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Su Z, Dhusia K, Wu Y. Encoding the space of protein-protein binding interfaces by artificial intelligence. Comput Biol Chem 2024; 110:108080. [PMID: 38643609 DOI: 10.1016/j.compbiolchem.2024.108080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 04/23/2024]
Abstract
The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.
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Affiliation(s)
- Zhaoqian Su
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN 37212, USA
| | - 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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2
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Rietman EA, Siegelmann HT, Klement GL, Tuszynski JA. Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions. Int J Mol Sci 2023; 24:14648. [PMID: 37834096 PMCID: PMC10572529 DOI: 10.3390/ijms241914648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.
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Affiliation(s)
- Edward A. Rietman
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA 01003, USA; (E.A.R.); (H.T.S.)
- Applied Physics, 477 Madison Ave., 6th Floor, New York, NY 10022, USA
| | - Hava T. Siegelmann
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA 01003, USA; (E.A.R.); (H.T.S.)
| | | | - Jack A. Tuszynski
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, I-10129 Turin, Italy
- Department of Data Science and Engineering, The Silesian University of Technology, 44-100 Gliwice, Poland
- Department of Physics, University of Alberta, Edmonton, AB T6G 2E9, Canada
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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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4
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Gao M, Nakajima An D, Parks JM, Skolnick J. AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nat Commun 2022; 13:1744. [PMID: 35365655 PMCID: PMC8975832 DOI: 10.1038/s41467-022-29394-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/15/2022] [Indexed: 12/20/2022] Open
Abstract
Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system. Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Here the authors present AF2Complex and show that application to the E. coli cytochrome biogenesis system I yields confident computational models for three sought-after assemblies.
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Affiliation(s)
- Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Atlanta, GA, USA.
| | - Davi Nakajima An
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jerry M Parks
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Atlanta, GA, USA.
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Dhusia K, Madrid C, Su Z, Wu Y. EXCESP: A Structure-Based Online Database for Extracellular Interactome of Cell Surface Proteins in Humans. J Proteome Res 2022; 21:349-359. [PMID: 34978816 DOI: 10.1021/acs.jproteome.1c00612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The interactions between ectodomains of cell surface proteins are vital players in many important cellular processes, such as regulating immune responses, coordinating cell differentiation, and shaping neural plasticity. However, while the construction of a large-scale protein interactome has been greatly facilitated by the development of high-throughput experimental techniques, little progress has been made to support the discovery of extracellular interactome for cell surface proteins. Harnessed by the recent advances in computational modeling of protein-protein interactions, here we present a structure-based online database for the extracellular interactome of cell surface proteins in humans, called EXCESP. The database contains both experimentally determined and computationally predicted interactions among all type-I transmembrane proteins in humans. All structural models for these interactions and their binding affinities were further computationally modeled. Moreover, information such as expression levels of each protein in different cell types and its relation to various signaling pathways from other online resources has also been integrated into the database. In summary, the database serves as a valuable addition to the existing online resources for the study of cell surface proteins. It can contribute to the understanding of the functions of cell surface proteins in the era of systems biology.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States
| | - Carlos Madrid
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States.,Laboratory for Macromolecular Analysis and Proteomics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States
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6
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Yu Y. One of Nature’s Basic Laws: Combination-Sharing. Hu Arenas 2021. [DOI: 10.1007/s42087-021-00215-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Bianchetti L, Sinar D, Depenveiller C, Dejaegere A. Insights into mineralocorticoid receptor homodimerization from a combined molecular modeling and bioinformatics study. Proteins 2021; 89:952-965. [PMID: 33713045 DOI: 10.1002/prot.26073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/25/2021] [Accepted: 03/04/2021] [Indexed: 01/01/2023]
Abstract
In vertebrates, the mineralocorticoid receptor (MR) is a steroid-activated nuclear receptor (NR) that plays essential roles in water-electrolyte balance and blood pressure homeostasis. It belongs to the group of oxo-steroidian NRs, together with the glucocorticoid (GR), progesterone (PR), and androgen (AR) receptors. Classically, these oxo-steroidian NRs homodimerize and bind to specific genomic sequences to activate gene expression. NRs are multi-domain proteins, and dimerization is mediated by both the DNA (DBD) and ligand binding domains (LBDs), with the latter thought to provide the largest dimerization interface. However, at the structural level, the dimerization of oxo-steroidian receptors LBDs has remained largely a matter of debate and, despite their sequence homology, there is currently no consensus on a common homodimer assembly across the four receptors, that is, GR, PR, AR, and MR. Here, we examined all available MR LBD crystals using different computational methods (protein common interface database, proteins, interfaces, structures and assemblies, protein-protein interaction prediction by structural matching, and evolutionary protein-protein interface classifier, and the molecular mechanics Poisson-Boltzmann surface area method). A consensus is reached by all methods and singles out an interface mediated by helices H9, H10 and the C-terminal F domain as having characteristics of a biologically relevant assembly. Interestingly, a similar assembly was previously identified for GRα, MR closest homolog. Alternative architectures that were proposed for GRα were not observed for MR. These data call for further experimental investigations of oxo-steroid dimer architectures.
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Affiliation(s)
- Laurent Bianchetti
- Laboratoire de Chimie Biophysique de la Signalisation de la Transcription, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France
- Centre National de la Recherche Scientifique UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, Illkirch, France
- Ecole Supérieure de Biotechnologie de Strasbourg, Université de Strasbourg, Illkirch, France
| | - Deniz Sinar
- Laboratoire de Chimie Biophysique de la Signalisation de la Transcription, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France
- Centre National de la Recherche Scientifique UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, Illkirch, France
- Ecole Supérieure de Biotechnologie de Strasbourg, Université de Strasbourg, Illkirch, France
| | - Camille Depenveiller
- Laboratoire de Chimie Biophysique de la Signalisation de la Transcription, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France
- Centre National de la Recherche Scientifique UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, Illkirch, France
- Ecole Supérieure de Biotechnologie de Strasbourg, Université de Strasbourg, Illkirch, France
| | - Annick Dejaegere
- Laboratoire de Chimie Biophysique de la Signalisation de la Transcription, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France
- Centre National de la Recherche Scientifique UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, Illkirch, France
- Ecole Supérieure de Biotechnologie de Strasbourg, Université de Strasbourg, Illkirch, France
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Cyrus C, Vatte C, Chathoth S, Sayed AA, Borgio JF, Alrubaish MA, Alfalah R, Alsaikhan J, Al Ali AK. Haemoglobin switching modulator SNPs rs5006884 is associated with increased HbA 2 in β-thalassaemia carriers. Arch Med Sci 2021; 17:1064-1074. [PMID: 34336034 PMCID: PMC8314410 DOI: 10.5114/aoms.2019.86705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 05/15/2018] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Haemoglobin A2 (HbA2), the tetramer of α- and δ-globin chains, is used as a diagnostic biomarker for β-thalassaemia carriers. The HbA2 levels are regulated by the presence of HPFH, δ-thalassaemia, HbA1/2 gene triplication, and variants of KLF1, β-globin gene, and HbF regulating QTLs. Saudi Arabia has a high incidence of borderline HbA2 levels, thereby making it difficult to classify the haemoglobinopathies. This study aims to investigate the association of known HbF enhancer QTL gene SNPs with HbA2 levels. MATERIAL AND METHODS 14 Specific SNPs in BCL11A, HMIP, OR51B6, HBBP1, and HBG2 loci were genotyped in 164 Saudi β-thalassaemia carriers by TaqMan assay to validate their role as regulators of HbA2 levels. HbA2 levels were determined using the Variant II β-Thalassemia Short Program Recorder kit. The non-random association of these SNPs was tested using HaploView software. Protein interaction was assessed using 3D structure modelling for OR51B6 (rs5006884), comparative energy minimisation, and root-mean-square deviation (RMSD) prediction. RESULTS Elevated HbA2 levels were associated with SNPs in HBBP1, OR51B6, and TCT haplotype from HBG2 promoter region. The bioinformatics modelling and prediction revealed that the exonic rs5006884 had RMSD value deviations and significantly varied binding energy minimisation. α-globin variations were found in 57.92% of individuals but were not associated with elevated HbA2. CONCLUSIONS The haemoglobin switching modulators rs2071348, rs7482144, and rs5006884 are involved in regulation of HbA2 level with rs5006884 influencing the tetramer formation. Screening for haemoglobinopathies should take these SNPs into consideration, specifically in borderline HbA2 cases. Assiduous analysis of rs5006884 as HbA2 modulator for amelioration of disease severity is recommended.
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Affiliation(s)
- Cyril Cyrus
- Department of Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Chittibabu Vatte
- Department of Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Shahanas Chathoth
- Department of Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Abdul Azeez Sayed
- Department of Genetic Research, Institute for Research and Medical Consultation, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - J. Francis Borgio
- Department of Genetic Research, Institute for Research and Medical Consultation, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | | | - Rawan Alfalah
- King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Jana Alsaikhan
- King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Amein K. Al Ali
- Department of Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Kataria R, Duhan N, Kaundal R. Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop. Front Plant Sci 2021; 12:807354. [PMID: 35251063 PMCID: PMC8891223 DOI: 10.3389/fpls.2021.807354] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/29/2021] [Indexed: 05/04/2023]
Abstract
Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant's innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen's infection mechanism and develop pathogen-resistant lines.
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Affiliation(s)
- Raghav Kataria
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
| | - Naveen Duhan
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
- Bioinformatics Facility, Center for Integrated Biosystems, Utah State University, Logan, UT, United States
- Department of Computer Science, College of Science, Utah State University, Logan, UT, United States
- *Correspondence: Rakesh Kaundal, ;
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10
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Ceddia G, Martino LN, Parodi A, Secchi P, Campaner S, Masseroli M. Association rule mining to identify transcription factor interactions in genomic regions. Bioinformatics 2020; 36:1007-1013. [PMID: 31504203 DOI: 10.1093/bioinformatics/btz687] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 08/07/2019] [Accepted: 08/29/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework. RESULTS We developed a novel approach to infer interactions among transcription factors in user-selected genomic regions, by combining the computation of association rules and of a novel Importance Index on ChIP-seq datasets. The hallmark of our method is the definition of the Importance Index, which provides a relevance measure of the interaction among transcription factors found associated in the computed rules. Examples on synthetic data explain the index use and potential. A straightforward pre-processing pipeline enables the easy extraction of input data for our approach from any set of ChIP-seq experiments. Applications on ENCODE ChIP-seq data prove that our approach can reliably detect interactions between transcription factors, including known interactions that validate our approach. AVAILABILITY AND IMPLEMENTATION A R/Bioconductor package implementing our association rules and Importance Index-based method is available at http://bioconductor.org/packages/release/bioc/html/TFARM.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gaia Ceddia
- Dipartimento di Elettronica, Informazione e Bioingegneria, Italy
| | | | - Alice Parodi
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milan 20133, Italy
| | - Piercesare Secchi
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milan 20133, Italy.,Center for Analysis, Decisions and Society, Human Technopole, Milan 20157, Italy
| | - Stefano Campaner
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan 20139, Italy
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Italy
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11
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Yu Y. Combination-Sharing Theory and Sharing Rule of Regularity. Integr Psychol Behav Sci 2020; 54:771-784. [PMID: 32405819 DOI: 10.1007/s12124-020-09537-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The objective world we live in is actually a combination of multiple regularities, and human civilization is established in the process of adapting and applying these regularity in the consciousness system. Therefore, it can be imagined that a close and objective connection must exist among different disciplines that humans have developed through long-term accumulation of knowledge. Perhaps from an overall perspective of the objective existence, there may be many shared rules and connections that we have not yet discovered among the rules of various disciplines. This is the fundamental reason for the scientific nature of metaphor and analogy. By finding these common rules, we can use each other to understand strange things. In this study, I proposed the hypothesis that the combination-sharing rule is one of the basic rules that constitute the natural world, and briefly describe the theory by focusing on the sharing phenomenon of regularity. This article is based on the scientific achievements in the process of multidisciplinary development, and discusses the sharing mechanism of regularity in biology through interdisciplinary research.
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Affiliation(s)
- Yong Yu
- Institute of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, 18877 Jing Shi Road, Jinan, Shandong, 250062, China.
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12
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Poverennaya EV, Kiseleva OI, Ivanov AS, Ponomarenko EA. Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms. Biochemistry (Mosc) 2020; 85:68-79. [PMID: 32079518 DOI: 10.1134/s000629792001006x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Human genome contains ca. 20,000 protein-coding genes that could be translated into millions of unique protein species (proteoforms). Proteoforms coded by a single gene often have different functions, which implies different protein partners. By interacting with each other, proteoforms create a network reflecting the dynamics of cellular processes in an organism. Perturbations of protein-protein interactions change the network topology, which often triggers pathological processes. Studying proteoforms is a relatively new research area in proteomics, and this is why there are comparatively few experimental studies on the interaction of proteoforms. Bioinformatics tools can facilitate such studies by providing valuable complementary information to the experimental data and, in particular, expanding the possibilities of the studies of proteoform interactions.
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Affiliation(s)
| | - O I Kiseleva
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - A S Ivanov
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
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13
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Li H, Jiang S, Li C, Liu L, Lin Z, He H, Deng XW, Zhang Z, Wang X. The hybrid protein interactome contributes to rice heterosis as epistatic effects. Plant J 2020; 102:116-128. [PMID: 31736145 DOI: 10.1111/tpj.14616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 10/27/2019] [Accepted: 11/01/2019] [Indexed: 05/15/2023]
Abstract
Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein-protein interactions specific to F1 hybrids (F1 -specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter-subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four-thousand, six-hundred and twelve F1 -specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F1 -specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F1 -specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F1 -specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F1 -specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F1 -specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large-scale protein interactome datasets are generated in hybrid rice in the future.
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Affiliation(s)
- Hong Li
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Shuqin Jiang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Lei Liu
- Beijing Key Laboratory of Plant Resources Research and Development, School of Sciences, Beijing Technology and Business University, Beijing, 100048, China
| | - Zechuan Lin
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Hang He
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Xing-Wang Deng
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
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14
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Padhorny D, Porter KA, Ignatov M, Alekseenko A, Beglov D, Kotelnikov S, Ashizawa R, Desta I, Alam N, Sun Z, Brini E, Dill K, Schueler-Furman O, Vajda S, Kozakov D. ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking. Proteins 2020; 88:1082-1090. [PMID: 32142178 DOI: 10.1002/prot.25887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/27/2020] [Accepted: 03/04/2020] [Indexed: 01/01/2023]
Abstract
Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Acpharis Inc., Holliston, Massachusetts, USA
| | - 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, Innopolis, Russia
| | - 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
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - 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
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15
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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16
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Guven-Maiorov E, Tsai CJ, Nussinov R. Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry. Front Oncol 2019; 9:1236. [PMID: 31803618 PMCID: PMC6872517 DOI: 10.3389/fonc.2019.01236] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/28/2019] [Indexed: 01/17/2023] Open
Abstract
Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces—interfaces—of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States.,Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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17
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Clancy JW, Zhang Y, Sheehan C, D'Souza-Schorey C. An ARF6-Exportin-5 axis delivers pre-miRNA cargo to tumour microvesicles. Nat Cell Biol 2019; 21:856-66. [PMID: 31235936 DOI: 10.1038/s41556-019-0345-y] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 05/16/2019] [Indexed: 02/07/2023]
Abstract
Tumor-derived microvesicles (TMVs) comprise a class of extracellular vesicles released from tumor cells that are now understood to facilitate communication between the tumor and the surrounding microenvironment. Despite their significance, the regulatory mechanisms governing the trafficking of bioactive cargos to TMVs at the cell surface remain poorly defined. Here we describe a molecular pathway for the delivery of microRNA (miRNA) cargo to nascent TMVs involving the dissociation of a pre-miRNA/Exportin-5 complex from Ran-GTP following nuclear export, and its subsequent transfer to a cytoplasmic shuttle comprised of ARF6-GTP and GRP1. As such, ARF6 activation increases pre-miRNA cargo contained within TMVs via a process that requires casein kinase 2-mediated phosphorylation of Ran-GAP1. Further, TMVs were found to contain pre-miRNA processing machinery including Dicer and Argonaute 2, which allow for cell-free pre-miRNA processing within shed vesicles. These findings offer cellular targets to block the loading and processing of pre-miRNAs within TMVs.
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18
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Halder AK, Dutta P, Kundu M, Basu S, Nasipuri M. Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions. Brief Funct Genomics 2019; 17:381-391. [PMID: 29028879 PMCID: PMC7109800 DOI: 10.1093/bfgp/elx026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Identification of potential virus–host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus–host interactions for infectious diseases followed by a case study on EBOV–human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host–pathogen relationships.
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Affiliation(s)
- Anup Kumar Halder
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Pritha Dutta
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mahantapas Kundu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, India
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19
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Abstract
About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.
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20
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Abstract
Biological systems are made of complex networks non-covalent interactions observed among protein-protein, protein-DNA, proteinlipid complexes using hydrogen-bonds, salt-bridges, aromatic-aromatic, van der Waals (vdW), hydrophobic-interactions and several others using distance criteria. Hence, large-scale data analysis is required to understand the principles of biological complex formation. Therefore, it is of interest to analyze non-covalent interaction namely, salt-bridge and aromatic-aromatic contacts in known and modeled protein complex structures. Here, we describe ASBAAC for automatic calculation of salt-bridges and aromatic-aromatic contacts in protein complexes. This software tool is fast, robust and user-friendly for large-scale analysis of inter-chain salt bridges and aromatic-aromatic contact in protein complexes. AVAILABILITY ASBAAC is available for free at http://sourceforge.net/projects/asbaac.
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Affiliation(s)
- Chittran Roy
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research - Indian Institute of Chemical Biology, 4 Raja SC Mullick Road, Jadavpur, Kolkata-700032, West Bengal, India
| | - Saumen Datta
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research - Indian Institute of Chemical Biology, 4 Raja SC Mullick Road, Jadavpur, Kolkata-700032, West Bengal, India
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21
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Abstract
Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodynamic stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quantitative and qualitative manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissociation free energy of subunits by analyzing the structural and topological properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topological properties of a protein binding patch are decoded by recurrence quantification analysis. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.
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Affiliation(s)
- Hamid Hadi-Alijanvand
- Department of Biological Sciences , Institute for Advanced Studies in Basic Sciences (IASBS) , Zanjan , 45137-66731 , Iran
| | - Maryam Rouhani
- Department of Biological Sciences , Institute for Advanced Studies in Basic Sciences (IASBS) , Zanjan , 45137-66731 , Iran
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22
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Ni D, Liu D, Zhang J, Lu S. Computational Insights into the Interactions between Calmodulin and the c/nSH2 Domains of p85α Regulatory Subunit of PI3Kα: Implication for PI3Kα Activation by Calmodulin. Int J Mol Sci 2018; 19:E151. [PMID: 29300353 DOI: 10.3390/ijms19010151] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 12/18/2017] [Accepted: 12/26/2017] [Indexed: 12/15/2022] Open
Abstract
Calmodulin (CaM) and phosphatidylinositide-3 kinase (PI3Kα) are well known for their multiple roles in a series of intracellular signaling pathways and in the progression of several human cancers. Crosstalk between CaM and PI3Kα has been an area of intensive research. Recent experiments have shown that in adenocarcinoma, K-Ras4B is involved in the CaM-PI3Kα crosstalk. Based on experimental results, we have recently put forward a hypothesis that the coordination of CaM and PI3Kα with K-Ras4B forms a CaM-PI3Kα-K-Ras4B ternary complex, which leads to the formation of pancreatic ductal adenocarcinoma. However, the mechanism for the CaM-PI3Kα crosstalk is unresolved. Based on molecular modeling and molecular dynamics simulations, here we explored the potential interactions between CaM and the c/nSH2 domains of p85α subunit of PI3Kα. We demonstrated that CaM can interact with the c/nSH2 domains and the interaction details were unraveled. Moreover, the possible modes for the CaM-cSH2 and CaM-nSH2 interactions were uncovered and we used them to construct a complete CaM-PI3Kα complex model. The structural model of CaM-PI3Kα interaction not only offers a support for our previous ternary complex hypothesis, but also is useful for drug design targeted at CaM-PI3Kα protein-protein interactions.
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23
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Spadafore M, Najarian K, Boyle AP. A proximity-based graph clustering method for the identification and application of transcription factor clusters. BMC Bioinformatics 2017; 18:530. [PMID: 29187152 PMCID: PMC5706350 DOI: 10.1186/s12859-017-1935-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 11/14/2017] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. METHODS Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. RESULTS We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. CONCLUSION The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.
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Affiliation(s)
- Maxwell Spadafore
- University of Michigan Medical School, 1301 Catherine, Ann Arbor, 48109-5624 USA
| | - Kayvan Najarian
- University of Michigan Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, 48109 USA
- University of Michigan Medical School Department of Emergency Medicine, 1500 E Medical Center Drive, Ann Arbor, 48109 USA
| | - Alan P. Boyle
- University of Michigan Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, 48109 USA
- University of Michigan Department of Genetics, 1241 E Catherine, Ann Arbor, 48109 USA
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24
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer. J Mol Biol 2017; 429:3925-3941. [PMID: 29106933 DOI: 10.1016/j.jmb.2017.10.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 02/07/2023]
Abstract
There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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25
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Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. Prediction of Protein-Protein Interactions by Evidence Combining Methods. Int J Mol Sci 2016; 17:ijms17111946. [PMID: 27879651 PMCID: PMC5133940 DOI: 10.3390/ijms17111946] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/15/2016] [Accepted: 11/15/2016] [Indexed: 12/27/2022] Open
Abstract
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
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Affiliation(s)
- Ji-Wei Chang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yan-Qing Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Muhammad Tahir Ul Qamar
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yu-Duan Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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26
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Tabaro F, Minervini G, Sundus F, Quaglia F, Leonardi E, Piovesan D, Tosatto SC. VHLdb: A database of von Hippel-Lindau protein interactors and mutations. Sci Rep 2016; 6:31128. [PMID: 27511743 DOI: 10.1038/srep31128] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 07/08/2016] [Indexed: 01/15/2023] Open
Abstract
Mutations in von Hippel-Lindau tumor suppressor protein (pVHL) predispose to develop
tumors affecting specific target organs, such as the retina, epididymis, adrenal
glands, pancreas and kidneys. Currently, more than 400 pVHL interacting
proteins are either described in the literature or predicted in public databases.
This data is scattered among several different sources, slowing down the
comprehension of pVHL’s biological role. Here we present VHLdb, a novel
database collecting available interaction and mutation data on pVHL to provide novel
integrated annotations. In VHLdb, pVHL interactors are organized according to two
annotation levels, manual and automatic. Mutation data are easily accessible and a
novel visualization tool has been implemented. A user-friendly feedback function to
improve database content through community-driven curation is also provided. VHLdb
presently contains 478 interactors, of which 117 have been manually curated, and
1,074 mutations. This makes it the largest available database for pVHL-related
information. VHLdb is available from URL: http://vhldb.bio.unipd.it/.
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27
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Sukhwal A, Sowdhamini R. PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots. Bioinform Biol Insights 2015; 9:141-51. [PMID: 26448684 PMCID: PMC4578551 DOI: 10.4137/bbi.s25928] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 04/21/2015] [Accepted: 04/28/2015] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Modeling protein-protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein-protein interfaces, which contribute more in stabilizing a protein-protein interface. RESULTS We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate 'native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning. CONCLUSIONS PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein-protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.
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Affiliation(s)
- Anshul Sukhwal
- National Centre for Biological Sciences, Bangalore, Karnataka, India. ; SASTRA University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India
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Xie ZR, Chen J, Zhao Y, Wu Y. Decomposing the space of protein quaternary structures with the interface fragment pair library. BMC Bioinformatics 2015; 16:14. [PMID: 25592649 PMCID: PMC4384354 DOI: 10.1186/s12859-014-0437-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 12/18/2014] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation. RESULTS Here we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library. CONCLUSIONS Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.
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Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yilin Zhao
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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Mirceva G, Kulakov A. Improvement of protein binding sites prediction by selecting amino acid residues' features. J Struct Biol 2014; 189:9-19. [PMID: 25478969 DOI: 10.1016/j.jsb.2014.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Revised: 08/30/2014] [Accepted: 11/23/2014] [Indexed: 11/17/2022]
Abstract
One of the main focuses of bioinformatics community is the study of the relationship between the structure of the protein molecules and their functions. In the literature, there are various methods that consider different protein-derived information for predicting protein functions. In our research, we focus on predicting the protein binding sites, which could be used to functionally annotate the protein structures. In this paper we consider a set of sixteen amino acid residues' features, and by applying various feature selection techniques we estimate their significance. Although the number of features in our case is not high, we perform feature selection in order to improve the prediction power and time complexity of the prediction models. The results show that by applying proper feature selection technique, the predictive performance of the classification algorithms is improved, i.e., by considering the most relevant features we induce more accurate models than if we consider the entire set of features. Furthermore, the model complexity, as well as the training and testing times are decreased by performing feature selection. We also compare our approach with several existing methods for protein binding sites prediction. The results demonstrate that the existing methods considered in this research are specific and applicable to the group of proteins for which the model was developed, while our approach is more generic and can be applied to a wider class of proteins.
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Affiliation(s)
- Georgina Mirceva
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, Macedonia.
| | - Andrea Kulakov
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, Macedonia.
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Papanikolaou N, Pavlopoulos GA, Theodosiou T, Iliopoulos I. Protein-protein interaction predictions using text mining methods. Methods 2015; 74:47-53. [PMID: 25448298 DOI: 10.1016/j.ymeth.2014.10.026] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 09/05/2014] [Accepted: 10/21/2014] [Indexed: 01/10/2023] Open
Abstract
It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.
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31
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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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Kleinjung J, Fraternali F. Design and application of implicit solvent models in biomolecular simulations. Curr Opin Struct Biol 2014; 25:126-34. [PMID: 24841242 PMCID: PMC4045398 DOI: 10.1016/j.sbi.2014.04.003] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 04/07/2014] [Accepted: 04/09/2014] [Indexed: 11/23/2022]
Abstract
Implicit solvent replaces explicit water by a potential of mean force. Popular models are SASA, VOL and Generalized Born. Implicit solvent is used in MD, protein modelling, folding, design, prediction and drug screening. Large-scale simulations allow for parametrisation via force matching. Application to nucleic acids and membranes is challenging.
We review implicit solvent models and their parametrisation by introducing the concepts and recent devlopments of the most popular models with a focus on parametrisation via force matching. An overview of recent applications of the solvation energy term in protein dynamics, modelling, design and prediction is given to illustrate the usability and versatility of implicit solvation in reproducing the physical behaviour of biomolecular systems. Limitations of implicit modes are discussed through the example of more challenging systems like nucleic acids and membranes.
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Affiliation(s)
- Jens Kleinjung
- Division of Mathematical Biology, MRC National Institute for Medical Research, The Ridgeway, London NW7 1AA, United Kingdom
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, New Hunt's House, London SE1 1UL, United Kingdom.
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Szilagyi A, Zhang Y. Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 2014; 24:10-23. [PMID: 24721449 DOI: 10.1016/j.sbi.2013.11.005] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/29/2013] [Accepted: 11/21/2013] [Indexed: 01/21/2023]
Abstract
The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.
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Engin HB, Guney E, Keskin O, Oliva B, Gursoy A. Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer. PLoS One 2013; 8:e81035. [PMID: 24278371 PMCID: PMC3838352 DOI: 10.1371/journal.pone.0081035] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Accepted: 10/05/2013] [Indexed: 11/18/2022] Open
Abstract
Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the "guilt-by-association" principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.
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Affiliation(s)
- H. Billur Engin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Emre Guney
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Baldo Oliva
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
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Kundrotas PJ, Vakser IA. Global and local structural similarity in protein-protein complexes: implications for template-based docking. Proteins 2013; 81:2137-42. [PMID: 23946125 DOI: 10.1002/prot.24392] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 07/23/2013] [Accepted: 08/02/2013] [Indexed: 02/02/2023]
Abstract
The increasing amount of structural information on protein-protein interactions makes it possible to predict the structure of protein-protein complexes by comparison/alignment of the interacting proteins to the ones in cocrystallized complexes. In the predictions based on structure similarity, the template search is performed by structural alignment of the target interactors with the entire structures or with the interface only of the subunits in cocrystallized complexes. This study investigates the scope of the structural similarity that facilitates the detection of a broad range of templates significantly divergent from the targets. The analysis of the target-template similarity is based on models of protein-protein complexes in a large representative set of heterodimers. The similarity of the biological and crystal packing interfaces, dissimilar interface structural motifs in overall similar structures, interface similarity to the full structure, and local similarity away from the interface were analyzed. The structural similarity at the protein-protein interfaces only was observed in ~25% of target-template pairs with sequence identity <20% and primarily homodimeric templates. For ~50% of the target-template pairs, the similarity at the interface was accompanied by the similarity of the whole structure. However, the structural similarity at the interfaces was still stronger than that of the noninterface parts. The study provides insights into structural and functional diversity of protein-protein complexes, and relative performance of the interface and full structure alignment in docking.
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Abstract
Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.
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Abstract
The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD <2.5 Å by SPRING is 134% and 167% higher than these competing methods. SPRING is controlled with ZDOCK on 77 docking benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.
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Affiliation(s)
- Aysam Guerler
- Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan, 48109, United States
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38
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Abstract
Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.
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Affiliation(s)
- Pascal Braun
- Department of Plant Systems Biology, Center for Life and Food Sciences Weihenstephan, Technische Universität München (TUM), 85354 Freising-Weihenstephan, Germany.
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Abstract
Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities--structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein-protein interactions and the insight these approaches have provided on the nature and organization of protein-protein interactions inside cells.
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Affiliation(s)
- Yogesh Hooda
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
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40
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Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 2012; 490:556-60. [PMID: 23023127 DOI: 10.1038/nature11503] [Citation(s) in RCA: 475] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Accepted: 08/10/2012] [Indexed: 12/23/2022]
Abstract
The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression, and phylogenetic profiles have also been developed for the genome-wide inference of protein-protein interactions (PPIs)5,6. Yet, comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7–9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, PrePPI, that combines structural information with other functional clues is comparable in accuracy to high-throughput experiments, yielding over 30,000 high confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of significant biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.
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Kundrotas PJ, Zhu Z, Vakser IA. GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions. Hum Genomics 2012; 6:7. [PMID: 23245398 PMCID: PMC3500202 DOI: 10.1186/1479-7364-6-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 07/11/2012] [Indexed: 11/10/2022] Open
Abstract
Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.
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Kundrotas PJ, Zhu Z, Janin J, Vakser IA. Templates are available to model nearly all complexes of structurally characterized proteins. Proc Natl Acad Sci U S A 2012; 109:9438-41. [PMID: 22645367 DOI: 10.1073/pnas.1200678109] [Citation(s) in RCA: 161] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Traditional approaches to protein-protein docking sample the binding modes with no regard to similar experimentally determined structures (templates) of protein-protein complexes. Emerging template-based docking approaches utilize such similar complexes to determine the docking predictions. The docking problem assumes the knowledge of the participating proteins' structures. Thus, it provides the possibility of aligning the structures of the proteins and the template complexes. The progress in the development of template-based docking and the vast experience in template-based modeling of individual proteins show that, generally, such approaches are more reliable than the free modeling. The key aspect of this modeling paradigm is the availability of the templates. The current common perception is that due to the difficulties in experimental structure determination of protein-protein complexes, the pool of docking templates is insignificant, and thus a broad application of template-based docking is possible only at some future time. The results of our large scale, systematic study show that, surprisingly, in spite of the limited number of protein-protein complexes in the Protein Data Bank, docking templates can be found for complexes representing almost all the known protein-protein interactions, provided the components themselves have a known structure or can be homology-built. About one-third of the templates are of good quality when they are compared to experimental structures in test sets extracted from the Protein Data Bank and would be useful starting points in modeling the complexes. This finding dramatically expands our ability to model protein interactions, and has far-reaching implications for the protein docking field in general.
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Garcia-Garcia J, Bonet J, Guney E, Fornes O, Planas J, Oliva B. Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details. Mol Inform 2012; 31:342-62. [PMID: 27477264 DOI: 10.1002/minf.201200005] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 03/09/2012] [Indexed: 11/08/2022]
Abstract
Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored.
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Affiliation(s)
- Javier Garcia-Garcia
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain
| | - Jaume Bonet
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain
| | - Emre Guney
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain
| | - Oriol Fornes
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain
| | - Joan Planas
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain
| | - Baldo Oliva
- Structural Bioinformatics Group, GRIB-IMIM, Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Catalonia, Spain.
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Abstract
The increasing availability of co-crystallized protein-protein complexes provides an opportunity to use template-based modeling for protein-protein docking. Structure alignment techniques are useful in detection of remote target-template similarities. The size of the structure involved in the alignment is important for the success in modeling. This paper describes a systematic large-scale study to find the optimal definition/size of the interfaces for the structure alignment-based docking applications. The results showed that structural areas corresponding to the cutoff values <12 Å across the interface inadequately represent structural details of the interfaces. With the increase of the cutoff beyond 12 Å, the success rate for the benchmark set of 99 protein complexes, did not increase significantly for higher accuracy models, and decreased for lower-accuracy models. The 12 Å cutoff was optimal in our interface alignment-based docking, and a likely best choice for the large-scale (e.g., on the scale of the entire genome) applications to protein interaction networks. The results provide guidelines for the docking approaches, including high-throughput applications to modeled structures.
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Affiliation(s)
- Rohita Sinha
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
| | - Petras J. Kundrotas
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (PJK); (IAV)
| | - Ilya A. Vakser
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (PJK); (IAV)
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45
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Nussinov R, Tsai CJ, Csermely P. Allo-network drugs: harnessing allostery in cellular networks. Trends Pharmacol Sci 2011; 32:686-93. [PMID: 21925743 DOI: 10.1016/j.tips.2011.08.004] [Citation(s) in RCA: 110] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2011] [Revised: 08/16/2011] [Accepted: 08/19/2011] [Indexed: 10/17/2022]
Abstract
Allosteric drugs are increasingly used because they produce fewer side effects. Allosteric signal propagation does not stop at the 'end' of a protein, but may be dynamically transmitted across the cell. We propose here that the concept of allosteric drugs can be broadened to 'allo-network drugs' - whose effects can propagate either within a protein, or across several proteins, to enhance or inhibit specific interactions along a pathway. We posit that current allosteric drugs are a special case of allo-network drugs, and suggest that allo-network drugs can achieve specific, limited changes at the systems level, and in this way can achieve fewer side effects and lower toxicity. Finally, we propose steps and methods to identify allo-network drug targets and sites that outline a new paradigm in systems-based drug design.
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Affiliation(s)
- Ruth Nussinov
- Center for Cancer Research Nanobiology Program, Science Applications International Corporation-Frederick, National Cancer Institute-Frederick, Frederick, MD 21702, USA.
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Bhardwaj N, Abyzov A, Clarke D, Shou C, Gerstein MB. Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions. Protein Sci 2011; 20:1745-54. [PMID: 21826754 DOI: 10.1002/pro.710] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2011] [Revised: 07/05/2011] [Accepted: 07/19/2011] [Indexed: 11/09/2022]
Abstract
The integration of molecular networks with other types of data, such as changing levels of gene expression or protein-structural features, can provide richer information about interactions than the simple node-and-edge representations commonly used in the network community. For example, the mapping of 3D-structural data onto networks enables classification of proteins into singlish- or multi-interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi-interface hubs display a greater degree of conformational change than do singlish-interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.
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Tuncbag N, Gursoy A, Nussinov R, Keskin O. Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc 2011; 6:1341-54. [PMID: 21886100 DOI: 10.1038/nprot.2011.367] [Citation(s) in RCA: 206] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.
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Abstract
Interactions between proteins play a key role in many cellular processes.
Studying protein-protein interactions that share similar interaction interfaces
may shed light on their evolution and could be helpful in elucidating the
mechanisms behind stability and dynamics of the protein complexes. When two
complexes share structurally similar subunits, the similarity of the interaction
interfaces can be found through a structural superposition of the subunits.
However, an accurate detection of similarity between the protein complexes
containing subunits of unrelated structure remains an open problem. Here, we present an alignment-free machine learning approach to measure interface
similarity. The approach relies on the feature-based representation of protein
interfaces and does not depend on the superposition of the interacting subunit
pairs. Specifically, we develop an SVM classifier of similar and dissimilar
interfaces and derive a feature-based interface similarity measure. Next, the
similarity measure is applied to a set of 2,806×2,806 binary complex pairs
to build a hierarchical classification of protein-protein interactions. Finally,
we explore case studies of similar interfaces from each level of the hierarchy,
considering cases when the subunits forming interactions are either homologous
or structurally unrelated. The analysis has suggested that the positions of
charged residues in the homologous interfaces are not necessarily conserved and
may exhibit more complex conservation patterns.
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Affiliation(s)
- Nan Zhao
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Bin Pang
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Chi-Ren Shyu
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Dmitry Korkin
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
- Bond Life Science Center, University of
Missouri, Columbia, Missouri, United States of America
- * E-mail:
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Koch O. Use of secondary structure element information in drug design: polypharmacology and conserved motifs in protein–ligand binding and protein–protein interfaces. Future Med Chem 2011; 3:699-708. [DOI: 10.4155/fmc.11.26] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The structure-based design of small-molecule inhibitors of protein–ligand and protein–protein interfaces is a key component of drug discovery. The underlying protein interactions can be regarded based on structural similarity of the secondary structure elements: similarities around the binding site (‘ligand-sensing cores’) or in the protein interface (‘interface-sensing surfaces’) in otherwise unrelated proteins can be useful in predicting polypharmacology and identifying new lead structures. Even small conserved motifs can provide similar interaction patterns in proteins with a completely different fold and function. The identification of these structural similarities can help in the design of new drugs by guiding further optimization. Here, the concepts and ideas based on secondary structure element similarities and their successful applications in drug design are reviewed and discussed.
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