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Lu P, Tian J. ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins. Comput Biol Chem 2024; 112:108115. [PMID: 38865861 DOI: 10.1016/j.compbiolchem.2024.108115] [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: 04/03/2024] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024]
Abstract
Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model's superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model's performance.
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Affiliation(s)
- Pengli Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
| | - Jialong Tian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
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2
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Nikam R, Jemimah S, Gromiha MM. DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation. Bioinformatics 2024; 40:btae309. [PMID: 38718170 PMCID: PMC11112046 DOI: 10.1093/bioinformatics/btae309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/08/2024] [Accepted: 05/08/2024] [Indexed: 05/24/2024] Open
Abstract
MOTIVATION Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes. RESULTS We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance. AVAILABILITY AND IMPLEMENTATION https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788 , Abu Dhabi, United Arab Emirates
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Computer Science, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
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Harini K, Sekijima M, Gromiha MM. PRA-Pred: Structure-based prediction of protein-RNA binding affinity. Int J Biol Macromol 2024; 259:129490. [PMID: 38224813 DOI: 10.1016/j.ijbiomac.2024.129490] [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: 12/10/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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Feng H, Wang F, Li N, Xu Q, Zheng G, Sun X, Hu M, Li X, Xing G, Zhang G. Use of tree-based machine learning methods to screen affinitive peptides based on docking data. Mol Inform 2023; 42:e202300143. [PMID: 37696773 DOI: 10.1002/minf.202300143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/13/2023]
Abstract
Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.
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Affiliation(s)
- Hua Feng
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Fangyu Wang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Ning Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, China
| | - Qian Xu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guanming Zheng
- Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Xuefeng Sun
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Man Hu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Xuewu Li
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guangxu Xing
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Gaiping Zhang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Longhu Modern Immunology Laboratory, Zhengzhou, China
- School of Advanced Agricultural sciences, Peking University, Beijing, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu, China
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5
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Nikam R, Yugandhar K, Gromiha MM. Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2023; 1871:140948. [PMID: 37567456 DOI: 10.1016/j.bbapap.2023.140948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/05/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computational Biology, Cornell University, New York, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, National University of Singapore, Singapore.
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6
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Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 2023; 213:10-17. [PMID: 36924867 PMCID: PMC10563387 DOI: 10.1016/j.ymeth.2023.03.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/17/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
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Delaunay M, Ha-Duong T. Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2405:205-230. [PMID: 35298816 DOI: 10.1007/978-1-0716-1855-4_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.
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Affiliation(s)
| | - Tâp Ha-Duong
- Université Paris-Saclay, CNRS, BioCIS, Châtenay-Malabry, France.
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8
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Meseguer A, Bota P, Fernández-Fuentes N, Oliva B. Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures. Methods Mol Biol 2022; 2385:335-351. [PMID: 34888728 DOI: 10.1007/978-1-0716-1767-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.
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Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Patricia Bota
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
| | - Narcis Fernández-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain.
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Druchok M, Yarish D, Garkot S, Nikolaienko T, Gurbych O. Ensembling machine learning models to boost molecular affinity prediction. Comput Biol Chem 2021; 93:107529. [PMID: 34192653 DOI: 10.1016/j.compbiolchem.2021.107529] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/01/2021] [Accepted: 06/08/2021] [Indexed: 02/01/2023]
Abstract
This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor - human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors. The methods include Support Vector Machine, Random Forest, CatBoost, feed-forward neural network, graph neural network, and Bidirectional Encoder Representations from Transformers. The first five methods use input features based on physico-chemical properties of molecules, while the last one is based on textual molecular representations. All approaches do not rely on atomic spatial coordinates, avoiding a potential bias from known structures, and are capable of generalizing for compounds with unknown conformations. Within each of the methods, we have trained two models that solve classification and regression tasks. Then, all models are grouped into a pipeline of two subsequent ensembles. The first ensemble aggregates six classification models which vote whether a ligand binds to a receptor or not. If a ligand is classified as active (i.e., binds), the second ensemble predicts its binding affinity in terms of the inhibition constant Ki.
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Affiliation(s)
- Maksym Druchok
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Institute for Condensed Matter Physics, NAS of Ukraine, 1 Svientsitskii Str., 79011 Lviv, Ukraine.
| | | | - Sofiya Garkot
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Ukrainian Catholic University, 17 Svientsitskii Str., 79011 Lviv, Ukraine
| | - Tymofii Nikolaienko
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Str., 01601 Kyiv, Ukraine
| | - Oleksandr Gurbych
- SoftServe, Inc., 2d Sadova Str., 79021 Lviv, Ukraine; Lviv Polytechnic National University, 5 Kniazia Romana Str., 79005 Lviv, Ukraine
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein–protein interactions. J Biosci 2019. [DOI: 10.1007/s12038-019-9909-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Lee ACL, Harris JL, Khanna KK, Hong JH. A Comprehensive Review on Current Advances in Peptide Drug Development and Design. Int J Mol Sci 2019; 20:ijms20102383. [PMID: 31091705 PMCID: PMC6566176 DOI: 10.3390/ijms20102383] [Citation(s) in RCA: 353] [Impact Index Per Article: 70.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 11/16/2022] Open
Abstract
Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
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Affiliation(s)
- Andy Chi-Lung Lee
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
| | | | - Kum Kum Khanna
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
| | - Ji-Hong Hong
- Radiation Biology Research Center, Institute for Radiological Research, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan 333, Taiwan.
- Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
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Abbasi WA, Asif A, Ben-Hur A, Minhas FUAA. Learning protein binding affinity using privileged information. BMC Bioinformatics 2018; 19:425. [PMID: 30442086 PMCID: PMC6238365 DOI: 10.1186/s12859-018-2448-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 10/25/2018] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. RESULTS In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well. CONCLUSIONS The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well.
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Affiliation(s)
- Wajid Arshad Abbasi
- Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan
- Information Technology Center (ITC), University of Azad Jammu & Kashmir, Muzaffarabad, Azad Kashmir, 13100, Pakistan
- Department of Computer Science, Colorado State University (CSU), Fort Collins, CO, 80523, USA
| | - Amina Asif
- Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University (CSU), Fort Collins, CO, 80523, USA.
| | - Fayyaz Ul Amir Afsar Minhas
- Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan.
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13
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Lin X, Zhang X. Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1470-1479. [PMID: 29994749 DOI: 10.1109/tcbb.2018.2793858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The hot regions in PPIs are some assembly regions which are composed of the tightly packed HotSpots. The discovery of hot regions helps to understand life activities and has very important value for biological applications. The identification of hot regions is the basis for protein design and cancer prevention. The existing algorithms of predicting hot regions often have some defects, such as low accuracy and unstability. This paper proposes a novel hot region prediction method based on diverse biological characteristics. First, feature evaluation is employed by using an impoved mRMR method. Then, SVM is adopted to create cassification model based on the features selected. In addition, a new clustering algorithm, namely LCSD (Local community structure detecting), is developed to detect and analyze the conformation of hot regions. In the clustering process, the link similarity of protein residues is introduced to handle the boundary nodes. This algorithm can effectively deal with the missing residue nodes and control the local community boundaries. The results indicate that the spatial structure of hot regions can be obtained more effectively, and that our method is more effective than previous methods for precise identification of hot regions.
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14
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Muthu Krishnan S. Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 2018; 445:62-74. [DOI: 10.1016/j.jtbi.2018.02.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/24/2018] [Accepted: 02/12/2018] [Indexed: 01/31/2023]
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15
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Ruan P, Hayashida M, Akutsu T, Vert JP. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel. BMC Bioinformatics 2018; 19:39. [PMID: 29504897 PMCID: PMC5836830 DOI: 10.1186/s12859-018-2017-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. Results In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. Conclusions We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.
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Affiliation(s)
- Peiying Ruan
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
| | - Morihiro Hayashida
- Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, 14-4, Nishiikumacho, Matsue, 690-8518, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 6110011, Japan
| | - Jean-Philippe Vert
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, 75006, France. .,Institut Curie, Paris, 75005, France. .,INSERM U900, Paris, 75005, France. .,Ecole Normale Supérieure, Department of Mathematics and Applications, Paris, 75005, France.
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Vyas R, Bapat S, Goel P, Karthikeyan M, Tambe SS, Kulkarni BD. Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:27-37. [PMID: 28113781 DOI: 10.1109/tcbb.2016.2621042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.
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17
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Anoosha P, Sakthivel R, Gromiha MM. Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer. Mutat Res 2017; 806:19-26. [PMID: 28938109 DOI: 10.1016/j.mrfmmm.2017.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/21/2017] [Accepted: 08/22/2017] [Indexed: 06/07/2023]
Abstract
Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.
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Affiliation(s)
- P Anoosha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.
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18
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Quantitative prediction of drug side effects based on drug-related features. Interdiscip Sci 2017; 9:434-444. [DOI: 10.1007/s12539-017-0236-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 04/29/2017] [Accepted: 05/03/2017] [Indexed: 01/07/2023]
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19
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Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:33-38. [PMID: 28069340 DOI: 10.1016/j.pbiomolbio.2017.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Revised: 01/04/2017] [Accepted: 01/05/2017] [Indexed: 01/09/2023]
Abstract
Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.
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20
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Important amino acid residues involved in folding and binding of protein–protein complexes. Int J Biol Macromol 2017; 94:438-444. [DOI: 10.1016/j.ijbiomac.2016.10.045] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 10/07/2016] [Accepted: 10/15/2016] [Indexed: 01/12/2023]
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21
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Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol 2017; 1484:237-253. [PMID: 27787830 DOI: 10.1007/978-1-4939-6406-2_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.
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22
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Gromiha MM, Yugandhar K, Jemimah S. Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 2016; 44:31-38. [PMID: 27866112 DOI: 10.1016/j.sbi.2016.10.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/25/2016] [Indexed: 01/16/2023]
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
| | - K Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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23
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Nagarajan R, Archana A, Thangakani AM, Jemimah S, Velmurugan D, Gromiha MM. PDBparam: Online Resource for Computing Structural Parameters of Proteins. Bioinform Biol Insights 2016; 10:73-80. [PMID: 27330281 PMCID: PMC4909059 DOI: 10.4137/bbi.s38423] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 04/20/2016] [Accepted: 04/24/2016] [Indexed: 02/07/2023] Open
Abstract
Understanding the structure-function relationship in proteins is a longstanding goal in molecular and computational biology. The development of structure-based parameters has helped to relate the structure with the function of a protein. Although several structural features have been reported in the literature, no single server can calculate a wide-ranging set of structure-based features from protein three-dimensional structures. In this work, we have developed a web-based tool, PDBparam, for computing more than 50 structure-based features for any given protein structure. These features are classified into four major categories: (i) interresidue interactions, which include short-, medium-, and long-range interactions, contact order, long-range order, total contact distance, contact number, and multiple contact index, (ii) secondary structure propensities such as α-helical propensity, β-sheet propensity, and propensity of amino acids to exist at various positions of α-helix and amino acid compositions in high B-value regions, (iii) physicochemical properties containing ionic interactions, hydrogen bond interactions, hydrophobic interactions, disulfide interactions, aromatic interactions, surrounding hydrophobicity, and buriedness, and (iv) identification of binding site residues in protein-protein, protein-nucleic acid, and protein-ligand complexes. The server can be freely accessed at http://www.iitm.ac.in/bioinfo/pdbparam/. We suggest the use of PDBparam as an effective tool for analyzing protein structures.
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Affiliation(s)
- R. Nagarajan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - A. Archana
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - A. Mary Thangakani
- CAS in Crystallography and Biophysics, University of Madras, Chennai, India
- Bioinformatics Infrastructure Facility, University of Madras, Chennai, India
| | - S. Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - D. Velmurugan
- CAS in Crystallography and Biophysics, University of Madras, Chennai, India
- Bioinformatics Infrastructure Facility, University of Madras, Chennai, India
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
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24
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Srinivasulu YS, Wang JR, Hsu KT, Tsai MJ, Charoenkwan P, Huang WL, Huang HL, Ho SY. Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes. BMC Bioinformatics 2015; 16 Suppl 18:S14. [PMID: 26681483 PMCID: PMC4682391 DOI: 10.1186/1471-2105-16-s18-s14] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.
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25
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Prediction of Protein–Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures. J Membr Biol 2015; 249:141-53. [DOI: 10.1007/s00232-015-9856-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 11/03/2015] [Indexed: 12/12/2022]
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26
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Anoosha P, Huang LT, Sakthivel R, Karunagaran D, Gromiha MM. Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer. Mutat Res 2015; 780:24-34. [PMID: 26264175 DOI: 10.1016/j.mrfmmm.2015.07.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 05/21/2015] [Accepted: 07/07/2015] [Indexed: 06/04/2023]
Abstract
Cancer is one of the most life-threatening diseases and mutations in several genes are the vital cause in tumorigenesis. Protein kinases play essential roles in cancer progression and specifically, epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this work, we have developed a method to classify single amino acid polymorphisms (SAPs) in EGFR into disease-causing (driver) and neutral (passenger) mutations using both sequence and structure based features of the mutation site by machine learning approaches. We compiled a set of 222 features and selected a set of 21 properties utilizing feature selection methods, for maximizing the prediction performance. In a set of 540 mutants, we obtained an overall classification accuracy of 67.8% with 10 fold cross validation using support vector machines. Further, the mutations have been grouped into four sets based on secondary structure and accessible surface area, which enhanced the overall classification accuracy to 80.2%, 81.9%, 77.9% and 75.1% for helix, strand, coil-buried and coil-exposed mutants, respectively. The method was tested with a blind dataset of 60 mutations, which showed an average accuracy of 85.4%. These accuracy levels are superior to other methods available in the literature for EGFR mutants, with an increase of more than 30%. Moreover, we have screened all possible single amino acid polymorphisms (SAPs) in EGFR and suggested the probable driver and passenger mutations, which would help in the development of mutation specific drugs for cancer treatment.
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Affiliation(s)
- P Anoosha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamil Nadu, India
| | - Liang-Tsung Huang
- Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamil Nadu, India
| | - D Karunagaran
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamil Nadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600 036, Tamil Nadu, India.
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Anoosha P, Sakthivel R, Gromiha MM. Prediction of protein disorder on amino acid substitutions. Anal Biochem 2015; 491:18-22. [PMID: 26348538 DOI: 10.1016/j.ab.2015.08.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 07/27/2015] [Accepted: 08/27/2015] [Indexed: 12/22/2022]
Abstract
Intrinsically disordered regions of proteins are known to have many functional roles in cell signaling and regulatory pathways. The altered expression of these proteins due to mutations is associated with various diseases. Currently, most of the available methods focus on predicting the disordered proteins or the disordered regions in a protein. On the other hand, methods developed for predicting protein disorder on mutation showed a poor performance with a maximum accuracy of 70%. Hence, in this work, we have developed a novel method to classify the disorder-related amino acid substitutions using amino acid properties, substitution matrices, and the effect of neighboring residues that showed an accuracy of 90.0% with a sensitivity and specificity of 94.9 and 80.6%, respectively, in 10-fold cross-validation. The method was evaluated with a test set of 20% data using 10 iterations, which showed an average accuracy of 88.9%. Furthermore, we systematically analyzed the features responsible for the better performance of our method and observed that neighboring residues play an important role in defining the disorder of a given residue in a protein sequence. We have developed a prediction server to identify disorder-related mutations, and it is available at http://www.iitm.ac.in/bioinfo/DIM_Pred/.
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Affiliation(s)
- P Anoosha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India.
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28
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Yugandhar K, Gromiha MM. Protein–protein binding affinity prediction from amino acid sequence. Bioinformatics 2014; 30:3583-9. [DOI: 10.1093/bioinformatics/btu580] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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