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A holistic molecular docking approach for predicting protein-protein complex structure

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Science China(Life Sciences) 2010年第9期53卷 1152-1161页

作者： GONG XinQi LIU Bin CHANG Shan LI ChunHua CHEN WeiZu WANG CunXin College of Life Science and Bioengineering Beijing University of TechnologyBeijing 100124China

A holistic protein-protein molecular docking approach,HoDock,was established,composed of such steps as binding site prediction,initial complex structure sampling,refined complex structure sampling,structure clustering,scoring and final structure *** article explains the detailed steps and applications for CAPRI Target *** CAPRI result showed that three predicted binding site residues,A191HIS,B512ARG and B531ARG,were correct,and there were five submitted structures with a high fraction of correct receptor-ligand interface residues,indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.

关键词： protein-protein complex structure prediction binding site molecular docking CAPRI

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells

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World Journal of Gastroenterology 2019年第9期25卷 1067-1079页

作者： Xing-Qiu Liang Jian Liang Xiao-Fang Zhao Xin-Yuan Wang Xin Deng Department of Science and Technology Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine College of Medical Guangxi University School of Basic Sciences Guangxi University of Chinese Medicine

BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver *** To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC *** We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape *** A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, inclu

关键词： Taurine Hepatic stellate cells Differentially expressed genes Liver fibrogenesis Transcriptomic protein-protein interaction network

Construction of protein-protein interaction network based ontranscriptome profiling of ovine granulosa cells during thesheep＇s anestrus phase

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Frontiers in Biology 2018年第3期13卷 215-225页

作者： Reza Talebi Ahmad Ahmadi Fazlollah Afraz Department of Animal Sciences Faculty of Agriculture Bu-Ali Sina University Hamedan Iran Department of Livestock and Aquaculture Biotechnology Agricultural Biotechnology Research Institute of North Region Rasht Iran

Small antral follicles as the final reserve of folliculogenesis, are existed throughout the reproductive life span of sheep. However, the ovarian cycles of ewe cease in the anestrus phase. This study in thus was aimed to elucidate the ovarian small antral follicles transcriptome in the ewe’s anestrus phase. Granulosa cells of small antral follicles (#3 mm) were collected from ovaries of anestrous ewes under long days of summer in the non-breeding season as anestrus phase. Transcriptome profiling of these granulosa cells were obtained using the RNA-Seq technology. An integrative analysis was utilized to identify key regulatory genes whose may have potential impacts on intra-ovarian molecular activities. Globally, 14506 genes were expressed whose higher expressions were belonged to genes that encoded ribosomal proteins. Top significant terms of gene ontology were pertained to protein translational processes. Apart of this, most of highly significant terms were also relevant to apoptotic process through extracellular vesicles, including apoptotic bodies and exosomes. Regarding to node effect property, UBA52 (ubiquitin A-52 residue ribosomal protein fusion product 1) and RPS5 (ribosomal proteins S5) contained in highest out-degree and in-degree, respectively. Our data suggest that ribosomal mRNA/proteins could make the granulosa cells undergo a lot of changes from the point of view that ovarian activities are ceased in the anestrus phase.

关键词： RNA-sequencing, ovarian follicles, anestrus phase, protein-protein interaction network, functional modules

Construct protein-protein Interaction Network by Mining Domain-Domain Interactions

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Journal of Harbin Institute of Technology(New Series) 2016年第4期23卷 27-36页

作者： Zhixia Teng Maozu Guo Xiaoyan Liu Jin Li Qiguo Dai Chunyu Wang Department of Computer Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Information Management and Information System Northeast Forestry UniversityHarbin 150040China

Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are conservative and commonplace in proteins,domain-domain interactions are discovered based on pairs of domains which significantly co-exist in proteins. Meanwhile,it is realized that:( 1) domain-domain interactions may exist within the same proteins or across different proteins;( 2) only the domain-domain interactions across different proteins can mediate interactions between proteins;( 3) domains have biases to interact with other domains. And then,a novel method is put forward to construct protein-protein interaction network by using domain-domain interactions. The method is validated by experiments and compared with the state- of-art methods in the field. The experimental results suggest that the method is reasonable and effectiveness on constructing protein-protein interactions network.

关键词： protein-protein interaction domain-domain interaction statistical significance testCLC number: TP391, Q811.4 Document code: A Article ID: 1005-9113(2016)04-0027-10

Prediction of protein-protein Interactions by a Novel Model Based on Domain Information

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Journal of Donghua University(English Edition) 2018年第2期35卷 163-169页

作者：董露露谢飞章程李斌 Center of Anhui Continuing Education Online Anhui Radio and TV University School of Computer Science and Technology Hefei Normal University College of Computer Science and Technology Anhui University

Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.

关键词： domain-based protein-protein interactions (PPIs) multi-instance learning amino acid composition ( AAC) pseudo-amino acidcomposition (PseAAC)

protein-protein Interaction Extraction Based on Convex Combination Kernel Function

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Journal of Computer and Communications 2013年第5期1卷 9-13页

作者： Peng Chen Jianyi Guo Zhengtao Yu Sichao Wei Feng Zhou Xin Yan The School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology Kunming China.

Owing to the effect of classified models was different in protein-protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.

关键词： protein-protein Interaction Support Vector Machine Convex Combination Kernel Function

Predicting residue contacts for protein-protein interactions by integration of multiple information

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Journal of Biomedical Science and Engineering 2014年第1期7卷 28-37页

作者： Tu Kien T. Le Osamu Hirose Vu Anh Tran Thammakorn Saethang Lan Anh T. Nguyen Xuan Tho Dang Duc Luu Ngo Mamoru Kubo Yoichi Yamada Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, this is one of the most difficult tasks and current methods are constrained by some factors. In this study, we developed a new method to predict residue-residue contacts of two interacting protein domains by integrating information about evolutionary couplings andamino acid pairwise contact potentials, as well as domain-domain interaction interfaces. The experimental results showed that our proposed method outperformed the previous method with the same datasets. Moreover, the method promises an improvement in the source of template-based protein docking.

关键词： Residue-Residue Contacts Domain-Domain Interactions protein-protein Interactions Domain Interfaces Residue Co-Evolution Contact Potentials

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

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Science China(Life Sciences) 2007年第1期50卷 125-134页

作者： GAO Lei1, LI Xia1,2, GUO Zheng1,2, ZHU MingZhu1, LI YanHui1 & RAO ShaoQi1,3 1 Department of Bioinformatics, Harbin Medical University, Harbin 150086, China 2 School of Biology Science and Technology, Tongji University, Shanghai 200092, China 3 Departments of Molecular Cardiology and Cardiovascular Medicine, the Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleve-land, Ohio 44195, USA Department of Bioinformatics Harbin Medical University Harbin 150086 China Department of Bioinformatics Harbin Medical University Harbin 150086 China School of Biology Science and Technology Tongji University Shanghai 200092 China Department of Bioinformatics Harbin Medical University Harbin 150086 China Departments of Molecular Cardiology and Cardiovascular Medicine the Cleveland Clinic Foundation 9500 Euclid Avenue Cleveland Ohio 44195 USA

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

关键词： gene expression profile protein-protein interaction Gene Ontology similarity gene function prediction

Factors That Affect the Computational Prediction of Hot Spots in protein-protein Complexes

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Computational Molecular Bioscience 2012年第1期2卷 23-34页

作者： Jianping Lin Pi Liu Hua-Zheng Yang Nagarajan Vaidehi College of Pharmacy Nankai University Tianjin China Department of Immunology Beckman Research Institute of the City of Hope Duarte USA State Key Laboratory of Elemento-Organic Chemistry Elemento-Organic Chemistry Institute Nankai University Tianjin China

protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts for most of the protein-protein binding free energy. Computational methods play a critical role in identifying the hotspots on the proteinprotein interface. In this paper, we use a computational alanine scanning method with all-atom force fields for predicting hotspots for 313 mutations in 16 protein complexes of known structures. We studied the effect of force fields, solvation models, and conformational sampling on the hotspot predictions. We compared the calculated change in the protein-protein interaction energies upon mutation of the residues in and near the protein-protein interface, to the experimental change in free energies. The AMBER force field (FF) predicted 86% of the hotspots among the three commonly used FF for proteins, namely, AMBER FF, Charmm27 FF, and OPLS-2005 FF. However, AMBER FF also showed a high rate of false positives, while the Charmm27 FF yielded 74% correct predictions of the hotspot residues with low false positives. Van der Waals and hydrogen bonding energy show the largest energy contribution with a high rate of prediction accuracy, while the desolvation energy was found to contribute little to improve the hot spot prediction. Using a conformational ensemble including limited backbone movement instead of one static structure leads to better predicttion of hotpsots.

关键词： Hotspot Prediction Computational Mutagenesis Concoord Ensemble protein-protein Complexes