses of the interactome between TNF and TNFR superfamilies. Comput Biol Chem 2023; 103:107823. [PMID: 36682326 DOI: 10.1016/j.compbiolchem.2023.107823] [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: 09/06/2022] [Revised: 01/05/2023] [Accepted: 01/18/2023] [Indexed: 01/20/2023]
Abstract
Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America.
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11
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Rui H, Ashton KS, Min J, Wang C, Potts PR. Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction. RSC Chem Biol 2023; 4:192-215. [PMID: 36908699 PMCID: PMC9994104 DOI: 10.1039/d2cb00207h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.
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Affiliation(s)
- Huan Rui
- Center for Research Acceleration by Digital Innovation, Amgen Research Thousand Oaks CA 91320 USA
| | - Kate S Ashton
- Medicinal Chemistry, Amgen Research Thousand Oaks CA 91320 USA
| | - Jaeki Min
- Induced Proximity Platform, Amgen Research Thousand Oaks CA 91320 USA
| | - Connie Wang
- Digital, Technology & Innovation, Amgen Thousand Oaks CA 91320 USA
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12
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Kumar N, Kaushik R, Zhang KYJ, Uversky VN, Sahu U, Sood R, Bhatia S. A novel consensus-based computational pipeline for screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including Omicron variant. Proteins 2023; 91:798-806. [PMID: 36629264 DOI: 10.1002/prot.26467] [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: 05/16/2022] [Revised: 11/11/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to evolve carrying flexible amino acid substitutions in the spike protein's receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility, and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with 3-fold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero- dimeric and trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concerns (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variant's RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow_3D11, and C118)-that may likely neutralize the recently emerged Omicron variant-facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.
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Affiliation(s)
- Naveen Kumar
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Rahul Kaushik
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, UAE.,Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Vladimir N Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.,Institute for Biological Instrumentation of the Russian Academy of Sciences, Federal Research Center 'Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences', Pushchino, Russia
| | - Upasana Sahu
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Richa Sood
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Sandeep Bhatia
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
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13
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Collins KW, Copeland MM, Kotthoff I, Singh A, Kundrotas PJ, Vakser IA. Dockground resource for protein recognition studies. Protein Sci 2022; 31:e4481. [PMID: 36281025 PMCID: PMC9667896 DOI: 10.1002/pro.4481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 12/13/2022]
Abstract
Structural information of protein-protein interactions is essential for characterization of life processes at the molecular level. While a small fraction of known protein interactions has experimentally determined structures, computational modeling of protein complexes (protein docking) has to fill the gap. The Dockground resource (http://dockground.compbio.ku.edu) provides a collection of datasets for the development and testing of protein docking techniques. Currently, Dockground contains datasets for the bound and the unbound (experimentally determined and simulated) protein structures, model-model complexes, docking decoys of experimentally determined and modeled proteins, and templates for comparative docking. The Dockground bound proteins dataset is a core set, from which other Dockground datasets are generated. It is devised as a relational PostgreSQL database containing information on experimentally determined protein-protein complexes. This report on the Dockground resource describes current status of the datasets, new automated update procedures and further development of the core datasets. We also present a new Dockground interactive web interface, which allows search by various parameters, such as release date, multimeric state, complex type, structure resolution, and so on, visualization of the search results with a number of customizable parameters, as well as downloadable datasets with predefined levels of sequence and structure redundancy.
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Affiliation(s)
| | | | - Ian Kotthoff
- Computational Biology ProgramThe University of KansasKansasUSA
| | - Amar Singh
- Computational Biology ProgramThe University of KansasKansasUSA
| | | | - Ilya A. Vakser
- Computational Biology ProgramThe University of KansasKansasUSA
- Department of Molecular BiosciencesThe University of KansasKansasUSA
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14
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Liu J, Xia KL, Wu J, Yau SST, Wei GW. Biomolecular Topology: Modelling and Analysis. ACTA MATHEMATICA SINICA, ENGLISH SERIES 2022; 38:1901-1938. [PMID: 36407804 PMCID: PMC9640850 DOI: 10.1007/s10114-022-2326-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/12/2022] [Indexed: 05/25/2023]
Abstract
With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.
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Affiliation(s)
- Jian Liu
- School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024 P. R. China
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
| | - Ke-Lin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 639798 Singapore
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Stephen Shing-Toung Yau
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Guo-Wei Wei
- Department of Mathematics & Department of Biochemistry and Molecular Biology & Department of Electrical and Computer Engineering, Michigan State University, Wells Hall 619 Red Cedar Road, East Lansing, MI 48824-1027 USA
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15
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Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
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16
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Baranwal M, Magner A, Saldinger J, Turali-Emre ES, Elvati P, Kozarekar S, VanEpps JS, Kotov NA, Violi A, Hero AO. Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions. BMC Bioinformatics 2022; 23:370. [PMID: 36088285 PMCID: PMC9464414 DOI: 10.1186/s12859-022-04910-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/26/2022] [Indexed: 12/03/2022] Open
Abstract
Background Development of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. Results In this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein–protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein–protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. Conclusions In this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04910-9.
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17
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Kaushik R, Kumar N, Zhang KYJ, Srivastava P, Bhatia S, Malik YS. A novel structure-based approach for identification of vertebrate susceptibility to SARS-CoV-2: Implications for future surveillance programmes. ENVIRONMENTAL RESEARCH 2022; 212:113303. [PMID: 35460633 PMCID: PMC9020514 DOI: 10.1016/j.envres.2022.113303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/09/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
Understanding the origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a highly debatable and unresolved issue for scientific communities all over the world. Understanding the mechanism of virus entry to the host cells is crucial to deciphering the susceptibility profiles of animal species to SARS-CoV-2. The interaction of SARS-CoV-2 ligands (receptor-binding domain on spike protein) with its host cell receptor, angiotensin-converting enzyme 2 (ACE2), is a critical determinant of host range and cross-species transmission. In this study, we developed and implemented a rigorous computational approach for predicting binding affinity between 299 ACE2 orthologs from diverse vertebrate species and the SARS-CoV-2 spike protein. The findings show that the SARS-CoV-2 spike protein can bind to a wide range of vertebrate species carrying evolutionary divergent ACE2, implying a broad host range at the virus entry level, which may contribute to cross-species transmission and further viral evolution. Furthermore, the current study facilitated the identification of genetic determinants that may differentiate susceptible from resistant host species based on the conservation of ACE2-spike protein interacting residues in vertebrate host species known to facilitate SARS-CoV-2 infection; however, these genetic determinants warrant in vivo experimental confirmation. The molecular interactions associated with varied binding affinity of distinct ACE2 isoforms in a specific bat species were identified using protein structure analysis, implying the existence of diversified bat species' susceptibility to SARS-CoV-2. The current study's findings highlight the importance of intensive surveillance programmes aimed at identifying susceptible hosts, especially those with the potential to transmit zoonotic pathogens, in order to prevent future outbreaks.
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Affiliation(s)
- Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
| | - Naveen Kumar
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
| | - Pratiksha Srivastava
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Sandeep Bhatia
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Yashpal Singh Malik
- College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Science University (GADVASU), Ludhiana, 141004, Punjab, India.
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18
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Liu X, Feng H, Wu J, Xia K. Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation. J Chem Inf Model 2022; 62:3961-3969. [PMID: 36040839 DOI: 10.1021/acs.jcim.2c00580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.
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Affiliation(s)
- Xiang Liu
- Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China, 300071.,Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
| | - Huitao Feng
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371.,Mathematical Science Research Center, Chongqing University of Technology, Chongqing, China, 400054
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing, China,101408
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
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19
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Protein–Protein Interaction Prediction for Targeted Protein Degradation. Int J Mol Sci 2022; 23:ijms23137033. [PMID: 35806036 PMCID: PMC9266413 DOI: 10.3390/ijms23137033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/17/2022] [Accepted: 06/18/2022] [Indexed: 02/04/2023] Open
Abstract
Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.
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20
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Si Y, Yan C. Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2. Brief Bioinform 2022; 23:6596987. [PMID: 35649388 DOI: 10.1093/bib/bbac208] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/17/2022] [Accepted: 05/05/2022] [Indexed: 12/19/2022] Open
Abstract
AlphaFold2 can predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input to AlphaFold2 for protein complex structure prediction. In this extensively benchmarked protocol on nonredundant PPI dataset, including 107 bacterial PPIs and 442 eukaryotic PPIs, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted, which yielded significantly better performance than the application of MSA of interologs prepared by two existing approaches. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found that the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% for the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show that the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the predicted template modeling (TM) scores of the output models.
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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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21
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Wee J, Xia K. Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction. Brief Bioinform 2022; 23:6533501. [PMID: 35189639 DOI: 10.1093/bib/bbac024] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/30/2021] [Accepted: 01/17/2022] [Indexed: 12/14/2022] Open
Abstract
Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a great challenge for all learning models for PPIs. Here, we propose persistent spectral (PerSpect) based PPI representation and featurization, and PerSpect-based ensemble learning (PerSpect-EL) models for PPI binding affinity prediction, for the first time. In our model, a sequence of Hodge (or combinatorial) Laplacian (HL) matrices at various different scales are generated from a specially designed filtration process. PerSpect attributes, which are statistical and combinatorial properties of spectrum information from these HL matrices, are used as features for PPI characterization. Each PerSpect attribute is input into a 1D convolutional neural network (CNN), and these CNN networks are stacked together in our PerSpect-based ensemble learning models. We systematically test our model on the two most commonly used datasets, i.e. SKEMPI and AB-Bind. It has been found that our model can achieve state-of-the-art results and outperform all existing models to the best of our knowledge.
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Affiliation(s)
- JunJie Wee
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
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22
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Qi S, Gao B, Zhu S. A Fungal Defensin Inhibiting Bacterial Cell-Wall Biosynthesis with Non-Hemolysis and Serum Stability. J Fungi (Basel) 2022; 8:jof8020174. [PMID: 35205928 PMCID: PMC8877149 DOI: 10.3390/jof8020174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 02/04/2023] Open
Abstract
Defensins are a class of cationic disulfide-bridged antimicrobial peptides (AMPs) present in many eukaryotic organisms and even in bacteria. They primarily include two distinct but evolutionarily related superfamilies (cis and trans). Defensins in fungi belong to the members of the cis-superfamily with the cysteine-stabilized α-helical and β-sheet fold. To date, many fungal defensin-like peptides (fDLPs) have been found through gene mining of the genome resource, but only a few have been experimentally characterized. Here, we report the structural and functional characterization of Pyronesin4 (abbreviated as Py4), a fDLP previously identified by genomic sequencing of the basal filamentous ascomycete Pyronema confluens. Chemically, synthetic Py4 adopts a native-like structure and exhibits activity on an array of Gram-positive bacteria including some clinical isolates of Staphylococcus and Staphylococcus warneri, a conditioned pathogen inhabiting in human skin. Py4 markedly altered the bacterial morphology and caused cytoplasmic accumulation of the cell-wall synthesis precursor through binding to the membrane-bound Lipid II, indicating that it works as an inhibitor of cell-wall biosynthesis. Py4 showed no hemolysis and high mammalian serum stability. This work identified a new fungal defensin with properties relevant to drug exploration. Intramolecular epistasis between mutational sites of fDLPs is also discussed.
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Affiliation(s)
- Sudong Qi
- Group of Peptide Biology and Evolution, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China; (S.Q.); (B.G.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bin Gao
- Group of Peptide Biology and Evolution, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China; (S.Q.); (B.G.)
| | - Shunyi Zhu
- Group of Peptide Biology and Evolution, State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China; (S.Q.); (B.G.)
- Correspondence: ; Tel.: +86-010-6480-7112
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23
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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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24
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Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
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Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Innopolis University, Russia
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Institute of Computer-Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
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25
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Heo L, Janson G, Feig M. Physics-based protein structure refinement in the era of artificial intelligence. Proteins 2021; 89:1870-1887. [PMID: 34156124 PMCID: PMC8616793 DOI: 10.1002/prot.26161] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/31/2021] [Accepted: 06/08/2021] [Indexed: 12/21/2022]
Abstract
Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
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26
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Scafuri N, Soler MA, Spitaleri A, Rocchia W. Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking. J Chem Theory Comput 2021; 17:7271-7280. [PMID: 34653335 PMCID: PMC8582249 DOI: 10.1021/acs.jctc.1c00789] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Protein–protein
docking typically consists of the generation
of putative binding conformations, which are subsequently ranked by
fast heuristic scoring functions. The simplicity of these functions
allows for computational efficiency but has severe repercussions on
their discrimination capabilities. In this work, we show the effectiveness
of suitable descriptors calculated along short scaled molecular dynamics
runs in recognizing the nearest-native bound conformation among a
set of putative structures generated by the HADDOCK tool for eight
protein–protein systems.
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Affiliation(s)
- Nicola Scafuri
- CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via E. Melen, 83, I-16152 Genova, Italy
| | - Miguel A Soler
- CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via E. Melen, 83, I-16152 Genova, Italy
| | - Andrea Spitaleri
- CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via E. Melen, 83, I-16152 Genova, Italy
| | - Walter Rocchia
- CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via E. Melen, 83, I-16152 Genova, Italy
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27
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Xie J, Zheng J, Hong X, Tong X, Liu X, Song Q, Liu S, Liu S. Protein-DNA complex structure modeling based on structural template. Biochem Biophys Res Commun 2021; 577:152-157. [PMID: 34517213 DOI: 10.1016/j.bbrc.2021.09.018] [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: 07/29/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 10/20/2022]
Abstract
DNA-binding is an important feature of proteins, and protein-DNA interaction involves in many life processes. Various computational methods have been developed to predict protein-DNA complex structures due to the difficulty of experimentally obtaining protein-DNA complex structures. However, prediction of protein-DNA complex is still a challenging problem compared with prediction of protein-RNA complex, this may be due to the large conformational changes between bound and unbound structure in both protein and DNA. We extend PRIME 2.0 to PRIME 2.0.1 to model protein-DNA complex structures. By comparing sequence and structure alignment methods, we found that structure-based methods can find more templates than sequence-based methods. The results of all-to-all structure alignments showed that DNA structure plays an important role in prediction of protein-DNA complex structure. By exploring the relationship of sequence and structure, we found that in protein-DNA interaction, numerous structures with dissimilar sequences have similar 3D structures and perform the similar function.
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Affiliation(s)
- Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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28
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/27/2021] [Indexed: 07/26/2023] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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29
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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30
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Raimondi F, Burkhart JG, Betts MJ, Russell RB, Wu G. Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces. F1000Res 2021; 10:1111. [PMID: 36569594 PMCID: PMC9755755 DOI: 10.12688/f1000research.74395.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2022] [Indexed: 07/26/2023] Open
Abstract
Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.
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Affiliation(s)
| | - Joshua G. Burkhart
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthew J. Betts
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Robert B. Russell
- Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
- BioQuant, University of Heidelberg, Heidelberg, Germany
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
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31
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Khalifa ME, Unterholzner L, Munir M. Structural and Evolutionary Insights Into the Binding of Host Receptors by the Rabies Virus Glycoprotein. Front Cell Infect Microbiol 2021; 11:736114. [PMID: 34708003 PMCID: PMC8542875 DOI: 10.3389/fcimb.2021.736114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Rabies represents a typical model for spillover of zoonotic viral diseases among multiple hosts. Understanding the success of rabies virus (RV) in switching hosts requires the analysis of viral evolution and host interactions. In this study, we have investigated the structural and sequence analysis of host receptors among different RV susceptible host species. Our extensive bioinformatic analysis revealed the absence of the integrin plexin domain in the integrin β1 (ITGB1) receptor of the black fruit bats in the current annotation of the genome. Interestingly, the nicotinic acetyl choline receptor (nAChR) interaction site with the glycoprotein (G) of RV was conserved among different species. To study the interaction dynamics between RV-G protein and the RV receptors, we constructed and analyzed structures of RV receptors and G proteins using homology modeling. The molecular docking of protein-protein interaction between RV-G protein and different host receptors highlighted the variability of interacting residues between RV receptors of different species. These in silico structural analysis and interaction mapping of viral protein and host receptors establish the foundation to understand complex entry mechanisms of RV entry, which may facilitate the understanding of receptor mediated spillover events in RV infections and guide the development of novel vaccines to contain the infection.
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Affiliation(s)
- Manar E Khalifa
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom.,Department of Foot and Mouth Disease, Veterinary Serum and Vaccine Research Institute, Cairo, Egypt
| | - Leonie Unterholzner
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom
| | - Muhammad Munir
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom
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32
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Khazen G, Gyulkhandanian A, Issa T, Maroun RC. Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes. Comput Struct Biotechnol J 2021; 19:5184-5197. [PMID: 34630938 PMCID: PMC8476896 DOI: 10.1016/j.csbj.2021.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/23/2021] [Accepted: 09/12/2021] [Indexed: 02/03/2023] Open
Abstract
Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.
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Affiliation(s)
- Georges Khazen
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Aram Gyulkhandanian
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
| | - Tina Issa
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Rachid C Maroun
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
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33
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Dhusia K, Wu Y. Classification of protein-protein association rates based on biophysical informatics. BMC Bioinformatics 2021; 22:408. [PMID: 34404340 PMCID: PMC8371850 DOI: 10.1186/s12859-021-04323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. RESULTS To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. CONCLUSIONS In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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34
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Park T, Won J, Baek M, Seok C. GalaxyHeteromer: protein heterodimer structure prediction by template-based and ab initio docking. Nucleic Acids Res 2021; 49:W237-W241. [PMID: 34048578 PMCID: PMC8262733 DOI: 10.1093/nar/gkab422] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/23/2021] [Accepted: 05/03/2021] [Indexed: 01/04/2023] Open
Abstract
Protein–protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein–protein interactions may provide important information that can facilitate the design of therapeutic agents. GalaxyHeteromer is a freely available automatic web server (http://galaxy.seoklab.org/heteromer) that predicts protein heterodimer complex structures from two subunit protein sequences or structures. When subunit structures are unavailable, they are predicted by template- or distance-prediction-based modelling methods. Heterodimer complex structures can be predicted by both template-based and ab initio docking, depending on the template's availability. Structural templates are detected from the protein structure database based on both the sequence and structure similarities. The templates for heterodimers may be selected from monomer and homo-oligomer structures, as well as from hetero-oligomers, owing to the evolutionary relationships of heterodimers with domains of monomers or subunits of homo-oligomers. In addition, the server employs one of the best ab initio docking methods when heterodimer templates are unavailable. The multiple heterodimer structure models and the associated scores, which are provided by the web server, may be further examined by user to test or develop functional hypotheses or to design new functional molecules.
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Affiliation(s)
- Taeyong Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.,Galux Inc., Seoul 08826, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.,Galux Inc., Seoul 08826, Republic of Korea
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35
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Gong W, Guerler A, Zhang C, Warner E, Li C, Zhang Y. Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli. J Mol Biol 2021; 433:166944. [PMID: 33741411 DOI: 10.1016/j.jmb.2021.166944] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022]
Abstract
Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.
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Affiliation(s)
- Weikang Gong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Aysam Guerler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elisa Warner
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chunhua Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA.
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36
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Xia CQ, Pan X, Yang Y, Huang Y, Shen HB. Recent Progresses of Computational Analysis of RNA-Protein Interactions. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11315-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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37
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Eismann S, Townshend RJL, Thomas N, Jagota M, Jing B, Dror RO. Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes. Proteins 2020; 89:493-501. [PMID: 33289162 DOI: 10.1002/prot.26033] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 10/10/2020] [Accepted: 11/21/2020] [Indexed: 12/16/2022]
Abstract
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics-inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.
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Affiliation(s)
- Stephan Eismann
- Department of Applied Physics, Stanford University, Stanford, California, USA.,Department of Computer Science, Stanford University, Stanford, California, USA
| | | | - Nathaniel Thomas
- Department of Physics, Stanford University, Stanford, California, USA
| | - Milind Jagota
- Department of Computer Science, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Bowen Jing
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, California, USA.,Department of Structural Biology, Stanford University, Stanford, California, USA.,Department of Molecular and Cellular Physiology, Stanford University, Stanford, California, USA.,Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
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38
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Keller A, Chavez JD, Tang X, Bruce JE. Leveraging the Entirety of the Protein Data Bank to Enable Improved Structure Prediction Based on Cross-Link Data. J Proteome Res 2020; 20:1087-1095. [PMID: 33263396 DOI: 10.1021/acs.jproteome.0c00495] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
XLinkDB is a fast-expanding public database now storing more than 100 000 distinct identified cross-linked protein residue pairs acquired by chemical cross-linking with mass spectrometry from samples of 12 species (J. Proteome Res. 2019, 18 (2), 753-758). Mapping identified cross-links to protein structures, when available, provides valuable guidance on protein conformations detected in the cross-linked samples. As more and more structures become available in the Protein Data Bank (Nucleic Acids Res. 2000, 28 (1), 235-242), we sought to leverage their utility for cross-link studies by automatically mapping identified cross-links to structures based on sequence homology of the cross-linked proteins with those within structures. This enables use of structures derived from organisms different from those of samples, including large multiprotein complexes and complexes in alternative states. We demonstrate utility of mapping to orthologous structures, highlighting a cross-link between two subunits of mouse mitochondrial Complex I that was mapped to 15 structures derived from five mammals, its distances there of 16.2 ± 0.4 Å indicating strong conservation of the protein interaction. We also show how multimeric structures enable reassessment of cross-links presumed to be intraprotein as potentially homodimeric interprotein in origin.
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Affiliation(s)
- Andrew Keller
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Juan D Chavez
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Xiaoting Tang
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - James E Bruce
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
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39
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Kashani-Amin E, Tabatabaei-Malazy O, Sakhteman A, Larijani B, Ebrahim-Habibi A. A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools. Curr Drug Discov Technol 2020; 16:159-172. [PMID: 29493456 DOI: 10.2174/1570163815666180227162157] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/15/2018] [Accepted: 02/22/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. OBJECTIVE A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. METHODS Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. RESULTS Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. CONCLUSION This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.
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Affiliation(s)
- Elaheh Kashani-Amin
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ozra Tabatabaei-Malazy
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Sakhteman
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.,Medicinal Chemistry and Natural Products Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Azadeh Ebrahim-Habibi
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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40
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Rosell M, Fernández-Recio J. Docking approaches for modeling multi-molecular assemblies. Curr Opin Struct Biol 2020; 64:59-65. [PMID: 32615514 PMCID: PMC7324114 DOI: 10.1016/j.sbi.2020.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/13/2020] [Accepted: 05/21/2020] [Indexed: 12/12/2022]
Abstract
Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.
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Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain
| | - Juan Fernández-Recio
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain.
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41
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Karami Y, Rey J, Postic G, Murail S, Tufféry P, de Vries SJ. DaReUS-Loop: a web server to model multiple loops in homology models. Nucleic Acids Res 2020; 47:W423-W428. [PMID: 31114872 PMCID: PMC6602439 DOI: 10.1093/nar/gkz403] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/20/2019] [Accepted: 05/06/2019] [Indexed: 02/07/2023] Open
Abstract
Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.
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Affiliation(s)
- Yasaman Karami
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Julien Rey
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Guillaume Postic
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France.,Institut Français de Bioinformatique (IFB), UMS 3601-CNRS, Université Paris-Saclay, Orsay, France
| | - Samuel Murail
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France
| | - Pierre Tufféry
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Sjoerd J de Vries
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
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42
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Singh A, Dauzhenka T, Kundrotas PJ, Sternberg MJE, Vakser IA. Application of docking methodologies to modeled proteins. Proteins 2020; 88:1180-1188. [PMID: 32170770 DOI: 10.1002/prot.25889] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/15/2020] [Accepted: 03/07/2020] [Indexed: 12/12/2022]
Abstract
Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.
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Affiliation(s)
- Amar Singh
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Taras Dauzhenka
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, South Kensington, London, UK
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
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43
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A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation. NAT MACH INTELL 2020; 2:116-123. [PMID: 34170981 PMCID: PMC7223817 DOI: 10.1038/s42256-020-0149-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
Abstract
The ability to predict protein-protein interactions is crucial to our understanding of a wide range of biological activities and functions in the human body, and for guiding drug discovery. Despite considerable efforts to develop suitable computational methods, predicting protein-protein interaction binding affinity changes following mutation (ΔΔG) remains a severe challenge. Algebraic topology, a champion in recent worldwide competitions for protein-ligand binding affinity predictions, is a promising approach to simplifying the complexity of biological structures. Here we introduce element- and site-specific persistent homology (a new branch of algebraic topology) to simplify the structural complexity of protein-protein complexes and embed crucial biological information into topological invariants. We also propose a new deep learning algorithm called NetTree to take advantage of convolutional neural networks and gradient-boosting trees. A topology-based network tree is constructed by integrating the topological representation and NetTree for predicting protein-protein interaction ΔΔG. Tests on major benchmark datasets indicate that the proposed topology-based network tree is an important improvement over the current state of the art in predicting ΔΔG.
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44
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Yan Y, He J, Feng Y, Lin P, Tao H, Huang SY. Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46. Proteins 2020; 88:1055-1069. [PMID: 31994779 DOI: 10.1002/prot.25874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/02/2020] [Accepted: 01/22/2020] [Indexed: 12/12/2022]
Abstract
Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Yuyu Feng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
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45
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Chan J, Zou J, Ortiz CL, Chang Chien CH, Pan RL, Yang LW. DR-SIP: protocols for higher order structure modeling with distance restraints- and cyclic symmetry-imposed packing. Bioinformatics 2020; 36:449-461. [PMID: 31347658 DOI: 10.1093/bioinformatics/btz579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 07/05/2019] [Accepted: 07/18/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Quaternary structure determination for transmembrane/soluble proteins requires a reliable computational protocol that leverages observed distance restraints and/or cyclic symmetry (Cn symmetry) found in most homo-oligomeric transmembrane proteins. RESULTS We survey 118 X-ray crystallographically solved structures of homo-oligomeric transmembrane proteins (HoTPs) and find that ∼97% are Cn symmetric. Given the prevalence of Cn symmetric HoTPs and the benefits of incorporating geometry restraints in aiding quaternary structure determination, we introduce two new filters, the distance-restraints (DR) and the Symmetry-Imposed Packing (SIP) filters. SIP relies on a new method that can rebuild the closest ideal Cn symmetric complex from docking poses containing a homo-dimer without prior knowledge of the number (n) of monomers. Using only the geometrical filter, SIP, near-native poses of 7 HoTPs in their monomeric states can be correctly identified in the top-10 for 71% of all cases, or 29% among 31 HoTP structures obtained through homology modeling, while ZDOCK alone returns 14 and 3%, respectively. When the n is given, the optional n-mer filter is applied with SIP and returns the near-native poses for 76% of the test set within the top-10, outperforming M-ZDOCK's 55% and Sam's 47%. While applying only SIP to three HoTPs that comes with distance restraints, we found the near-native poses were ranked 1st, 1st and 10th among 54 000 possible decoys. The results are further improved to 1st, 1st and 3rd when both DR and SIP filters are used. By applying only DR, a soluble system with distance restraints is recovered at the 1st-ranked pose. AVAILABILITY AND IMPLEMENTATION https://github.com/capslockwizard/drsip. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justin Chan
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Sciences, Academia Sinica, Taipei, Taiwan
| | - Jinhao Zou
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.,UTHealth Graduate School of Biomedical Science, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Chi-Hong Chang Chien
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Rong-Long Pan
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Sciences, Academia Sinica, Taipei, Taiwan.,Physics Division, National Center for Theoretical Sciences, National Tsing Hua University, Hsinchu, Taiwan
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46
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Lensink MF, Nadzirin N, Velankar S, Wodak SJ. Modeling protein‐protein, protein‐peptide, and protein‐oligosaccharide complexes: CAPRI 7th edition. Proteins 2020; 88:916-938. [DOI: 10.1002/prot.25870] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/19/2019] [Accepted: 12/26/2019] [Indexed: 12/19/2022]
Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle F‐59000 Lille France
| | - Nurul Nadzirin
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome Campus Cambridge UK
| | - Sameer Velankar
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome Campus Cambridge UK
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47
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Abstract
Protein-protein and protein-DNA/RNA interactions are involved in many cellular processes. Therefore, determining their complex structures at the atomic level is valuable to gain insights into these interactions. Because of the technical difficulties and high cost in experimental methods, computational approaches like molecular docking have been developed to predict the structures of macromolecular complexes in the last decades. To automatically integrate the available binding information from the PDB, we have developed HDOCK, a protein-protein/nucleic acid docking web server by combining template-based and free docking. In this chapter, we first briefly introduce our HDOCK server and then give a step-by-step description of docking bovine chymotrypsinogen A against its inhibitor (PDB ID: 1CGI). Two case studies of realistic examples are also discussed. The HDOCK server is freely available at http://hdock.phys.hust.edu.cn/ .
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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48
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Abstract
There is a large gap between the numbers of known protein-protein interactions and the corresponding experimentally solved structures of protein complexes. Fortunately, this gap can be in part bridged by computational structure modeling methods. Currently, template-based modeling is the most accurate means to predict both individual protein structures and protein complexes. One of the major issues in template-based modeling is to identify homologous structures that could be utilized as templates. To simplify this task, we have developed the PPI3D web server. The server is not only able to search for homologous protein complexes, but also provides means to analyze identified interactions and to model protein complexes. In recent CASP and CAPRI experiments, PPI3D proved to be a useful tool for homology modeling of multimeric proteins. In this chapter, we provide a brief description of the PPI3D web server capabilities and how to use the server for modeling of protein complexes.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
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49
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Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
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50
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Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 2019; 46:W296-W303. [PMID: 29788355 PMCID: PMC6030848 DOI: 10.1093/nar/gky427] [Citation(s) in RCA: 7044] [Impact Index Per Article: 1408.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 05/07/2018] [Indexed: 11/13/2022] Open
Abstract
Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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Affiliation(s)
- Andrew Waterhouse
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Martino Bertoni
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Rafal Gumienny
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Florian T Heer
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Tjaart A P de Beer
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Christine Rempfer
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Lorenza Bordoli
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Rosalba Lepore
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland
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