1
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Shafiee S, Fathi A, Taherzadeh G. DP-site: A dual deep learning-based method for protein-peptide interaction site prediction. Methods 2024; 229:17-29. [PMID: 38871095 DOI: 10.1016/j.ymeth.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/22/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. METHOD To address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. RESULTS The proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. CONCLUSIONS The outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.
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Affiliation(s)
- Shima Shafiee
- Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
| | - Abdolhossein Fathi
- Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
| | - Ghazaleh Taherzadeh
- Department of Math, Physics, and Computer Science, Wilkes University, Pennsylvania, USA.
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2
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Hu J, Chen KX, Rao B, Ni JY, Thafar MA, Albaradei S, Arif M. Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism. Anal Biochem 2024; 694:115637. [PMID: 39121938 DOI: 10.1016/j.ab.2024.115637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/28/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024]
Abstract
Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.
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Affiliation(s)
- Jun Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China; Center for AI and Computational Biology, Suzhou Institution of Systems Medicine, Suzhou, 215123, China.
| | - Kai-Xin Chen
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Bing Rao
- School of Information & Electrical Engineering, Hangzhou City University, Hangzhou, 310015, China
| | - Jing-Yuan Ni
- NUIST Reading Academy, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Maha A Thafar
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
| | - Somayah Albaradei
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, 34110, Qatar.
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3
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Le VT, Zhan ZJ, Vu TTP, Malik MS, Ou YY. ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites. J Mol Graph Model 2024; 130:108777. [PMID: 38642500 DOI: 10.1016/j.jmgm.2024.108777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/28/2024] [Accepted: 04/16/2024] [Indexed: 04/22/2024]
Abstract
This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.
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Affiliation(s)
- Van-The Le
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan
| | - Zi-Jun Zhan
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan
| | - Thi-Thu-Phuong Vu
- Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan
| | - Muhammad-Shahid Malik
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Department of Computer Science and Engineering, Karakoram International University, Pakistan
| | - Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan.
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4
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Zhu C, Zhang C, Shang T, Zhang C, Zhai S, Cao L, Xu Z, Su Z, Song Y, Su A, Li C, Duan H. GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach. Brief Bioinform 2024; 25:bbae297. [PMID: 38990514 PMCID: PMC11238429 DOI: 10.1093/bib/bbae297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/28/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.
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Affiliation(s)
- Cheng Zhu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Chengyun Zhang
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Tianfeng Shang
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Chenhao Zhang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Silong Zhai
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Zhenyu Xu
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - An Su
- College of Chemical Engineering, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Chengxi Li
- College of Chemical and Biological Engineering, Zhejiang University, Yuhangtang Road, Xihu District, Hangzhou 310027, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
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5
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Yin S, Mi X, Shukla D. Leveraging machine learning models for peptide-protein interaction prediction. RSC Chem Biol 2024; 5:401-417. [PMID: 38725911 PMCID: PMC11078210 DOI: 10.1039/d3cb00208j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/07/2024] [Indexed: 05/12/2024] Open
Abstract
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.
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Affiliation(s)
- Song Yin
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign Urbana 61801 Illinois USA
| | - Xuenan Mi
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign Urbana IL 61801 USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign Urbana 61801 Illinois USA
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign Urbana IL 61801 USA
- Department of Bioengineering, University of Illinois Urbana-Champaign Urbana IL 61801 USA
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6
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Yuan Q, Tian C, Yang Y. Genome-scale annotation of protein binding sites via language model and geometric deep learning. eLife 2024; 13:RP93695. [PMID: 38630609 PMCID: PMC11023698 DOI: 10.7554/elife.93695] [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] [Indexed: 04/19/2024] Open
Abstract
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.
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Affiliation(s)
- Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
| | - Chong Tian
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
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7
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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [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: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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8
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Zhang J, Wang R, Wei L. MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins. J Chem Inf Model 2024; 64:1050-1065. [PMID: 38301174 DOI: 10.1021/acs.jcim.3c01471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.
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Affiliation(s)
- Jiashuo Zhang
- School of Software, Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
| | - Leyi Wei
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
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9
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Hosseini S, Golding GB, Ilie L. Seq-InSite: sequence supersedes structure for protein interaction site prediction. Bioinformatics 2024; 40:btad738. [PMID: 38212995 PMCID: PMC10796176 DOI: 10.1093/bioinformatics/btad738] [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: 05/16/2023] [Revised: 11/17/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. As experimental methods are expensive and time consuming, computational prediction of the interaction sites has been studied extensively. Structure-based programs are the most accurate, while the sequence-based ones are much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. Ideally, we would like a tool that has the quality of the former and the applicability of the latter. RESULTS We provide here the first solution that achieves these two goals. Our new sequence-based program, Seq-InSite, greatly surpasses the performance of sequence-based models, matching the quality of state-of-the-art structure-based predictors, thus effectively superseding the need for models requiring structure. The predictive power of Seq-InSite is illustrated using an analysis of evolutionary conservation for four protein sequences. AVAILABILITY AND IMPLEMENTATION Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/ and as free source code, including trained models and all datasets used for training and testing, at https://github.com/lucian-ilie/Seq-InSite.
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Affiliation(s)
- SeyedMohsen Hosseini
- Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada
| | - G Brian Golding
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Lucian Ilie
- Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada
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10
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Cong H, Liu H, Cao Y, Liang C, Chen Y. Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention. BMC Bioinformatics 2023; 24:456. [PMID: 38053020 DOI: 10.1186/s12859-023-05592-7] [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: 12/18/2022] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China.
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
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11
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Fang Y, Jiang Y, Wei L, Ma Q, Ren Z, Yuan Q, Wei DQ. DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model. Bioinformatics 2023; 39:btad718. [PMID: 38015872 PMCID: PMC10723037 DOI: 10.1093/bioinformatics/btad718] [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: 07/21/2023] [Revised: 11/04/2023] [Accepted: 11/27/2023] [Indexed: 11/30/2023] Open
Abstract
MOTIVATION Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. RESULTS In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. AVAILABILITY AND IMPLEMENTATION The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.
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Affiliation(s)
- Yitian Fang
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200040, China
- Peng Cheng Laboratory, Shenzhen 518055, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Leyi Wei
- School of Software, Shandong University, Jinan, Shandong 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200040, China
- Peng Cheng Laboratory, Shenzhen 518055, China
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12
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Chandra A, Sharma A, Dehzangi I, Tsunoda T, Sattar A. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features. Sci Rep 2023; 13:20882. [PMID: 38016996 PMCID: PMC10684570 DOI: 10.1038/s41598-023-47624-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023] Open
Abstract
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .
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Affiliation(s)
- Abel Chandra
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, USA
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
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13
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Nikam R, Yugandhar K, Gromiha MM. DeepBSRPred: deep learning-based binding site residue prediction for proteins. Amino Acids 2023; 55:1305-1316. [PMID: 36574037 DOI: 10.1007/s00726-022-03228-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2022]
Abstract
MOTIVATION Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes. RESULTS We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods. AVAILABILITY AND IMPLEMENTATION The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.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, Tamil Nadu, 600036, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
- Department of Computational Biology, Cornell University, New York, NY, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India.
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
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14
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Song H, Wang Q, Shao Z, Wang X, Cao H, Huang K, Sun Q, Sun Z, Guan X. In vitro gastrointestinal digestion of buckwheat ( Fagopyrum esculentum Moench) protein: release and structural characteristics of novel bioactive peptides stimulating gut cholecystokinin secretion. Food Funct 2023; 14:7469-7477. [PMID: 37489980 DOI: 10.1039/d3fo01951a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Satiety hormone cholecystokinin (CCK) plays a vital role in appetite inhibition. Its secretion is regulated by dietary components. The search for bioactive compounds that stimulate CCK secretion is currently an active area of research. The objective of this study was to evaluate the ability of buckwheat (Fagopyrum esculentum Moench) protein digest (BPD) to stimulate CCK secretion in vitro and in vivo and clarify the structural characteristics of peptides stimulating CCK secretion. BPD was prepared by an in vitro gastrointestinal digestion model. The relative molecular weight of BPD was <10 000 Da, and peptides with <3000 Da accounted for 70%. BPD was rich in essential amino acids Lys, Leu, and Val but lacked sulfur amino acids Met and Cys. It had a stimulatory effect on CCK secretion in vitro and in vivo. Chromatographic separation was performed to isolate peptide fractions involved in CCK secretion, and five novel CCK-releasing peptides including QFDLDD, PAFKEEHL, SFHFPI, IPPLFP, and RVTVQPDS were successfully identified. A sequence length range of 6-8 and marked hydrophobicity (18-28) were observed among the most CCK-releasing peptides. The present study demonstrated for the first time that BPD could stimulate CCK secretion and clarify the structural characteristics of bioactive peptides having CCK secretagogue activity in BPD.
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Affiliation(s)
- Hongdong Song
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- National Grain Industry (Urban Grain and Oil Security) Technology Innovation Center, Shanghai 200093, China
| | - Qingyu Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Zhuwei Shao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Xinyue Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Hongwei Cao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- National Grain Industry (Urban Grain and Oil Security) Technology Innovation Center, Shanghai 200093, China
| | - Kai Huang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- National Grain Industry (Urban Grain and Oil Security) Technology Innovation Center, Shanghai 200093, China
| | - Qiqi Sun
- Fengxian Central Hospital, Shanghai 201499, China.
| | | | - Xiao Guan
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- National Grain Industry (Urban Grain and Oil Security) Technology Innovation Center, Shanghai 200093, China
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15
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Shukla N, Srivastava N, Gupta R, Srivastava P, Narayan J. COVID Variants, Villain and Victory: A Bioinformatics Perspective. Microorganisms 2023; 11:2039. [PMID: 37630599 PMCID: PMC10459809 DOI: 10.3390/microorganisms11082039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023] Open
Abstract
The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.
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Affiliation(s)
- Nityendra Shukla
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Neha Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Rohit Gupta
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Prachi Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Jitendra Narayan
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
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16
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Hu J, Dong M, Tang YX, Zhang GJ. Improving protein-protein interaction site prediction using deep residual neural network. Anal Biochem 2023; 670:115132. [PMID: 36997014 DOI: 10.1016/j.ab.2023.115132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/12/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.
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17
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Hou Z, Yang Y, Ma Z, Wong KC, Li X. Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning. Commun Biol 2023; 6:73. [PMID: 36653447 PMCID: PMC9849350 DOI: 10.1038/s42003-023-04462-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/ .
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Affiliation(s)
- Zilong Hou
- grid.64924.3d0000 0004 1760 5735School of Artificial Intelligence, Jilin University, Jilin, China
| | - Yuning Yang
- grid.27446.330000 0004 1789 9163 Information Science and Technology, Northeast Normal University, Jilin, China
| | - Zhiqiang Ma
- grid.27446.330000 0004 1789 9163 Information Science and Technology, Northeast Normal University, Jilin, China
| | - Ka-chun Wong
- grid.35030.350000 0004 1792 6846Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China
| | - Xiangtao Li
- grid.64924.3d0000 0004 1760 5735School of Artificial Intelligence, Jilin University, Jilin, China
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18
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Li K, Quan L, Jiang Y, Li Y, Zhou Y, Wu T, Lyu Q. ctP 2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:297-306. [PMID: 35213314 DOI: 10.1109/tcbb.2022.3154413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.
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19
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Sun Z, Zheng S, Zhao H, Niu Z, Lu Y, Pan Y, Yang Y. To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3735-3743. [PMID: 34637380 DOI: 10.1109/tcbb.2021.3118916] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
MOTIVATION The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes. The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules. However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases. As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecule types. RESULTS In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite. By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods. To my best knowledge, this is the first method using multi-task framework to predict multiple molecular binding sites simultaneously.
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20
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Varghese DM, Nussinov R, Ahmad S. Predictive modeling of moonlighting DNA-binding proteins. NAR Genom Bioinform 2022; 4:lqac091. [DOI: 10.1093/nargab/lqac091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/25/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Abstract
Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY
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Affiliation(s)
- Dana Mary Varghese
- School of Computational and Integrative Sciences, Jawaharlal Nehru University , New Delhi- 110067 , India
| | - Ruth Nussinov
- Computational Structural Biology Section, Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research , Frederick , MD 21702 , USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University , Israel
| | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University , New Delhi- 110067 , India
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21
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Abdin O, Nim S, Wen H, Kim PM. PepNN: a deep attention model for the identification of peptide binding sites. Commun Biol 2022; 5:503. [PMID: 35618814 PMCID: PMC9135736 DOI: 10.1038/s42003-022-03445-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/03/2022] [Indexed: 11/09/2022] Open
Abstract
Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.
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Affiliation(s)
- Osama Abdin
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Han Wen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Philip M Kim
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada. .,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, M5S 3E1, Canada.
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22
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Wang R, Jin J, Zou Q, Nakai K, Wei L. Predicting protein-peptide binding residues via interpretable deep learning. Bioinformatics 2022; 38:3351-3360. [PMID: 35604077 DOI: 10.1093/bioinformatics/btac352] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/13/2022] [Accepted: 05/18/2022] [Indexed: 11/14/2022] Open
Abstract
Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, they highly rely on third-party tools or information for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers)-based Contrastive Learning framework to predict the protein-Peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of designed features. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structure and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Our results highlight the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Interestingly, we demonstrate that peptide-binding residues in local sequential regions have more specific sequential patterns as compared with other protein-ligand binding residues, which potentially provides functional difference. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/. AVAILABILITY https://github.com/Ruheng-W/PepBCL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ruheng Wang
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
| | - Kenta Nakai
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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23
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Sorkhi AG, Pirgazi J, Ghasemi V. A hybrid feature extraction scheme for efficient malonylation site prediction. Sci Rep 2022; 12:5756. [PMID: 35388017 PMCID: PMC8987080 DOI: 10.1038/s41598-022-08555-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction.
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Affiliation(s)
- Ali Ghanbari Sorkhi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
| | - Jamshid Pirgazi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
| | - Vahid Ghasemi
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
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24
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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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25
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Taherzadeh G, Campbell M, Zhou Y. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins. Methods Mol Biol 2022; 2499:177-186. [PMID: 35696081 DOI: 10.1007/978-1-0716-2317-6_9] [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: 06/15/2023]
Abstract
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. Hence, there is a demand to build fast and efficient computational methods to address this problem. Here, we present the SPRINT-Gly framework containing the largest dataset and a prediction model of glycosylation sites for a given protein sequence. In this framework, we construct a large dataset containing N- and O-linked glycosylation sites of human and mouse proteins, collected from different sources. We then introduce the SPRINT-Gly method to predict putative N- and O-linked sites. SPRINT-Gly is a machine learning-based approach consisting of a number of trained predictive models for glycosylation sites in both human and mouse proteins, separately. The method is built by incorporating sequence-based, predicted structural, and physicochemical information of the neighboring residues of each N- and O-linked glycosylation site and by training deep learning neural network and support vector machine as classifiers. SPRINT-Gly outperformed other existing methods by achieving 18% and 50% higher Matthew's correlation coefficient for N- and O-linked glycosylation site prediction, respectively. SPRINT-Gly is publicly available as an online and stand-alone predictor at https://sparks-lab.org/server/sprint-gly/ .
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Affiliation(s)
- Ghazaleh Taherzadeh
- Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA, USA.
| | - Matthew Campbell
- Institute for Glycomics, Griffith University, Southport, QLD, Australia
| | - Yaoqi Zhou
- Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
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26
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Zhang F, Zhao B, Shi W, Li M, Kurgan L. DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning. Brief Bioinform 2021; 23:6461158. [PMID: 34905768 DOI: 10.1093/bib/bbab521] [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: 08/02/2021] [Revised: 10/30/2021] [Accepted: 11/14/2021] [Indexed: 12/14/2022] Open
Abstract
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Wenbo Shi
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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Kozlovskii I, Popov P. Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks. J Chem Inf Model 2021; 61:3814-3823. [PMID: 34292750 DOI: 10.1021/acs.jcim.1c00475] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occupy a unique niche of interaction interfaces with respect to protein-protein and protein-small molecule complexes. Protein-peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein-peptide binding site detection method called BiteNetPp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein-small molecule complexes using a manually curated set of protein-peptide structures. BiteNetPp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNetPp suitable for the large-scale analysis of protein-peptide binding sites.
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Affiliation(s)
- Igor Kozlovskii
- iMolecule, Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Petr Popov
- iMolecule, Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
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Li Y, Golding GB, Ilie L. DELPHI: accurate deep ensemble model for protein interaction sites prediction. Bioinformatics 2021; 37:896-904. [PMID: 32840562 DOI: 10.1093/bioinformatics/btaa750] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/14/2020] [Accepted: 08/19/2020] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods. RESULTS We propose DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI), a new sequence-based deep learning suite for PPI-binding sites prediction. DELPHI has an ensemble structure which combines a CNN and a RNN component with fine tuning technique. Three novel features, HSP, position information and ProtVec are used in addition to nine existing ones. We comprehensively compare DELPHI to nine state-of-the-art programmes on five datasets, and DELPHI outperforms the competing methods in all metrics even though its training dataset shares the least similarities with the testing datasets. In the most important metrics, AUPRC and MCC, it surpasses the second best programmes by as much as 18.5% and 27.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model and, especially, the three new features. Using DELPHI it is shown that there is a strong correlation with protein-binding residues (PBRs) and sites with strong evolutionary conservation. In addition, DELPHI's predicted PBR sites closely match known data from Pfam. DELPHI is available as open-sourced standalone software and web server. AVAILABILITY AND IMPLEMENTATION The DELPHI web server can be found at delphi.csd.uwo.ca/, with all datasets and results in this study. The trained models, the DELPHI standalone source code, and the feature computation pipeline are freely available at github.com/lucian-ilie/DELPHI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiwei Li
- Department of Computer Science, The University of Western Ontario London, ON N6A 5B7, Canada
| | - G Brian Golding
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Lucian Ilie
- Department of Computer Science, The University of Western Ontario London, ON N6A 5B7, Canada
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Haque HMF, Rafsanjani M, Arifin F, Adilina S, Shatabda S. SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences. Comput Biol Chem 2021; 92:107489. [PMID: 33932779 DOI: 10.1016/j.compbiolchem.2021.107489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 03/07/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
The information of a cell is primarily contained in deoxyribonucleic acid (DNA). There is a flow of DNA information to protein sequences via ribonucleic acids (RNA) through transcription and translation. These entities are vital for the genetic process. Recent epigenetics developments also show the importance of the genetic material and knowledge of their attributes and functions. However, the growth in these entities' available features or functionalities is still slow due to the time-consuming and expensive in vitro experimental methods. In this paper, we have proposed an ensemble classification algorithm called SubFeat to predict biological entities' functionalities from different types of datasets. Our model uses a feature subspace-based novel ensemble method. It divides the feature space into sub-spaces, which are then passed to learn individual classifier models. The ensemble is built on these base classifiers that use a weighted majority voting mechanism. SubFeat tested on four datasets comprising two DNA, one RNA, and one protein dataset, and it outperformed all the existing single classifiers and the ensemble classifiers. SubFeat is made available as a Python-based tool. We have made the package SubFeat available online along with a user manual. It is freely accessible from here: https://github.com/fazlulhaquejony/SubFeat.
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Affiliation(s)
- H M Fazlul Haque
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Muhammod Rafsanjani
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Fariha Arifin
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Sheikh Adilina
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh.
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Zhang J, Ghadermarzi S, Kurgan L. Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins. Bioinformatics 2021; 36:4729-4738. [PMID: 32860044 DOI: 10.1093/bioinformatics/btaa573] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/22/2020] [Accepted: 06/10/2020] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions). RESULTS Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs. AVAILABILITY AND IMPLEMENTATION HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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Zhang F, Shi W, Zhang J, Zeng M, Li M, Kurgan L. PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection. Bioinformatics 2020; 36:i735-i744. [DOI: 10.1093/bioinformatics/btaa806] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
Abstract
Motivation
Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods.
Results
We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein.
Availability and implementation
PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Wenbo Shi
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Min Zeng
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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Refahi MS, Mir A, Nasiri JA. A novel fusion based on the evolutionary features for protein fold recognition using support vector machines. Sci Rep 2020; 10:14368. [PMID: 32873824 PMCID: PMC7463267 DOI: 10.1038/s41598-020-71172-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 08/10/2020] [Indexed: 11/29/2022] Open
Abstract
Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance and Separated dimer evolutionary feature extraction methods. The results’ features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and EDD, the results of the support vector machine show more than 6\documentclass[12pt]{minimal}
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Affiliation(s)
- Mohammad Saleh Refahi
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - A Mir
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
| | - Jalal A Nasiri
- Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran.
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33
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Wardah W, Dehzangi A, Taherzadeh G, Rashid MA, Khan M, Tsunoda T, Sharma A. Predicting protein-peptide binding sites with a deep convolutional neural network. J Theor Biol 2020; 496:110278. [DOI: 10.1016/j.jtbi.2020.110278] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 04/05/2020] [Accepted: 04/08/2020] [Indexed: 10/24/2022]
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Zhang J, Kurgan L. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences. Bioinformatics 2020; 35:i343-i353. [PMID: 31510679 PMCID: PMC6612887 DOI: 10.1093/bioinformatics/btz324] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Motivation Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use. Results We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins. Availability and implementation SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, China.,Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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35
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Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning. J Comput Biol 2020; 27:796-814. [DOI: 10.1089/cmb.2019.0193] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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36
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AHMAD WAKIL, ARAFAT EASIN, TAHERZADEH GHAZALEH, SHARMA ALOK, DIPTA SHUBHASHISROY, DEHZANGI ABDOLLAH, SHATABDA SWAKKHAR. Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:77888-77902. [PMID: 33354488 PMCID: PMC7751949 DOI: 10.1109/access.2020.2989713] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
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Affiliation(s)
- WAKIL AHMAD
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - EASIN ARAFAT
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - GHAZALEH TAHERZADEH
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - ALOK SHARMA
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
- CREST, JST, Tokyo, 102-8666, Japan
| | - SHUBHASHIS ROY DIPTA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - ABDOLLAH DEHZANGI
- Department of Computer Science, Morgan State University, Baltimore, MD, 21251, USA
| | - SWAKKHAR SHATABDA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
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AIBH: Accurate Identification of Brain Hemorrhage Using Genetic Algorithm Based Feature Selection and Stacking. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2020. [DOI: 10.3390/make2020005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain hemorrhage is a type of stroke which is caused by a ruptured artery, resulting in localized bleeding in or around the brain tissues. Among a variety of imaging tests, a computerized tomography (CT) scan of the brain enables the accurate detection and diagnosis of a brain hemorrhage. In this work, we developed a practical approach to detect the existence and type of brain hemorrhage in a CT scan image of the brain, called Accurate Identification of Brain Hemorrhage, abbreviated as AIBH. The steps of the proposed method consist of image preprocessing, image segmentation, feature extraction, feature selection, and design of an advanced classification framework. The image preprocessing and segmentation steps involve removing the skull region from the image and finding out the region of interest (ROI) using Otsu’s method, respectively. Subsequently, feature extraction includes the collection of a comprehensive set of features from the ROI, such as the size of the ROI, centroid of the ROI, perimeter of the ROI, the distance between the ROI and the skull, and more. Furthermore, a genetic algorithm (GA)-based feature selection algorithm is utilized to select relevant features for improved performance. These features are then used to train the stacking-based machine learning framework to predict different types of a brain hemorrhage. Finally, the evaluation results indicate that the proposed predictor achieves a 10-fold cross-validation (CV) accuracy (ACC), precision (PR), Recall, F1-score, and Matthews correlation coefficient (MCC) of 99.5%, 99%, 98.9%, 0.989, and 0.986, respectively, on the benchmark CT scan dataset. While comparing AIBH with the existing state-of-the-art classification method of the brain hemorrhage type, AIBH provides an improvement of 7.03%, 7.27%, and 7.38% based on PR, Recall, and F1-score, respectively. Therefore, the proposed approach considerably outperforms the existing brain hemorrhage classification approach and can be useful for the effective prediction of brain hemorrhage types from CT scan images (The code and data can be found here: http://cs.uno.edu/~tamjid/Software/AIBH/code_data.zip).
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Gattani S, Mishra A, Hoque MT. StackCBPred: A stacking based prediction of protein-carbohydrate binding sites from sequence. Carbohydr Res 2019; 486:107857. [DOI: 10.1016/j.carres.2019.107857] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/05/2019] [Accepted: 10/23/2019] [Indexed: 11/26/2022]
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39
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Blaszczyk M, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S. Protein-peptide docking using CABS-dock and contact information. Brief Bioinform 2019; 20:2299-2305. [PMID: 30247502 PMCID: PMC6954405 DOI: 10.1093/bib/bby080] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 12/11/2022] Open
Abstract
CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/). SHORT ABSTRACT CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.
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40
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Meng Q, Peng Z, Yang J. CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction. Bioinformatics 2019; 34:2598-2604. [PMID: 29547921 DOI: 10.1093/bioinformatics/bty162] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 03/13/2018] [Indexed: 12/20/2022] Open
Abstract
Motivation Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction. Results We developed three methods for the prediction of CoA- and CoA derivatives-binding residues, including an ab initio method SVMpred, a template-based method TemPred and a consensus-based method CoABind. In SVMpred, a comprehensive set of features are designed from two complementary sequence profiles and the predicted secondary structure and solvent accessibility. The engine for classification in SVMpred is selected as the support vector machine. For TemPred, the prediction is transferred from homologous templates in the training set, which are detected by the program HHsearch. The assessment on an independent test set consisting of 73 proteins shows that SVMpred and TemPred achieve Matthews correlation coefficient (MCC) of 0.438 and 0.481, respectively. Analysis on the predictions by SVMpred and TemPred shows that these two methods are complementary to each other. Therefore, we combined them together, forming the third method CoABind, which further improves the MCC to 0.489 on the same set. Experiments demonstrate that the proposed methods significantly outperform the state-of-the-art general-purpose ligand-binding residues prediction algorithm COACH. As the first-of-its-kind method, we anticipate CoABind to be helpful for studying CoA-protein interaction. Availability and implementation http://yanglab.nankai.edu.cn/CoABind. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiaozhen Meng
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, China
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Chandra AA, Sharma A, Dehzangi A, Tsunoda T. EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction. BMC Genomics 2019; 19:984. [PMID: 30999859 PMCID: PMC7402405 DOI: 10.1186/s12864-018-5383-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 12/17/2018] [Indexed: 01/21/2023] Open
Abstract
Background Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid’s local structure conformations and profile bigrams of position-specific scoring matrices. Conclusion EvolStruct-Phogly showed a noteworthy improvement in regards to the performance when compared with the previous predictors. The performance metrics obtained are as follows: sensitivity 0.7744, specificity 0.8533, precision 0.7368, accuracy 0.8275, and Mathews correlation coefficient of 0.6242. The software package and data of this work can be obtained from https://github.com/abelavit/EvolStruct-Phogly or www.alok-ai-lab.com
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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Sharma A, Lysenko A, López Y, Dehzangi A, Sharma R, Reddy H, Sattar A, Tsunoda T. HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues. BMC Genomics 2019; 19:982. [PMID: 30999862 PMCID: PMC7402407 DOI: 10.1186/s12864-018-5206-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 10/28/2018] [Indexed: 02/06/2023] Open
Abstract
Background Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratory-based techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew’s correlation coefficient (0.78–0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method. The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO. Electronic supplementary material The online version of this article (10.1186/s12864-018-5206-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Q, Brisbane, LD-4111, Australia. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands.
| | - Artem Lysenko
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Yosvany López
- Genesis Institute of Genetic Research, Genesis Healthcare Co, Tokyo, Japan
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Ronesh Sharma
- School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands.,School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji
| | - Hamendra Reddy
- School of Engineering and Physics, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji Islands
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Q, Brisbane, LD-4111, Australia
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. .,CREST, JST, Tokyo, 113-8510, Japan.
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Reddy HM, Sharma A, Dehzangi A, Shigemizu D, Chandra AA, Tsunoda T. GlyStruct: glycation prediction using structural properties of amino acid residues. BMC Bioinformatics 2019; 19:547. [PMID: 30717650 PMCID: PMC7394324 DOI: 10.1186/s12859-018-2547-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/29/2018] [Indexed: 02/06/2023] Open
Abstract
Background Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew’s correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation. Conclusion Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods. Electronic supplementary material The online version of this article (10.1186/s12859-018-2547-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Daichi Shigemizu
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Division of Genomic Medicine, Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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Litfin T, Yang Y, Zhou Y. SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites. J Chem Inf Model 2019; 59:924-930. [DOI: 10.1021/acs.jcim.8b00777] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Thomas Litfin
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| | - Yuedong Yang
- School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong 510006, China
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
- Institute for Glycomics, Griffith University, Southport, QLD 4222, Australia
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Viswanathan R, Fajardo E, Steinberg G, Haller M, Fiser A. Protein-protein binding supersites. PLoS Comput Biol 2019; 15:e1006704. [PMID: 30615604 PMCID: PMC6336348 DOI: 10.1371/journal.pcbi.1006704] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 01/17/2019] [Accepted: 12/05/2018] [Indexed: 11/19/2022] Open
Abstract
The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.
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Affiliation(s)
- Raji Viswanathan
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Eduardo Fajardo
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine, Bronx, NY, United States of America
| | - Gabriel Steinberg
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Matthew Haller
- Department of Chemistry, Yeshiva University, New York, NY, United States of America
| | - Andras Fiser
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine, Bronx, NY, United States of America
- * E-mail:
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Singh V, Sharma A, Chandra A, Dehzangi A, Shigemizu D, Tsunoda T. Computational Prediction of Lysine Pupylation Sites in Prokaryotic Proteins Using Position Specific Scoring Matrix into Bigram for Feature Extraction. PRICAI 2019: TRENDS IN ARTIFICIAL INTELLIGENCE 2019. [DOI: 10.1007/978-3-030-29894-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Gil N, Fiser A. The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis. Bioinformatics 2019; 35:12-19. [PMID: 29947739 PMCID: PMC6298051 DOI: 10.1093/bioinformatics/bty523] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/20/2018] [Accepted: 06/26/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation The analysis of sequence conservation patterns has been widely utilized to identify functionally important (catalytic and ligand-binding) protein residues for over a half-century. Despite decades of development, on average state-of-the-art non-template-based functional residue prediction methods must predict ∼25% of a protein's total residues to correctly identify half of the protein's functional site residues. The overwhelming proportion of false positives results in reported 'F-Scores' of ∼0.3. We investigated the limits of current approaches, focusing on the so-far neglected impact of the specific choice of homologs included in multiple sequence alignments (MSAs). Results The limits of conservation-based functional residue prediction were explored by surveying the binding sites of 1023 proteins. A straightforward conservation analysis of MSAs composed of randomly selected homologs sampled from a PSI-BLAST search achieves average F-Scores of ∼0.3, a performance matching that reported by state-of-the-art methods, which often consider additional features for the prediction in a machine learning setting. Interestingly, we found that a simple combinatorial MSA sampling algorithm will in almost every case produce an MSA with an optimal set of homologs whose conservation analysis reaches average F-Scores of ∼0.6, doubling state-of-the-art performance. We also show that this is nearly at the theoretical limit of possible performance given the agreement between different binding site definitions. Additionally, we showcase the progress in this direction made by Selection of Alignment by Maximal Mutual Information (SAMMI), an information-theory-based approach to identifying biologically informative MSAs. This work highlights the importance and the unused potential of optimally composed MSAs for conservation analysis. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nelson Gil
- Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Andras Fiser
- Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
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Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci Rep 2018; 8:17923. [PMID: 30560923 PMCID: PMC6299098 DOI: 10.1038/s41598-018-36203-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022] Open
Abstract
The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct .
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Affiliation(s)
- Abel Chandra
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan.
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
- CREST, JST, Tokyo, 113-8510, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, Maryland, USA
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Anjeela Jokhan
- Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA, 02478, USA
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- CREST, JST, Tokyo, 113-8510, Japan
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Dehzangi A, López Y, Taherzadeh G, Sharma A, Tsunoda T. SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure. Molecules 2018; 23:E3260. [PMID: 30544729 PMCID: PMC6320791 DOI: 10.3390/molecules23123260] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 11/30/2018] [Accepted: 12/05/2018] [Indexed: 12/13/2022] Open
Abstract
Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.
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Affiliation(s)
- Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA.
| | - Yosvany López
- Genesis Institute of Genetic Research, Genesis Healthcare Co., Tokyo 150-6015, Japan.
| | - Ghazaleh Taherzadeh
- School of Information and Communication Technology, Griffith University, Gold Coast 4222, Australia.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane 4111, Australia.
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
- CREST, JST, Tokyo 102-0076, Japan.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
- CREST, JST, Tokyo 102-0076, Japan.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
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PPInS: a repository of protein-protein interaction sitesbase. Sci Rep 2018; 8:12453. [PMID: 30127348 PMCID: PMC6102274 DOI: 10.1038/s41598-018-30999-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 08/03/2018] [Indexed: 01/14/2023] Open
Abstract
Protein-Protein Interaction Sitesbase (PPInS), a high-performance database of protein-protein interacting interfaces, is presented. The atomic level information of the molecular interaction happening amongst various protein chains in protein-protein complexes (as reported in the Protein Data Bank [PDB]) together with their evolutionary information in Structural Classification of Proteins (SCOPe release 2.06), is made available in PPInS. Total 32468 PDB files representing X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å had been shortlisted to demarcate the protein-protein interaction interfaces (PPIIs). A total of 111857 PPIIs with ~32.24 million atomic contact pairs (ACPs) were generated and made available on a web server for on-site analysis and downloading purpose. All these PPIIs and protein-protein interacting patches (PPIPs) involved in them, were also analyzed in terms of a number of residues contributing in patch formation, their hydrophobic nature, amount of surface area they contributed in binding, and their homo and heterodimeric nature, to describe the diversity of information covered in PPInS. It was observed that 42.37% of total PPIPs were made up of 6–20 interacting residues, 53.08% PPIPs had interface area ≤1000 Å2 in PPII formation, 82.64% PPIPs were reported with hydrophobicity score of ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75–100%. A subset “Non-Redundant Database (NRDB)” of the PPInS containing 2265 PPIIs, with over 1.8 million ACPs corresponding to the 1931 protein-protein complexes (PDBs), was also designed by removing structural redundancies at the level of SCOP superfamily (SCOP release 1.75). The web interface of the PPInS (http://www.cup.edu.in:99/ppins/home.php) offers an easy-to-navigate, intuitive and user-friendly environment, and can be accessed by providing PDB ID, SCOP superfamily ID, and protein sequence.
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