1
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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] [MESH Headings] [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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2
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Wang B, Li W. Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction. Genes (Basel) 2024; 15:1090. [PMID: 39202449 PMCID: PMC11353971 DOI: 10.3390/genes15081090] [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: 07/22/2024] [Revised: 08/13/2024] [Accepted: 08/14/2024] [Indexed: 09/03/2024] Open
Abstract
Protein and nucleic acid binding site prediction is a critical computational task that benefits a wide range of biological processes. Previous studies have shown that feature selection holds particular significance for this prediction task, making the generation of more discriminative features a key area of interest for many researchers. Recent progress has shown the power of protein language models in handling protein sequences, in leveraging the strengths of attention networks, and in successful applications to tasks such as protein structure prediction. This naturally raises the question of the applicability of protein language models in predicting protein and nucleic acid binding sites. Various approaches have explored this potential. This paper first describes the development of protein language models. Then, a systematic review of the latest methods for predicting protein and nucleic acid binding sites is conducted by covering benchmark sets, feature generation methods, performance comparisons, and feature ablation studies. These comparisons demonstrate the importance of protein language models for the prediction task. Finally, the paper discusses the challenges of protein and nucleic acid binding site prediction and proposes possible research directions and future trends. The purpose of this survey is to furnish researchers with actionable suggestions for comprehending the methodologies used in predicting protein-nucleic acid binding sites, fostering the creation of protein-centric language models, and tackling real-world obstacles encountered in this field.
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Affiliation(s)
| | - Wenjin Li
- Institute for Advanced Study, Shenzhen University, Shenzhen 518061, China;
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3
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Madan S, Lentzen M, Brandt J, Rueckert D, Hofmann-Apitius M, Fröhlich H. Transformer models in biomedicine. BMC Med Inform Decis Mak 2024; 24:214. [PMID: 39075407 PMCID: PMC11287876 DOI: 10.1186/s12911-024-02600-5] [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: 06/18/2023] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various kinds of sequential data, including biological sequences and structured electronic health records. Along with this development, transformer-based models such as BioBERT, MedBERT, and MassGenie have been trained and deployed by researchers to answer various scientific questions originating in the biomedical domain. In this paper, we review the development and application of transformer models for analyzing various biomedical-related datasets such as biomedical textual data, protein sequences, medical structured-longitudinal data, and biomedical images as well as graphs. Also, we look at explainable AI strategies that help to comprehend the predictions of transformer-based models. Finally, we discuss the limitations and challenges of current models, and point out emerging novel research directions.
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Affiliation(s)
- Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
- Institute of Computer Science, University of Bonn, Bonn, 53115, Germany.
| | - Manuel Lentzen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany
| | - Johannes Brandt
- School of Medicine, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany
| | - Daniel Rueckert
- School of Medicine, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany.
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4
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Yuan Q, Tian C, Song Y, Ou P, Zhu M, Zhao H, Yang Y. GPSFun: geometry-aware protein sequence function predictions with language models. Nucleic Acids Res 2024; 52:W248-W255. [PMID: 38738636 PMCID: PMC11223820 DOI: 10.1093/nar/gkae381] [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: 03/07/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/14/2024] Open
Abstract
Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.
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Affiliation(s)
- Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Chong Tian
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Yidong Song
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Peihua Ou
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Mingming Zhu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Huiying Zhao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
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5
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Yu Z, Yu J, Wang H, Zhang S, Zhao L, Shi S. PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures. Anal Biochem 2024; 690:115510. [PMID: 38513769 DOI: 10.1016/j.ab.2024.115510] [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: 12/25/2023] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024]
Abstract
Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.
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Affiliation(s)
- Ziyuan Yu
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Jialin Yu
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Hongmei Wang
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Shuai Zhang
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Long Zhao
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Shaoping Shi
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China; Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China.
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6
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Sun X, Yang S, Wu Z, Su J, Hu F, Chang F, Li C. PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network. Structure 2024; 32:838-848.e3. [PMID: 38508191 DOI: 10.1016/j.str.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/19/2023] [Accepted: 02/22/2024] [Indexed: 03/22/2024]
Abstract
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.
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Affiliation(s)
- Xiaohan Sun
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Shuang Yang
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Zhixiang Wu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Jingjie Su
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Fangrui Hu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Fubin Chang
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
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7
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Xia Y, Pan X, Shen HB. A comprehensive survey on protein-ligand binding site prediction. Curr Opin Struct Biol 2024; 86:102793. [PMID: 38447285 DOI: 10.1016/j.sbi.2024.102793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/18/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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8
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Rao J, Xie J, Yuan Q, Liu D, Wang Z, Lu Y, Zheng S, Yang Y. A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions. Nat Commun 2024; 15:4476. [PMID: 38796523 DOI: 10.1038/s41467-024-48801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/14/2024] [Indexed: 05/28/2024] Open
Abstract
Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.
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Affiliation(s)
- Jiahua Rao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jiancong Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Deqin Liu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zhen Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| | - Shuangjia Zheng
- Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Sun Yat-sen University, Guangzhou, China.
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China.
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9
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Zheng M, Sun G, Li X, Fan Y. EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion. Brief Bioinform 2024; 25:bbae330. [PMID: 38975896 PMCID: PMC11229037 DOI: 10.1093/bib/bbae330] [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: 03/20/2024] [Revised: 06/08/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024] Open
Abstract
Mechanisms of protein-DNA interactions are involved in a wide range of biological activities and processes. Accurately identifying binding sites between proteins and DNA is crucial for analyzing genetic material, exploring protein functions, and designing novel drugs. In recent years, several computational methods have been proposed as alternatives to time-consuming and expensive traditional experiments. However, accurately predicting protein-DNA binding sites still remains a challenge. Existing computational methods often rely on handcrafted features and a single-model architecture, leaving room for improvement. We propose a novel computational method, called EGPDI, based on multi-view graph embedding fusion. This approach involves the integration of Equivariant Graph Neural Networks (EGNN) and Graph Convolutional Networks II (GCNII), independently configured to profoundly mine the global and local node embedding representations. An advanced gated multi-head attention mechanism is subsequently employed to capture the attention weights of the dual embedding representations, thereby facilitating the integration of node features. Besides, extra node features from protein language models are introduced to provide more structural information. To our knowledge, this is the first time that multi-view graph embedding fusion has been applied to the task of protein-DNA binding site prediction. The results of five-fold cross-validation and independent testing demonstrate that EGPDI outperforms state-of-the-art methods. Further comparative experiments and case studies also verify the superiority and generalization ability of EGPDI.
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Affiliation(s)
- Mengxin Zheng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Xueping Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
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10
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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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11
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Roche R, Moussad B, Shuvo MH, Tarafder S, Bhattacharya D. EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. Nucleic Acids Res 2024; 52:e27. [PMID: 38281252 PMCID: PMC10954458 DOI: 10.1093/nar/gkae039] [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: 09/13/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here, we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
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12
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Qin Z, Wang R, Hou P, Zhang Y, Yuan Q, Wang Y, Yang Y, Xu T. TCR signaling induces STAT3 phosphorylation to promote TH17 cell differentiation. J Exp Med 2024; 221:e20230683. [PMID: 38324068 PMCID: PMC10849914 DOI: 10.1084/jem.20230683] [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: 04/21/2023] [Revised: 08/11/2023] [Accepted: 01/18/2024] [Indexed: 02/08/2024] Open
Abstract
TH17 differentiation is critically controlled by "signal 3" of cytokines (IL-6/IL-23) through STAT3. However, cytokines alone induced only a moderate level of STAT3 phosphorylation. Surprisingly, TCR stimulation alone induced STAT3 phosphorylation through Lck/Fyn, and synergistically with IL-6/IL-23 induced robust and optimal STAT3 phosphorylation at Y705. Inhibition of Lck/Fyn kinase activity by Srci1 or disrupting the interaction between Lck/Fyn and STAT3 by disease-causing STAT3 mutations selectively impaired TCR stimulation, but not cytokine-induced STAT3 phosphorylation, which consequently abolished TH17 differentiation and converted them to FOXP3+ Treg cells. Srci1 administration or disrupting the interaction between Lck/Fyn and STAT3 significantly ameliorated TH17 cell-mediated EAE disease. These findings uncover an unexpected deterministic role of TCR signaling in fate determination between TH17 and Treg cells through Lck/Fyn-dependent phosphorylation of STAT3, which can be exploited to develop therapeutics selectively against TH17-related autoimmune diseases. Our study thus provides insight into how TCR signaling could integrate with cytokine signal to direct T cell differentiation.
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Affiliation(s)
- Zhen Qin
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ruining Wang
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ping Hou
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yuanyuan Zhang
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Ying Wang
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Tao Xu
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
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13
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Sagendorf JM, Mitra R, Huang J, Chen XS, Rohs R. PNAbind: Structure-based prediction of protein-nucleic acid binding using graph neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582387. [PMID: 38529493 PMCID: PMC10962711 DOI: 10.1101/2024.02.27.582387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
The recognition and binding of nucleic acids (NAs) by proteins depends upon complementary chemical, electrostatic and geometric properties of the protein-NA binding interface. Structural models of protein-NA complexes provide insights into these properties but are scarce relative to models of unbound proteins. We present a deep learning approach for predicting protein-NA binding given the apo structure of a protein (PNAbind). Our method utilizes graph neural networks to encode spatial distributions of physicochemical and geometric properties of the protein molecular surface that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein and can discriminate between specificity for DNA or RNA binding. We show that such predictions made on protein structures modeled with AlphaFold2 can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Using local encodings, our models predict the location of NA binding sites at the level of individual binding residues. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and show that our predictions are consistent with experimental RNA binding data.
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14
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Kabir MWU, Alawad DM, Pokhrel P, Hoque MT. DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues. Comput Biol Med 2024; 170:108081. [PMID: 38295475 PMCID: PMC10922697 DOI: 10.1016/j.compbiomed.2024.108081] [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: 08/16/2023] [Revised: 01/12/2024] [Accepted: 01/27/2024] [Indexed: 02/02/2024]
Abstract
DNA-binding and RNA-binding proteins are essential to an organism's normal life cycle. These proteins have diverse functions in various biological processes. DNA-binding proteins are crucial for DNA replication, transcription, repair, packaging, and gene expression. Likewise, RNA-binding proteins are essential for the post-transcriptional control of RNAs and RNA metabolism. Identifying DNA- and RNA-binding residue is essential for biological research and understanding the pathogenesis of many diseases. However, most DNA-binding and RNA-binding proteins still need to be discovered. This research explored various properties of the protein sequences, such as amino acid composition type, Position-Specific Scoring Matrix (PSSM) values of amino acids, Hidden Markov model (HMM) profiles, physiochemical properties, structural properties, torsion angles, and disorder regions. We utilized a sliding window technique to extract more information from a target residue's neighbors. We proposed an optimized Light Gradient Boosting Machine (LightGBM) method, named DRBpred, to predict DNA-binding and RNA-binding residues from the protein sequence. DRBpred shows an improvement of 112.00 %, 33.33 %, and 6.49 % for the DNA-binding test set compared to the state-of-the-art method. It shows an improvement of 112.50 %, 16.67 %, and 7.46 % for the RNA-binding test set regarding Sensitivity, Mathews Correlation Coefficient (MCC), and AUC metric.
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Affiliation(s)
- Md Wasi Ul Kabir
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
| | - Duaa Mohammad Alawad
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
| | - Pujan Pokhrel
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
| | - Md Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
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15
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Palacios A, Acharya P, Peidl A, Beck M, Blanco E, Mishra A, Bawa-Khalfe T, Pakhrin S. SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model. NAR Genom Bioinform 2024; 6:lqae011. [PMID: 38327870 PMCID: PMC10849187 DOI: 10.1093/nargab/lqae011] [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: 06/21/2023] [Revised: 11/17/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.
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Affiliation(s)
- Andrew Vargas Palacios
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Pujan Acharya
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Anthony Stephen Peidl
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Moriah Rene Beck
- Department of Chemistry and Biochemistry, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA
| | - Eduardo Blanco
- Department of Computer Science, University of Arizona, 1040 4th St., Tucson, AZ 85721, USA
| | - Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
| | - Tasneem Bawa-Khalfe
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Subash Chandra Pakhrin
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
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16
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Li G, Bai P, Chen J, Liang C. Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures. Comput Biol Med 2024; 170:108062. [PMID: 38308869 DOI: 10.1016/j.compbiomed.2024.108062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/13/2024] [Accepted: 01/27/2024] [Indexed: 02/05/2024]
Abstract
With the increasing resistance of bacterial pathogens to conventional antibiotics, antivirulence strategies targeting virulence factors (VFs) have become an effective new therapy for the treatment of pathogenic bacterial infections. Therefore, the identification and prediction of VFs can provide ideal candidate targets for the implementation of antivirulence strategies in treating infections caused by pathogenic bacteria. Currently, the existing computational models predominantly rely on the amino acid sequences of virulence proteins while overlooking structural information. Here, we propose a novel graph transformer autoencoder for VF identification (GTAE-VF), which utilizes ESMFold-predicted 3D structures and converts the VF identification problem into a graph-level prediction task. In an encoder-decoder framework, GTAE-VF adaptively learns both local and global information by integrating a graph convolutional network and a transformer to implement all-pair message passing, which can better capture long-range correlations and potential relationships. Extensive experiments on an independent test dataset demonstrate that GTAE-VF achieves reliable and robust prediction accuracy with an AUC of 0.963, which is consistently better than that of other structure-based and sequence-based approaches. We believe that GTAE-VF has the potential to emerge as a valuable tool for assessing VFs and devising antivirulence strategies.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Peihao Bai
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Jiao Chen
- School of Laboratory Medicine, Nanchang Medical College, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
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17
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Zhu YH, Liu Z, Liu Y, Ji Z, Yu DJ. ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction. Brief Bioinform 2024; 25:bbae040. [PMID: 38349057 PMCID: PMC10939370 DOI: 10.1093/bib/bbae040] [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: 09/10/2023] [Revised: 01/02/2024] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
Efficient and accurate recognition of protein-DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein-DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone.
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Affiliation(s)
- Yi-Heng Zhu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
| | - Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yan Liu
- School of Information Engineering, Yangzhou University, Yangzhou 225000, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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18
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Shenoy A, Kalakoti Y, Sundar D, Elofsson A. M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings. Bioinformatics 2024; 40:btad782. [PMID: 38175787 PMCID: PMC10792727 DOI: 10.1093/bioinformatics/btad782] [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: 04/04/2023] [Revised: 12/20/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Understanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding. RESULTS On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. AVAILABILITY AND IMPLEMENTATION M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). The GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data.
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Affiliation(s)
- Aditi Shenoy
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna 17121, Sweden
| | - Yogesh Kalakoti
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
| | - Durai Sundar
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
- Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
| | - Arne Elofsson
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna 17121, Sweden
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19
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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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20
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Le NQK. Leveraging transformers-based language models in proteome bioinformatics. Proteomics 2023; 23:e2300011. [PMID: 37381841 DOI: 10.1002/pmic.202300011] [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: 05/14/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023]
Abstract
In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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21
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Liu Y, Tian B. Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning. Brief Bioinform 2023; 25:bbad488. [PMID: 38171929 PMCID: PMC10782905 DOI: 10.1093/bib/bbad488] [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: 06/30/2023] [Revised: 09/28/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Protein-DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.
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Affiliation(s)
- Yufan Liu
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Boxue Tian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
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22
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Zhang Y, Liu C, Liu M, Liu T, Lin H, Huang CB, Ning L. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25:bbad467. [PMID: 38189543 PMCID: PMC10772984 DOI: 10.1093/bib/bbad467] [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: 09/30/2023] [Revised: 11/03/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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Affiliation(s)
- Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Caiqi Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
- Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
| | - Mujiexin Liu
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tianyuan Liu
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Lin Ning
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
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23
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Zhou X, Chen G, Ye J, Wang E, Zhang J, Mao C, Li Z, Hao J, Huang X, Tang J, Heng PA. ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention. Nat Commun 2023; 14:7434. [PMID: 37973874 PMCID: PMC10654420 DOI: 10.1038/s41467-023-43166-6] [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: 03/30/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to rely on noisy predicted residues located in the local neighborhood when generating sequences. To address this limitation, we propose an entropy-based residue selection method to remove noise in the input residue context. Additionally, we introduce ProRefiner, a memory-efficient global graph attention model to fully utilize the denoised context. Our proposed method achieves state-of-the-art performance on multiple sequence design benchmarks in different design settings. Furthermore, we demonstrate the applicability of ProRefiner in redesigning Transposon-associated transposase B, where six out of the 20 variants we propose exhibit improved gene editing activity.
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Affiliation(s)
- Xinyi Zhou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Central Ave, Hong Kong, China
| | | | - Junjie Ye
- Noah's Ark Lab, Huawei, Shenzhen, China
| | - Ercheng Wang
- Zhejiang Lab, Kechuang Avenue, Hangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jun Zhang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Cong Mao
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Zhanwei Li
- Zhejiang Lab, Kechuang Avenue, Hangzhou, China
| | | | | | - Jin Tang
- Zhejiang Lab, Kechuang Avenue, Hangzhou, China
| | - Pheng Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Central Ave, Hong Kong, China
- Zhejiang Lab, Kechuang Avenue, Hangzhou, China
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24
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Song Y, Yuan Q, Zhao H, Yang Y. Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures. Brief Bioinform 2023; 24:bbad360. [PMID: 37824738 DOI: 10.1093/bib/bbad360] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacking known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breakthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA- and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The network was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common network to acquire common binding characteristics. Finally, these characteristics were input into two fully connected layers to predict binding sites with DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmark datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for inferring nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, and trained models are available at https://github.com/biomed-AI/nucleic-acid-binding.
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Affiliation(s)
- Yidong Song
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Qianmu Yuan
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Huiying Zhao
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
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25
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Roche R, Moussad B, Shuvo MH, Tarafder S, Bhattacharya D. EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557719. [PMID: 37745556 PMCID: PMC10515942 DOI: 10.1101/2023.09.14.557719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
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Jiang Z, Shen YY, Liu R. Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches. PLoS Comput Biol 2023; 19:e1011428. [PMID: 37672551 PMCID: PMC10482303 DOI: 10.1371/journal.pcbi.1011428] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023] Open
Abstract
Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template module was constructed by transforming the alignments between the query and its multiple templates into features for supervised learning. Furthermore, the stacking strategy was adopted to integrate the above two modules for improving prediction performance. Finally, a post-processing module dependent on the random walk algorithm was proposed to further correct the integrative predictions. Extensive evaluations indicated that our approach could not only achieve excellent performance on both native and predicted structures but also outperformed existing hybrid algorithms and recent deep learning methods. The NABind server is available at http://liulab.hzau.edu.cn/NABind/.
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Affiliation(s)
- Zheng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yue-Yue Shen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Rong Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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Roche R, Moussad B, Shuvo MH, Bhattacharya D. E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction. PLoS Comput Biol 2023; 19:e1011435. [PMID: 37651442 PMCID: PMC10499216 DOI: 10.1371/journal.pcbi.1011435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 09/13/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
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28
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Firouzi R, Sowlati-Hashjin S, Chávez-García C, Ashouri M, Karimi-Jafari MH, Karttunen M. Identification of Catechins' Binding Sites in Monomeric A β42 through Ensemble Docking and MD Simulations. Int J Mol Sci 2023; 24:ijms24098161. [PMID: 37175868 PMCID: PMC10179585 DOI: 10.3390/ijms24098161] [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: 03/14/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.
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Affiliation(s)
- Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
| | | | - Cecilia Chávez-García
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Mitra Ashouri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mohammad Hossein Karimi-Jafari
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mikko Karttunen
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
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Zhou H, Tan W, Shi S. DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism. Brief Bioinform 2023; 24:7000314. [PMID: 36694944 DOI: 10.1093/bib/bbad018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/26/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023] Open
Abstract
Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.
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Affiliation(s)
- Haiwei Zhou
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Wenxi Tan
- School of Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Shaoping Shi
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
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30
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Quan L, Chu X, Sun X, Wu T, Lyu Q. How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1594-1599. [PMID: 35471887 DOI: 10.1109/tcbb.2022.3170343] [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
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.
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31
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Scardino V, Di Filippo JI, Cavasotto CN. How good are AlphaFold models for docking-based virtual screening? iScience 2023; 26:105920. [PMID: 36686396 PMCID: PMC9852548 DOI: 10.1016/j.isci.2022.105920] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/12/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.
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Affiliation(s)
- Valeria Scardino
- Meton AI, Inc, Wilmington, DE 19801, USA
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Juan I. Di Filippo
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
| | - Claudio N. Cavasotto
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina
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32
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33
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Wang S, Chen W, Han P, Li X, Song T. RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction. J Chem Inf Model 2022; 62:5961-5974. [PMID: 36398714 DOI: 10.1021/acs.jcim.2c01092] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and lack accuracy. Hence, coming up with an effective computational method is necessary. In this article, we present a novel computational model called RGN (residue-based graph attention and convolutional network) to predict PPI sites. In our paper, the protein is treated as a graph. The amino acid can be seen as the node in the graph structure. The position-specific scoring matrix, hidden Markov model, hydrogen bond estimation algorithm, and ProtBert are applied as node features. The edges are decided by the spatial distance between the amino acids. Then, we utilize a residue-based graph convolutional network and graph attention network to further extract the deeper feature. Finally, the processed node feature is fed into the prediction layer. We show the superiority of our model by comparing it with the other four protein structure-based methods and five protein sequence-based methods. Our model obtains the best performance on all the evaluation metrics (accuracy, precision, recall, F1 score, Matthews correlation coefficient, area under the receiver operating characteristic curve, and area under the precision recall curve). We also conduct a case study to demonstrate that extracting the protein information from the protein structure perspective is effective and points out the difficult aspect of PPI site prediction.
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Affiliation(s)
- Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, QingDao266580, China
| | - Wenqi Chen
- College of Computer Science and Technology, China University of Petroleum, QingDao266580, China
| | - Peifu Han
- College of Computer Science and Technology, China University of Petroleum, QingDao266580, China
| | - Xue Li
- College of Computer Science and Technology, China University of Petroleum, QingDao266580, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum, QingDao266580, China.,Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Madrid28031, Spain
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34
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Yuan Q, Chen S, Wang Y, Zhao H, Yang Y. Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning. Brief Bioinform 2022; 23:6770088. [PMID: 36274238 DOI: 10.1093/bib/bbac444] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 09/02/2022] [Accepted: 09/17/2022] [Indexed: 12/14/2022] Open
Abstract
More than one-third of the proteins contain metal ions in the Protein Data Bank. Correct identification of metal ion-binding residues is important for understanding protein functions and designing novel drugs. Due to the small size and high versatility of metal ions, it remains challenging to computationally predict their binding sites from protein sequence. Existing sequence-based methods are of low accuracy due to the lack of structural information, and time-consuming owing to the usage of multi-sequence alignment. Here, we propose LMetalSite, an alignment-free sequence-based predictor for binding sites of the four most frequently seen metal ions in BioLiP (Zn2+, Ca2+, Mg2+ and Mn2+). LMetalSite leverages the pretrained language model to rapidly generate informative sequence representations and employs transformer to capture long-range dependencies. Multi-task learning is adopted to compensate for the scarcity of training data and capture the intrinsic similarities between different metal ions. LMetalSite was shown to surpass state-of-the-art structure-based methods by more than 19.7, 14.4, 36.8 and 12.6% in area under the precision recall on the four independent tests, respectively. Further analyses indicated that the self-attention modules are effective to learn the structural contexts of residues from protein sequence. We provide the data sets, source codes and trained models of LMetalSite at https://github.com/biomed-AI/LMetalSite.
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Affiliation(s)
- Qianmu Yuan
- School of Computer Science and Engineering at Sun Yat-sen University, Guangzhou 510000, China
| | - Sheng Chen
- School of Computer Science and Engineering at Sun Yat-sen University, Guangzhou 510000, China
| | - Yu Wang
- Peng Cheng National Laboratory at Shenzhen, Guangzhou 510000, China
| | - Huiying Zhao
- Sun Yat-sen Memorial Hospital at Sun Yat-sen University, Guangzhou 510000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China, and Key Laboratory of Machine Intelligence and Advanced Computing of MOE, Sun Yat-sen University, Guangzhou 510000, China
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35
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Shang S, Cai XS, Qi LS. Computation empowers CRISPR discovery and technology. NATURE COMPUTATIONAL SCIENCE 2022; 2:533-535. [PMID: 38177471 DOI: 10.1038/s43588-022-00321-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Stephen Shang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Xiangmeng S Cai
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Lei S Qi
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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36
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Patiyal S, Dhall A, Raghava GPS. A deep learning-based method for the prediction of DNA interacting residues in a protein. Brief Bioinform 2022; 23:6658239. [PMID: 35943134 DOI: 10.1093/bib/bbac322] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/01/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.
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Affiliation(s)
- Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
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37
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Shi W, Singha M, Pu L, Srivastava G, Ramanujam J, Brylinski M. GraphSite: Ligand Binding Site Classification with Deep Graph Learning. Biomolecules 2022; 12:1053. [PMID: 36008947 PMCID: PMC9405584 DOI: 10.3390/biom12081053] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/10/2022] Open
Abstract
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.
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Affiliation(s)
- Wentao Shi
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
| | - Jagannathan Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA; (W.S.); (J.R.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA; (M.S.); (G.S.)
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA;
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