1
|
Zandi F, Mansouri P, Goodarzi M. Global protein-protein interaction networks in yeast saccharomyces cerevisiae and helicobacter pylori. Talanta 2023; 265:124836. [PMID: 37393709 DOI: 10.1016/j.talanta.2023.124836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/04/2023] [Accepted: 06/17/2023] [Indexed: 07/04/2023]
Abstract
Understanding many biological processes relies heavily on accurately predicting protein-protein interactions (PPIs). In this study, we propose a novel method for predicting PPIs that is based on LogitBoost with a binary bat feature selection algorithm. Our approach involves the extraction of an initial feature vector by combining pseudo amino acid composition (PseAAC), pseudo-position-specific scoring matrix (PsePSSM), reduced sequence and index-vectors (RSIV), and autocorrelation descriptor (AD). Subsequently, a binary bat algorithm is applied to eliminate redundant features, and the resulting optimal features are fed into the LogitBoost classifier for the identification of PPIs. To evaluate the proposed method, we test it on two databases, Saccharomyces cerevisiae and Helicobacter pylori, using 10-fold cross-validation, and achieve accuracies of 94.39% and 97.89%, respectively. Our results showcase the significant potential of our pipeline in accurately predicting protein-protein interactions (PPIs), thereby offering a valuable resource to the scientific research community.
Collapse
Affiliation(s)
- Farzad Zandi
- Faculty of Sciences, Islamic Azad University, Arak Branch, Arak, Markazi, Iran
| | | | - Mohammad Goodarzi
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| |
Collapse
|
2
|
Wang X, Zhang Y, Yu B, Salhi A, Chen R, Wang L, Liu Z. Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis. Comput Biol Med 2021; 134:104516. [PMID: 34119922 DOI: 10.1016/j.compbiomed.2021.104516] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 12/22/2022]
Abstract
Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.
Collapse
Affiliation(s)
- Xue Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Yaqun Zhang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China; Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, China.
| | - Adil Salhi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Ruixin Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Lin Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Zengfeng Liu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| |
Collapse
|
3
|
Xie Z, Deng X, Shu K. Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int J Mol Sci 2020; 21:E467. [PMID: 31940793 PMCID: PMC7013409 DOI: 10.3390/ijms21020467] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/20/2022] Open
Abstract
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.
Collapse
Affiliation(s)
- Zengyan Xie
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| | | | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| |
Collapse
|
4
|
Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features. Molecules 2018; 23:molecules23040823. [PMID: 29617272 PMCID: PMC6017726 DOI: 10.3390/molecules23040823] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 03/25/2018] [Accepted: 03/29/2018] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of cells; thus, detecting PPIs is one of the most important issues in current molecular biology. Although much effort has been devoted to using high-throughput techniques to identify protein-protein interactions, the experimental methods are both time-consuming and costly. In addition, they yield high rates of false positive and false negative results. In addition, most of the proposed computational methods are limited in information about protein homology or the interaction marks of the protein partners. In this paper, we report a computational method only using the information from protein sequences. The main improvements come from novel protein sequence representation by combing the continuous and discrete wavelet transforms and from adopting weighted sparse representation-based classifier (WSRC). The proposed method was used to predict PPIs from three different datasets: yeast, human and H. pylori. In addition, we employed the prediction model trained on the PPIs dataset of yeast to predict the PPIs of six datasets of other species. To further evaluate the performance of the prediction model, we compared WSRC with the state-of-the-art support vector machine classifier. When predicting PPIs of yeast, humans and H. pylori dataset, we obtained high average prediction accuracies of 97.38%, 98.92% and 93.93% respectively. In the cross-species experiments, most of the prediction accuracies are over 94%. These promising results show that the proposed method is indeed capable of obtaining higher performance in PPIs detection.
Collapse
|
5
|
Qiu Z, Zhou B, Yuan J. Protein–protein interaction site predictions with minimum covariance determinant and Mahalanobis distance. J Theor Biol 2017; 433:57-63. [DOI: 10.1016/j.jtbi.2017.08.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/26/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
|
6
|
Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep 2017; 7:10480. [PMID: 28874689 PMCID: PMC5585393 DOI: 10.1038/s41598-017-09654-8] [Citation(s) in RCA: 488] [Impact Index Per Article: 69.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/28/2017] [Indexed: 01/01/2023] Open
Abstract
Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.
Collapse
Affiliation(s)
- Martino Bertoni
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Florian Kiefer
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Marco Biasini
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Lorenza Bordoli
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland. .,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland.
| |
Collapse
|
7
|
Jiao X, Ranganathan S. Prediction of interface residue based on the features of residue interaction network. J Theor Biol 2017; 432:49-54. [PMID: 28818468 DOI: 10.1016/j.jtbi.2017.08.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 07/31/2017] [Accepted: 08/13/2017] [Indexed: 10/19/2022]
Abstract
Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model.
Collapse
Affiliation(s)
- Xiong Jiao
- Institute of Applied Mechanics and Biomedical Engineering, College of Mechanics, Taiyuan University of Technology, Taiyuan 030024, China; Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia.
| | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| |
Collapse
|
8
|
Gurung AB, Das AK, Bhattacharjee A. Disruption of redox catalytic functions of peroxiredoxin-thioredoxin complex in Mycobacterium tuberculosis H37Rv using small interface binding molecules. Comput Biol Chem 2017; 67:69-83. [DOI: 10.1016/j.compbiolchem.2016.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 09/19/2016] [Accepted: 12/30/2016] [Indexed: 10/20/2022]
|
9
|
Raza K. Protein Features Identification for Machine Learning-Based Prediction of Protein-Protein Interactions. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2017:305-317. [DOI: 10.1007/978-981-10-6544-6_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
|
10
|
Huang YA, You ZH, Chen X, Yan GY. Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition. BMC SYSTEMS BIOLOGY 2016; 10:120. [PMID: 28155718 PMCID: PMC5260127 DOI: 10.1186/s12918-016-0360-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Background Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological technologies can be used to identify new PPIs, but they are expensive, time-consuming, and tedious. Therefore, computational methods for predicting PPIs have an important role. For the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting PPIs. The major limitation in principle of these methods lies in the prior information of the protein to infer PPIs. Therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence. Results Here, we report a highly efficient approach for predicting PPIs. The main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and Chou’s pseudo amino acid composition (PseAAC), and from adopting weighted sparse representation based classifier (WSRC). This method, cross-validated on the PPIs datasets of Saccharomyces cerevisiae, Human and H. pylori, achieves an excellent results with accuracies as high as 92.50%, 95.54% and 84.28% respectively, significantly better than previously proposed methods. Extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier. Conclusions The outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. In addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and WSRC. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
Collapse
Affiliation(s)
- Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
| | - Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Gui-Ying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100010, China
| |
Collapse
|
11
|
Huang YA, You ZH, Chen X, Chan K, Luo X. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinformatics 2016; 17:184. [PMID: 27112932 PMCID: PMC4845433 DOI: 10.1186/s12859-016-1035-4] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 04/12/2016] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information. RESULTS In this study, we present a novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence. Two kinds of protein descriptors, composition and transition, are extracted for representing each protein sequence. On the basis of such a feature representation, novel weighted sparse representation based classifier is introduced to predict protein interaction class. When the proposed method was evaluated with the PPIs data of S. cerevisiae, Human and H. pylori, it achieved high prediction accuracies of 96.82, 97.66 and 92.83 % respectively. Extensive experiments were performed for cross-species PPIs prediction and the prediction accuracies were also very promising. CONCLUSIONS To further evaluate the performance of the proposed method, we then compared its performance with the method based on support vector machine (SVM). The results show that the proposed method achieved a significant improvement. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
Collapse
Affiliation(s)
- Yu-An Huang
- />College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060 China
| | - Zhu-Hong You
- />School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116 China
| | - Xing Chen
- />Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 China
| | - Keith Chan
- />Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077 China
| | - Xin Luo
- />Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077 China
| |
Collapse
|
12
|
Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
Collapse
|
13
|
Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
Collapse
|
14
|
Wang B, Kennedy MA. Principal components analysis of protein sequence clusters. ACTA ACUST UNITED AC 2014; 15:1-11. [PMID: 24496727 DOI: 10.1007/s10969-014-9173-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 01/24/2014] [Indexed: 12/21/2022]
Abstract
Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large variable space, i.e. the number of amino acids that make up the protein sequence, and PCA is powerful at reducing the dimensionality of complex problems by projecting the data into an eigenspace that represents the directions of greatest variation. However, PCA of aligned protein sequence families is complicated by the fact that protein sequences are traditionally represented by single letter alphabetic codes, whereas PCA of protein sequence families requires conversion of sequence information into a numerical representation. Here, we introduce a new amino acid sequence conversion algorithm optimized for PCA data input. The method is demonstrated using a small artificial dataset to illustrate the characteristics and performance of the algorithm, as well as a small protein sequence family consisting of nine members, COG2263, and finally with a large protein sequence family, Pfam04237, which contains more than 1,800 sequences that group into two sub-clusters.
Collapse
Affiliation(s)
- Bo Wang
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH, 45056, USA
| | | |
Collapse
|
15
|
Fornili A, Pandini A, Lu HC, Fraternali F. Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles. J Chem Theory Comput 2013; 9:5127-5147. [PMID: 24250278 PMCID: PMC3827836 DOI: 10.1021/ct400486p] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2013] [Indexed: 12/13/2022]
Abstract
![]()
The
ability to interact with different partners is one of the most
important features in proteins. Proteins that bind a large number
of partners (hubs) have been often associated with intrinsic disorder.
However, many examples exist of hubs with an ordered structure, and
evidence of a general mechanism promoting promiscuity in ordered proteins
is still elusive. An intriguing hypothesis is that promiscuous binding
sites have specific dynamical properties, distinct from the rest of
the interface and pre-existing in the protein isolated state. Here,
we present the first comprehensive study of the intrinsic dynamics
of promiscuous residues in a large protein data set. Different computational
methods, from coarse-grained elastic models to geometry-based sampling
methods and to full-atom Molecular Dynamics simulations, were used
to generate conformational ensembles for the isolated proteins. The
flexibility and dynamic correlations of interface residues with a
different degree of binding promiscuity were calculated and compared
considering side chain and backbone motions, the latter both on a
local and on a global scale. The study revealed that (a) promiscuous
residues tend to be more flexible than nonpromiscuous ones, (b) this
additional flexibility has a higher degree of organization, and (c)
evolutionary conservation and binding promiscuity have opposite effects
on intrinsic dynamics. Findings on simulated ensembles were also validated
on ensembles of experimental structures extracted from the Protein
Data Bank (PDB). Additionally, the low occurrence of single nucleotide
polymorphisms observed for promiscuous residues indicated a tendency
to preserve binding diversity at these positions. A case study on
two ubiquitin-like proteins exemplifies how binding promiscuity in
evolutionary related proteins can be modulated by the fine-tuning
of the interface dynamics. The interplay between promiscuity and flexibility
highlighted here can inspire new directions in protein–protein
interaction prediction and design methods.
Collapse
Affiliation(s)
- Arianna Fornili
- Randall Division of Cell and Molecular Biophysics, King's College London , New Hunt's House, London SE1 1UL, United Kingdom
| | | | | | | |
Collapse
|
16
|
Liu B, Wang X, Zou Q, Dong Q, Chen Q. Protein Remote Homology Detection by Combining Chou’s Pseudo Amino Acid Composition and Profile-Based Protein Representation. Mol Inform 2013; 32:775-82. [DOI: 10.1002/minf.201300084] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2013] [Accepted: 06/11/2013] [Indexed: 11/12/2022]
|
17
|
Mukherjee K, Abhipriya, Vidyarthi AS, Pandey DM. SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes. Bioinformation 2013; 9:500-5. [PMID: 23861565 PMCID: PMC3705624 DOI: 10.6026/97320630009500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 05/17/2013] [Indexed: 12/02/2022] Open
Abstract
Support vector machine is a class of machine learning algorithms which uses a set of related supervised learning methods
for classification and regression. Nowadays this method is vividly applied to many detection problems related with secondary
structure, tumor cell and binding residue prediction. In this work, support vector machines (SVMs) have been trained on 90
sequences of transcription factors with HTH motif. Four sequence features were used as attribute for the prediction of interaction
site in HTH motif. A web page was also developed so that user can easily enter the protein sequence and receive the output as
interaction site predicted or not predicted. The generated model shows a very high amount of accuracy, sensitivity and specificity
which proves to be a good model for the selected case.
Collapse
Affiliation(s)
- Koel Mukherjee
- Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi-835 215, Jharkhand, India
| | | | | | | |
Collapse
|
18
|
Chen P, Wong L, Li J. Detection of outlier residues for improving interface prediction in protein heterocomplexes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:1155-1165. [PMID: 22529331 DOI: 10.1109/tcbb.2012.58] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Sequence-based understanding and identification of protein binding interfaces is a challenging research topic due to the complexity in protein systems and the imbalanced distribution between interface and noninterface residues. This paper presents an outlier detection idea to address the redundancy problem in protein interaction data. The cleaned training data are then used for improving the prediction performance. We use three novel measures to describe the extent a residue is considered as an outlier in comparison to the other residues: the distance of a residue instance from the center instance of all residue instances of the same class label (Dist), the probability of the class label of the residue instance (PCL), and the importance of within-class and between-class (IWB) residue instances. Outlier scores are computed by integrating the three factors; instances with a sufficiently large score are treated as outliers and removed. The data sets without outliers are taken as input for a support vector machine (SVM) ensemble. The proposed SVM ensemble trained on input data without outliers performs better than that with outliers. Our method is also more accurate than many literature methods on benchmark data sets. From our empirical studies, we found that some outlier interface residues are truly near to noninterface regions, and some outlier noninterface residues are close to interface regions.
Collapse
Affiliation(s)
- Peng Chen
- Institute of Intelligent Machines, Chinese Academy of Sciences, PO Box 1130, Hefei 230031, China.
| | | | | |
Collapse
|
19
|
Partner-aware prediction of interacting residues in protein-protein complexes from sequence data. PLoS One 2011; 6:e29104. [PMID: 22194998 PMCID: PMC3237601 DOI: 10.1371/journal.pone.0029104] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2011] [Accepted: 11/21/2011] [Indexed: 12/22/2022] Open
Abstract
Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.
Collapse
|
20
|
Zellner H, Staudigel M, Trenner T, Bittkowski M, Wolowski V, Icking C, Merkl R. Prescont: Predicting protein-protein interfaces utilizing four residue properties. Proteins 2011; 80:154-68. [DOI: 10.1002/prot.23172] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2011] [Revised: 08/18/2011] [Accepted: 08/29/2011] [Indexed: 12/26/2022]
|
21
|
Qiu Z, Wang X. Prediction of protein-protein interaction sites using patch-based residue characterization. J Theor Biol 2011; 293:143-50. [PMID: 22037062 DOI: 10.1016/j.jtbi.2011.10.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2011] [Revised: 09/13/2011] [Accepted: 10/15/2011] [Indexed: 10/15/2022]
Abstract
Identifying protein-protein interaction sites provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Using a patch-based model for residue characterization, we trained random forest classifiers for residue-based interface prediction, which was followed by a clustering procedure to produce patches for patch-based interface prediction. For residue-based interface prediction, our method achieves a specificity rate of 0.7 and a sensitivity rate of 0.78. For patch-based interface prediction, a success rate of 0.80 is achieved. Based on same datasets, we also compare it with several published methods. The results show that our method is a successful predictor for residue-based and patch-based interface prediction.
Collapse
Affiliation(s)
- Zhijun Qiu
- The State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology, 2 Ling-Gong Road, Dalian 116024, China
| | | |
Collapse
|
22
|
Hayat S, Walter P, Park Y, Helms V. Prediction of the exposure status of transmembrane beta barrel residues from protein sequence. J Bioinform Comput Biol 2011; 9:43-65. [PMID: 21328706 DOI: 10.1142/s0219720011005240] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2010] [Revised: 09/21/2010] [Accepted: 09/22/2010] [Indexed: 11/18/2022]
Abstract
We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar C(α)-C(β) vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at http://service.bioinformatik.uni-saarland.de/tmx-site/. Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.
Collapse
Affiliation(s)
- Sikander Hayat
- Center for Bioinformatics, Saarland University, Germany.
| | | | | | | |
Collapse
|
23
|
Lensink MF, Wodak SJ. Blind predictions of protein interfaces by docking calculations in CAPRI. Proteins 2010; 78:3085-95. [DOI: 10.1002/prot.22850] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|
24
|
Chen P, Li J. Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information. BMC Bioinformatics 2010; 11:402. [PMID: 20667087 PMCID: PMC2921408 DOI: 10.1186/1471-2105-11-402] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2010] [Accepted: 07/28/2010] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone. RESULTS We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods. CONCLUSIONS The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance. AVAILABILITY Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.
Collapse
Affiliation(s)
- Peng Chen
- Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, 639798 Singapore
| | | |
Collapse
|
25
|
Liu B, Wang X, Lin L, Tang B, Dong Q, Wang X. Prediction of protein binding sites in protein structures using hidden Markov support vector machine. BMC Bioinformatics 2009; 10:381. [PMID: 19925685 PMCID: PMC2785799 DOI: 10.1186/1471-2105-10-381] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2009] [Accepted: 11/20/2009] [Indexed: 01/08/2023] Open
Abstract
Background Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance. Results In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods. Conclusion The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.
Collapse
Affiliation(s)
- Bin Liu
- Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China.
| | | | | | | | | | | |
Collapse
|
26
|
Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes. Protein J 2009; 28:369-74. [DOI: 10.1007/s10930-009-9203-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
27
|
Exploiting three kinds of interface propensities to identify protein binding sites. Comput Biol Chem 2009; 33:303-11. [DOI: 10.1016/j.compbiolchem.2009.07.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2008] [Revised: 06/22/2009] [Accepted: 07/01/2009] [Indexed: 11/21/2022]
|
28
|
Improved Prediction of Protein Binding Sites from Sequences Using Genetic Algorithm. Protein J 2009; 28:273-80. [DOI: 10.1007/s10930-009-9192-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
29
|
Ezkurdia I, Bartoli L, Fariselli P, Casadio R, Valencia A, Tress ML. Progress and challenges in predicting protein-protein interaction sites. Brief Bioinform 2009; 10:233-46. [PMID: 19346321 DOI: 10.1093/bib/bbp021] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.
Collapse
Affiliation(s)
- Iakes Ezkurdia
- Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | | | | | | | | |
Collapse
|
30
|
Identifying protein–protein interaction sites in transient complexes with temperature factor, sequence profile and accessible surface area. Amino Acids 2009; 38:263-70. [DOI: 10.1007/s00726-009-0245-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2008] [Accepted: 01/21/2009] [Indexed: 11/26/2022]
|
31
|
Liu B, Wang X, Lin L, Dong Q, Wang X. A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis. BMC Bioinformatics 2008; 9:510. [PMID: 19046430 PMCID: PMC2613933 DOI: 10.1186/1471-2105-9-510] [Citation(s) in RCA: 109] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2008] [Accepted: 12/01/2008] [Indexed: 11/23/2022] Open
Abstract
Background Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. Results In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. Conclusion The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.
Collapse
Affiliation(s)
- Bin Liu
- Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China.
| | | | | | | | | |
Collapse
|
32
|
Identification of protein interaction partners and protein-protein interaction sites. J Mol Biol 2008; 382:1276-89. [PMID: 18708070 DOI: 10.1016/j.jmb.2008.08.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2008] [Revised: 07/10/2008] [Accepted: 08/01/2008] [Indexed: 01/10/2023]
Abstract
Rigid-body docking has become quite successful in predicting the correct conformations of binary protein complexes, at least when the constituent proteins do not undergo large conformational changes upon binding. However, determining whether two given proteins interact is a more difficult problem. Successful docking procedures often give equally good scores for proteins that do not interact experimentally. This is the case for the multiple minimization approach we use here. An analysis of the results where all proteins within a set are docked with all other proteins (complete cross-docking) shows that the predictions can be greatly improved if the location of the correct binding interface on each protein is known, since the experimental complexes are much more likely to bring these two interfaces into contact, at the same time as yielding good interaction energy scores. While various methods exist for identifying binding interfaces, it is shown that simply studying the interaction of all potential protein pairs within a data set can itself help to identify the correct interfaces.
Collapse
|