1
|
Xiao J, Hu G, Zhou X, Zheng Y, Li J. TIDGN: A Transfer Learning Framework for Predicting Interactions of Intrinsically Disordered Proteins with High Conformational Dynamics. J Chem Inf Model 2025; 65:4866-4877. [PMID: 40360271 DOI: 10.1021/acs.jcim.5c00422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2025]
Abstract
Interactions between intrinsically disordered proteins (IDPs) are crucial for biological processes, such as intracellular liquid-liquid phase separation (LLPS). Experiments (e.g., NMR) and simulations used to study IDP interactions encounter a variety of difficulties, highlighting the necessity to develop relevant machine learning methods. However, reliable machine learning methods face the challenge resulting from the scarcity of available training data. In this work, we propose a transfer learning-based invariant geometric dynamic graph model, named TIDGN, for predicting IDP interactions. The model consists of a pretraining task module and a downstream task module. The pretraining task module learns the dynamic structural encoding of IDP monomers, which is then used by the downstream task module for interaction site prediction. The IDP monomer structure data set and the IDP interaction event data set are constructed using all-atom molecular dynamics (MD) simulations. The transfer learning strategy effectively enhances the model's performance. Both homotypic interactions and heterotypic interactions between two IDPs are considered in this work. Interestingly, TIDGN performs well for the heterotypic interaction prediction. Additionally, the feature ablation analysis emphasizes the importance of invariant geometric graph features. Taken together, our work demonstrates that the integration of transfer learning and the invariant geometric graph network offers a promising approach for addressing data scarcity challenges of IDP interaction prediction.
Collapse
Affiliation(s)
- Jing Xiao
- School of Physics, Zhejiang University, Hangzhou 310058, P. R. China
| | - Guorong Hu
- School of Physics, Zhejiang University, Hangzhou 310058, P. R. China
| | - Xiaozhou Zhou
- School of Physics, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yuchuan Zheng
- School of Physics, Zhejiang University, Hangzhou 310058, P. R. China
| | - Jingyuan Li
- School of Physics, Zhejiang University, Hangzhou 310058, P. R. China
| |
Collapse
|
2
|
Lai L, Geng J, Duan H, Chen S, Huang L, Yu J. A New Structure Feature Introduced to Predict Protein-Protein Interaction Sites. J Comput Biol 2025; 32:520-536. [PMID: 40000026 DOI: 10.1089/cmb.2024.0804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2025] Open
Abstract
Interaction between proteins often depends on the sequence features and structure features of proteins. Both of these features are helpful for machine learning methods to predict (protein-protein interaction) PPI sites. In this study, we introduced a new structure feature: concave-convex feature on the protein surface, which was computed by the structural data of proteins in Protein Data Bank database. And then, a prediction model combining protein sequence features and structure features was constructed, named SSPPI_Ensemble (Sequence and Structure geometric feature-based PPI site prediction). Three sequence features, i.e., PSSMs (Position-Specific Scoring Matrices), HMM (Hidden Markov Models) and raw protein sequence, were used. The Dictionary of Secondary Structure in Proteins and the concave-convex feature were used as the structure feature. Compared with the other prediction methods, our method has achieved better performance or showed the obvious advantages on the same test datasets, confirming the proposed concave-convex feature is useful in predicting PPI sites.
Collapse
Affiliation(s)
- Lingwei Lai
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Jing Geng
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Haochen Duan
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Siyuan Chen
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Lvwen Huang
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Jiantao Yu
- College of Information Engineering, Northwest A&F University, Yangling, China
| |
Collapse
|
3
|
Rodrigues CF, de Sousa BL, da Silva JHM, Amorim LAA, Nagano CS, de Oliveira Sousa AR, Carneiro RF, de Oliveira JS, de Freitas CDT, da Silva ALC, Rocha BAM. A novel galectin with triple carbohydrate recognition domain in the parotoid secretion of Rhinella diptycha. Int J Biol Macromol 2025; 306:141586. [PMID: 40023411 DOI: 10.1016/j.ijbiomac.2025.141586] [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: 11/10/2024] [Revised: 02/05/2025] [Accepted: 02/26/2025] [Indexed: 03/04/2025]
Abstract
Galectins are a family of animal lectins involved in cell adhesion, tumor differentiation, and apoptosis that can bind reversibly to carbohydrates with a high affinity for β-galactosides. Thus far, however, the primary structure and solved three-dimensional structure have been described for only a few amphibian galectins. Therefore, this work aimed to identify and structurally characterize the galectin (RdG) present in the secretion of the parotid gland of R. diptycha. RdG was partially purified and identified through hemagglutinating activity. The partial primary structure was obtained using peptide sequencing obtained from proteolysis with different enzymes, resulting in a sequence comprising 393 amino acids (86,4 % of coverage). In addition, based on alignments with homologous proteins, the complete sequence was predicted to consist of 455 residues with a molecular mass of 51 kDa and a triple carbohydrate recognition domain (CRD). The three-dimensional structure was then predicted, and protein-carbohydrate interaction was analyzed by molecular docking. The signature sequence of a highly conserved domain was identified in RdG with residues differing somewhat from those of other galectins. Thus, with the structural data for RdG, we were well positioned to better understand the interactions between ligands and amino acid residues of this novel triple CRD galectin. Given the therapeutic potential of galectins in general, structural studies like this one are crucial for understanding the mechanisms of action of galectins like RdG.
Collapse
Affiliation(s)
| | - Bruno Lopes de Sousa
- Dom Aureliano Matos Faculty of Philosophy, Universidade Estadual do Ceará, Limoeiro do Norte, Brazil
| | | | | | - Celso Shiniti Nagano
- Department of Fisheries Engineering, Universidade Federal do Ceará, Fortaleza, Brazil
| | | | | | | | | | | | | |
Collapse
|
4
|
Shao D, Zou Y, Ma L, Yi S. Multiscale and global-local U-Net for protein-protein interaction site prediction. Comput Biol Chem 2025; 118:108485. [PMID: 40306099 DOI: 10.1016/j.compbiolchem.2025.108485] [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: 12/13/2024] [Revised: 03/18/2025] [Accepted: 04/21/2025] [Indexed: 05/02/2025]
Abstract
Precise prediction of protein-protein interaction sites (PPIS) is fundamental to deciphering cellular mechanisms and accelerating therapeutic discovery. Despite significant advancements in computational approaches, current methods frequently fail to integrate multiscale features that simultaneously capture global context and local interactions. We present Multiscale and Global-Local U-Net for Protein-Protein Interaction Site Prediction (MGU-PPIS), a novel architecture designed to address this critical limitation. Our model leverages a U-Net framework with implemented multi-level pooling to extract comprehensive multiscale features. Within each scale, we synergistically combine Transformer networks, Graph Convolutional Networks (GCNs), and Graph Attention Networks (GATs) to simultaneously capture global patterns and local structural motifs. We implement Laplacian positional encoding to effectively represent global protein structural characteristics. In our framework, proteins are conceptualized as graph structures where individual residues function as nodes and their spatial relationships define edges. The model processes information through an innovative two-stage U-Net architecture, where output features from the initial stage serve as refined inputs for the subsequent stage. This dual-stage design, coupled with our graph-based representation, enables MGU-PPIS to extract a rich spectrum of multiscale features encompassing both global context and local interactions at each scale. Comprehensive experimental validation demonstrates that MGU-PPIS significantly outperforms state-of-the-art methods in predictive accuracy. Beyond introducing a novel computational strategy for PPIS prediction, our work establishes a foundation for advances in protein functional analysis and structure-based drug design.
Collapse
Affiliation(s)
- Dangguo Shao
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Yuyang Zou
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Lei Ma
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.
| | - Sanli Yi
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.
| |
Collapse
|
5
|
Meng L, Wei L, Wu R. MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning. Int J Biol Macromol 2025; 300:140096. [PMID: 39848362 DOI: 10.1016/j.ijbiomac.2025.140096] [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: 11/30/2024] [Revised: 01/04/2025] [Accepted: 01/17/2025] [Indexed: 01/25/2025]
Abstract
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental methods, accurate computational approaches for protein-protein interaction sites (PPIS) prediction can save significant time and costs. In this study, we propose a novel model named MVGNN-PPIS. To the best of our knowledge, it is the first to utilize predicted structures generated by AlphaFold3, and combined with transfer learning techniques, for predicting PPIS. This approach addresses the limitations of traditional methods that depend on native protein structures and multiple sequence alignments (MSA). Additionally, we introduced a multi-view graph framework based on two types of graph structures: the k-nearest neighbor graph and the adjacency matrix. By alternately employing a Graph Transformer and Graph Convolutional Networks (GCN) to aggregate node information, this framework effectively captures both local and global dependencies of each residue in the predicted structures, thereby significantly enhancing the model's sensitivity to binding sites. This framework further integrates direction, distances and angular information between the 3D coordinates of side-chain atom centroids to construct a relative coordinate system, generating enhanced edge features that ensure the model's equivariance to molecular translations and rotations in space. During training, the Focal Loss function is employed to effectively address the class imbalance in the dataset. Experimental results demonstrate that MVGNN outperforms the current state-of-the-art methods across multiple PPIS benchmark datasets. To further validate the model's generalization capability, we extended MVGNN to the domain of predicting protein-nucleic acid interaction sites, where it also achieved superior performance.
Collapse
Affiliation(s)
- Lu Meng
- College of Information Science and Engineering, Northeastern University, China.
| | - Lishuai Wei
- College of Information Science and Engineering, Northeastern University, China
| | - Rina Wu
- College of Information Science and Engineering, Northeastern University, China
| |
Collapse
|
6
|
Khan S, Noor S, Awan HH, Iqbal S, AlQahtani SA, Dilshad N, Ahmad N. Deep-ProBind: binding protein prediction with transformer-based deep learning model. BMC Bioinformatics 2025; 26:88. [PMID: 40121399 PMCID: PMC11929993 DOI: 10.1186/s12859-025-06101-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2024] [Accepted: 03/04/2025] [Indexed: 03/25/2025] Open
Abstract
Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high specificity makes them essential for signal transduction, transport, and enzymatic activity. Traditional experimental methods for identifying protein-binding peptides are costly and time-consuming. Current sequence-based approaches often struggle with accuracy, focusing too narrowly on proximal sequence features and ignoring structural data. This study presents Deep-ProBind, a powerful prediction model designed to classify protein binding sites by integrating sequence and structural information. The proposed model employs a transformer and evolutionary-based attention mechanism, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Pseudo position specific scoring matrix -Discrete Wavelet Transform (PsePSSM -DWT) approach to encode peptides. The SHapley Additive exPlanations (SHAP) algorithm selects the optimal hybrid features, and a Deep Neural Network (DNN) is then used as the classification algorithm to predict protein-binding peptides. The performance of the proposed model was evaluated in comparison with traditional Machine Learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-ProBind achieved 92.67% accuracy with tenfold cross-validation on benchmark datasets and 93.62% accuracy on independent samples. The Deep-ProBind outperforms existing models by 3.57% on training data and 1.52% on independent tests. These results demonstrate Deep-ProBind's reliability and effectiveness, making it a valuable tool for researchers and a potential resource in pharmacological studies, where peptide binding plays a critical role in therapeutic development.
Collapse
Affiliation(s)
- Salman Khan
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KPK, Pakistan
| | - Sumaiya Noor
- Business and Management Sciences Department, Purdue University, West Lafayette, IN, USA
| | - Hamid Hussain Awan
- Department of Computer Science, Rawalpindi Women University, Rawalpindi, 46300, Punjab, Pakistan
| | - Shehryar Iqbal
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
| | - Salman A AlQahtani
- New Emerging Technologies and 5g Network and Beyond Research Chair, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Naqqash Dilshad
- Department of Computer Science & Engineering, Sejong University, Seoul, 05006, South Korea
| | - Nijad Ahmad
- Department of Computer Science, Khurasan University, Jalalabad, Afghanistan.
| |
Collapse
|
7
|
Li Y, Tian Z, Nan X, Zhang S, Zhou Q, Lu S. HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction. Brief Bioinform 2025; 26:bbaf079. [PMID: 40037640 PMCID: PMC11879409 DOI: 10.1093/bib/bbaf079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Revised: 02/10/2025] [Accepted: 02/13/2025] [Indexed: 03/06/2025] Open
Abstract
MOTIVATION Protein-protein interactions play a fundamental role in biological systems. Accurate detection of protein-protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein's natural hierarchical structure is ignored. RESULTS In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the protein is a residue (residue-level graph) and a node in the residue is an atom (atom-level graph). Moreover, we design a spatial-sequential block for capturing complex interaction relationships from spatial and sequential forms of protein. We evaluate HSSPPI on public benchmark datasets and the predicting results outperform the comparative models. This indicates the effectiveness of hierarchical protein modeling and also illustrates that HSSPPI has a strong feature extraction ability by considering spatial and sequential information simultaneously. AVAILABILITY AND IMPLEMENTATION The code of HSSPPI is available at https://github.com/biolushuai/Hierarchical-Spatial-Sequential-Modeling-of-Protein.
Collapse
Affiliation(s)
- Yuguang Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, Zhejiang, China
| | - Xiaofei Nan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Shoutao Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
- Zhongyuan Intelligent Medical Laboratory, Zhengzhou 450001, Henan, China
| | - Qinglei Zhou
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Shuai Lu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, Henan, China
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, Henan, China
| |
Collapse
|
8
|
Zhai Z, Xu S, Ma W, Niu N, Qu C, Zong C. LGS-PPIS: A Local-Global Structural Information Aggregation Framework for Predicting Protein-Protein Interaction Sites. Proteins 2025; 93:716-727. [PMID: 39520116 DOI: 10.1002/prot.26763] [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: 06/12/2024] [Revised: 10/20/2024] [Accepted: 10/22/2024] [Indexed: 11/16/2024]
Abstract
Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic mechanisms of diverse biological processes. On this basis, recent studies have applied deep learning-based technologies to overcome the high cost of wet experiments for PPIS determination. However, the existing methods still suffer from two limitations that remain to be solved. Firstly, the process of feature aggregation in most methods only took into account node features, but ignored the complex edge features of the target residue to its neighbor residues, resulting in insufficient local feature extraction. Secondly, such feature aggregation was limited to aggregating spatially adjacent residues, and could not capture the "remote" residues that played a critical role in determining PPIS, which can be summed up as the lack of global feature at the residue level. To break the above limitations, a local-global structural information aggregation framework, LGS-PPIS, was proposed in this study, including two modules of edge-aware graph convolutional network (EA-GCN) and self-attention integrated with initial residual and identity mapping (SA-RIM), which achieved the aggregation of local and global information for PPIS prediction. Evaluation results of LGS-PPIS showed that the proposed method outperformed state-of-the-art deep learning methods on three widely used PPIS prediction benchmarks. Besides, the results of ablation experiments demonstrated that the local features from spatially adjacent residues and global features from "remote" residues separately captured by EA-GCN and SA-RIM could benefit the model performance. Among them, the former was shown to have a more significant role in the PPIS prediction.
Collapse
Affiliation(s)
- Zhengli Zhai
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Shiya Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Wenjian Ma
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Niuwangjie Niu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Chunyu Qu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Chao Zong
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| |
Collapse
|
9
|
Hui WH, Chen YL, Chang SW. GraphLOGIC: Lethality prediction of osteogenesis imperfecta on type I collagen by a mechanics-informed graph neural network. Int J Biol Macromol 2025; 291:139001. [PMID: 39706395 DOI: 10.1016/j.ijbiomac.2024.139001] [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: 09/11/2024] [Revised: 12/16/2024] [Accepted: 12/17/2024] [Indexed: 12/23/2024]
Abstract
Collagen plays a crucial role in human bodies and has a significant presence in connective tissues. As such, the impact of collagen mutations can be devastating. Osteogenesis imperfecta (OI), a rare genetic disease affecting 1 in every 15,000 to 20,000 people, is one such example characterized by brittle bones. Severe cases of OI could lead to prenatal death. Previous studies have provided insights into the impact of mutations on collagen molecules and predictions of lethality. However, these discussions have focused mainly on mutations in the α1 chain, and some mutation types exhibit poor predictive performance. Coverage of α2 mutations is also limited. We propose a method to predict the risk of lethality for OI-inducing mutations, where a novel mechanics-informed graph representation of the collagen fibril is proposed based on full atomistic simulations to encode sequential and structural information. The method demonstrated improved accuracy in predicting the risk of lethality associated with mutations occurring on both α1 and α2chains. We also found a correlation between the sequences and the predicted OI lethality with the use of a variant of the Grad-CAM technique, where the results agree well with previous studies. Our findings provide insights into the molecular mechanism of collagen on OI lethality.
Collapse
Affiliation(s)
- Wei-Han Hui
- Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Yen-Lin Chen
- Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Shu-Wei Chang
- Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan; Department of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.
| |
Collapse
|
10
|
Han J, Zhang S, Guan M, Li Q, Gao X, Liu J. GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning. Structure 2024; 32:2435-2448.e5. [PMID: 39488202 DOI: 10.1016/j.str.2024.10.011] [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: 05/17/2024] [Revised: 09/13/2024] [Accepted: 10/08/2024] [Indexed: 11/04/2024]
Abstract
The identification of protein binding residues is essential for understanding their functions in vivo. However, it remains a computational challenge to accurately identify binding sites due to the lack of known residue binding patterns. Local residue spatial distribution and its interactive biophysical environment both determine binding patterns. Previous methods could not capture both information simultaneously, resulting in unsatisfactory performance. Here, we present GeoNet, an interpretable geometric deep learning model for predicting DNA, RNA, and protein binding sites by learning the latent residue binding patterns. GeoNet achieves this by introducing a coordinate-free geometric representation to characterize local residue distributions and generating an eigenspace to depict local interactive biophysical environments. Evaluation shows that GeoNet is superior compared to other leading predictors and it shows a strong interpretability of learned representations. We present three test cases, where interaction interfaces were successfully identified with GeoNet.
Collapse
Affiliation(s)
- Jiyun Han
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Shizhuo Zhang
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Mingming Guan
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Qiuyu Li
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
| |
Collapse
|
11
|
Wang L, Li R, Guan X, Yan S. Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion. FRONTIERS IN PLANT SCIENCE 2024; 15:1489116. [PMID: 39687321 PMCID: PMC11646721 DOI: 10.3389/fpls.2024.1489116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Accepted: 11/12/2024] [Indexed: 12/18/2024]
Abstract
Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plant-pathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.
Collapse
Affiliation(s)
- Liuyan Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Rongguang Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Xuemei Guan
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Shanchun Yan
- Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin, Heilongjiang, China
| |
Collapse
|
12
|
Sun X, Wu Z, Su J, Li C. GraphPBSP: Protein binding site prediction based on Graph Attention Network and pre-trained model ProstT5. Int J Biol Macromol 2024; 282:136933. [PMID: 39471921 DOI: 10.1016/j.ijbiomac.2024.136933] [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: 09/28/2024] [Revised: 10/21/2024] [Accepted: 10/24/2024] [Indexed: 11/01/2024]
Abstract
Protein-protein/peptide interactions play crucial roles in various biological processes. Exploring their interactions attracts wide attention. However, accurately predicting their binding sites remains a challenging task. Here, we develop an effective model GraphPBSP based on Graph Attention Network with Convolutional Neural Network and Multilayer Perceptron for protein-protein/peptide binding site prediction, which utilizes various feature types derived from protein sequence and structure including interface residue pairwise propensity developed by us and sequence embeddings obtained from a new pre-trained model ProstT5, alongside physicochemical properties and structural features. To our best knowledge, ProstT5 sequence embeddings and residue pairwise propensity are first introduced for protein-protein/peptide binding site prediction. Additionally, we propose a spatial neighbor-based feature statistic method for effectively considering key spatially neighboring information that significantly improves the model's prediction ability. For model training, a multi-scale objective function is constructed, which enhances the learning capability across samples of the same or different classes. On multiple protein-protein/peptide binding site test sets, GraphPBSP outperforms the currently available state-of-the-art methods with an excellent performance. Additionally, its performances on protein-DNA/RNA binding site test sets also demonstrate its good generalization ability. In conclusion, GraphPBSP is a promising method, which can offer valuable information for protein engineering and drug design.
Collapse
Affiliation(s)
- Xiaohan Sun
- 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
| | - Chunhua Li
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
| |
Collapse
|
13
|
Zhou L, Zhu Z, Gao H, Wang C, Khan MA, Ullah M, Khan SU. Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2024; 9:1572-1586. [DOI: 10.1049/cit2.12395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/05/2024] [Indexed: 01/12/2025] Open
Abstract
AbstractThe prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.
Collapse
Affiliation(s)
- Lin Zhou
- School of Information Science and Technology University of Science and Technology of China Hefei Anhui China
- Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot Wuhu Anhui China
| | - Zhengzhi Zhu
- Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China
| | - Hongbo Gao
- School of Information Science and Technology University of Science and Technology of China Hefei Anhui China
- Institute of Advanced Technology University of Science and Technology of China Hefei Anhui China
- School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore
| | - Chunyu Wang
- School of Biological and Environmental Engineering Chaohu University Chaohu Regional Collaborative Technology Service Center for Rural Revitalization Hefei China
| | - Muhammad Attique Khan
- Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al‐Khobar Saudi Arabia
| | - Mati Ullah
- School of Information Science and Technology University of Science and Technology of China Hefei Anhui China
- School of Automation Northwestern Polytechnical University Xi'an Shaanxi China
| | - Siffat Ullah Khan
- School of Information Science and Technology University of Science and Technology of China Hefei Anhui China
- Institute of Engineering and Computing Science University of Science and Technology of Bannu KPK Bannu Pakistan
| |
Collapse
|
14
|
Asediya VS, Anjaria PA, Mathakiya RA, Koringa PG, Nayak JB, Bisht D, Fulmali D, Patel VA, Desai DN. Vaccine development using artificial intelligence and machine learning: A review. Int J Biol Macromol 2024; 282:136643. [PMID: 39426778 DOI: 10.1016/j.ijbiomac.2024.136643] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 09/30/2024] [Accepted: 10/15/2024] [Indexed: 10/21/2024]
Abstract
The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.
Collapse
Affiliation(s)
| | | | | | | | | | - Deepanker Bisht
- Indian Veterinary Research Institute, Izatnagar, U.P., India
| | | | | | | |
Collapse
|
15
|
Meng L, Zhang H. GACT-PPIS: Prediction of protein-protein interaction sites based on graph structure and transformer network. Int J Biol Macromol 2024; 283:137272. [PMID: 39528184 DOI: 10.1016/j.ijbiomac.2024.137272] [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: 06/28/2024] [Revised: 10/19/2024] [Accepted: 11/04/2024] [Indexed: 11/16/2024]
Abstract
The prediction of protein-protein interaction sites (PPIS) is currently crucial for regulating many biological activities in cells and developing drugs for various diseases. Deep learning-based methods have been proposed for predicting PPIS, significantly reducing the manpower and time costs associated with traditional experimental methods such as yeast two-hybrid, mass spectrometry, and affinity purification. However, the predictive accuracy of these deep learning methods has not yet reached the expected level. Therefore, we introduce a model called GACT-PPIS. The design of the GACT-PPIS algorithm aims to utilize combined information from protein sequences and structures as input to predict protein-protein interaction sites. The core of GACT-PPIS utilizes an Enhanced Graph Attention Network (EGAT) with initial residual and identity mappings, along with a deep Transformer network as the basic units, supplemented by Graph Convolutional Networks (GCN), effectively aggregating information from neighboring nodes for each node. After multiple network layers, the information of the entire protein is also fused into the nodes, and the Transformer network further enhances the model's performance. Experimental results show that GACT-PPIS outperforms the most representative models in terms of Recall, F1-measure, MCC, AUROC, and AUPRC on the benchmark test set (Test-60). Additionally, on other independent test sets (UBTest-31-6), GACT-PPIS leads in terms of Accuracy, Precision, Recall, F1-measure, MCC, AUROC, and AUPRC compared to the most representative models. It is worth noting that GACT-PPIS demonstrates excellent generalization and versatility across different test sets, showcasing good performance on multiple test sets for the same trained GACT-PPIS model.
Collapse
Affiliation(s)
- Lu Meng
- College of Information Science and Engineering, Northeastern University, China.
| | - Huashuai Zhang
- College of Information Science and Engineering, Northeastern University, China
| |
Collapse
|
16
|
Ljubic M, D'Ercole C, Waheed Y, de Marco A, Borišek J, De March M. Computational study of the HLTF ATPase remodeling domain suggests its activity on dsDNA and implications in damage tolerance. J Struct Biol 2024; 216:108149. [PMID: 39491691 DOI: 10.1016/j.jsb.2024.108149] [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: 07/19/2024] [Revised: 10/04/2024] [Accepted: 10/28/2024] [Indexed: 11/05/2024]
Abstract
The Helicase-Like Transcription Factor (HLTF) is member of the SWI/SNF-family of ATP dependent chromatin remodellers known primarily for maintaining genome stability. Biochemical and cellular assays support its multiple roles in DNA Damage Tolerance. However, the lack of sufficient structural data limits the comprehension of the molecular basis of its modes of action. In this work we have modelled and characterized the HLTF ATPase remodeling domain by using bioinformatic tools and all-atoms molecular dynamics simulations. In-silico results suggested that its binding to dsDNA is mainly mediated by the positively charged residues Arg563 and Lys913, found conserved in HLTF homologs, and Arg620 and Lys999, found only in HLTF. Interestingly, these residues are mutated in cancer cells. During translocation on dsDNA, HLTF remains persistently bound through the N-terminal ATPase subunit. However, DNA advancement occurs only in the presence of the synergic-anticorrelated action of both motor lobes. In contrast, the C-terminal facilitates substrate remodeling through DNA deformation and generation of bulges according to a wave-model. Finally, the large conformational change suggested between the two motor-remodeling subunits might be activated upon the release of PARP1 on stalled fork and be responsible for the intervention of HLTF-HIRAN in the formation of D-loop and 4-way junction DNA structures.
Collapse
Affiliation(s)
- Martin Ljubic
- Theory Department, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Claudia D'Ercole
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Vipavska 13, SI-500, Nova Gorica, Slovenia
| | - Yossma Waheed
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Vipavska 13, SI-500, Nova Gorica, Slovenia; National Institute of Science and Technology, Sector H-12, Islamabad Capital Territory, Pakistan
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Vipavska 13, SI-500, Nova Gorica, Slovenia
| | - Jure Borišek
- Theory Department, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Matteo De March
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Vipavska 13, SI-500, Nova Gorica, Slovenia.
| |
Collapse
|
17
|
Zhang K, Tao Y, Wang F. AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction. Brief Bioinform 2024; 26:bbaf008. [PMID: 39831890 PMCID: PMC11744619 DOI: 10.1093/bib/bbaf008] [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: 07/21/2024] [Revised: 11/07/2024] [Accepted: 01/04/2025] [Indexed: 01/22/2025] Open
Abstract
Antibodies play a key role in medical diagnostics and therapeutics. Accurately predicting antibody-antigen binding is essential for developing effective treatments. Traditional protein-protein interaction prediction methods often fall short because they do not account for the unique structural and dynamic properties of antibodies and antigens. In this study, we present AntiBinder, a novel predictive model specifically designed to address these challenges. AntiBinder integrates the unique structural and sequence characteristics of antibodies and antigens into its framework and employs a bidirectional cross-attention mechanism to automatically learn the intrinsic mechanisms of antigen-antibody binding, eliminating the need for manual feature engineering. Our comprehensive experiments, which include predicting interactions between known antigens and new antibodies, predicting the binding of previously unseen antigens, and predicting cross-species antigen-antibody interactions, demonstrate that AntiBinder outperforms existing state-of-the-art methods. Notably, AntiBinder excels in predicting interactions with unseen antigens and maintains a reasonable level of predictive capability in challenging cross-species prediction tasks. AntiBinder's ability to model complex antigen-antibody interactions highlights its potential applications in biomedical research and therapeutic development, including the design of vaccines and antibody therapies for rapidly emerging infectious diseases.
Collapse
Affiliation(s)
- Kaiwen Zhang
- Research Center for Social Intelligence, Fudan University, Handan Street, Shanghai 200433, China
- School of Computer Science and Technology, Fudan University, Handan Street, Shanghai 200433, China
| | - Yuhao Tao
- Research Center for Social Intelligence, Fudan University, Handan Street, Shanghai 200433, China
- School of Computer Science and Technology, Fudan University, Handan Street, Shanghai 200433, China
| | - Fei Wang
- Research Center for Social Intelligence, Fudan University, Handan Street, Shanghai 200433, China
- School of Computer Science and Technology, Fudan University, Handan Street, Shanghai 200433, China
| |
Collapse
|
18
|
Gu M, Yang W, Liu M. Prediction of antibody-antigen interaction based on backbone aware with invariant point attention. BMC Bioinformatics 2024; 25:348. [PMID: 39506679 PMCID: PMC11542381 DOI: 10.1186/s12859-024-05961-w] [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: 08/18/2024] [Accepted: 10/16/2024] [Indexed: 11/08/2024] Open
Abstract
BACKGROUND Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack of characterizing the underlying structure of the antibody. RESULTS Beneficial from the recent breakthroughs in deep learning for antibody structure prediction, we propose a novel neural network architecture to predict antibody-antigen interaction. We first introduce AbAgIPA: an antibody structure prediction network to obtain the antibody backbone structure, where the structural features of antibodies and antigens are encoded into representation vectors according to the amino acid physicochemical features and Invariant Point Attention (IPA) computation methods. Finally, the antibody-antigen interaction is predicted by global max pooling, feature concatenation, and a fully connected layer. We evaluated our method on antigen diversity and antigen-specific antibody-antigen interaction datasets. Additionally, our model exhibits a commendable level of interpretability, essential for understanding underlying interaction mechanisms. CONCLUSIONS Quantitative experimental results demonstrate that the new neural network architecture significantly outperforms the best sequence-based methods as well as the methods based on residue contact maps and graph convolution networks (GCNs). The source code is freely available on GitHub at https://github.com/gmthu66/AbAgIPA .
Collapse
Affiliation(s)
- Miao Gu
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Weiyang Yang
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Min Liu
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| |
Collapse
|
19
|
Wang S, Dong K, Liang D, Zhang Y, Li X, Song T. MIPPIS: protein-protein interaction site prediction network with multi-information fusion. BMC Bioinformatics 2024; 25:345. [PMID: 39497043 PMCID: PMC11536593 DOI: 10.1186/s12859-024-05964-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Accepted: 10/21/2024] [Indexed: 11/06/2024] Open
Abstract
BACKGROUND The prediction of protein-protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy. RESULTS Addressing these challenges, we propose a novel protein-protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein's primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction. CONCLUSION Compared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model.
Collapse
Affiliation(s)
- Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Kaiyu Dong
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Dingming Liang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Yunjing Zhang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Xue Li
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China.
- Department of Artificial Intelligence, Polytechnical University of Madrid, Madrid, 28031, Spain.
| |
Collapse
|
20
|
Zhong J, Zhao H, Zhao Q, Zhou R, Zhang L, Guo F, Wang J. RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1676-1684. [PMID: 38843057 DOI: 10.1109/tcbb.2024.3410350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentrate on sequence context features and rarely consider the spatial neighborhood features. To address this limitation, we propose a novel residual graph convolutional network for structure-based PPI site prediction (RGCNPPIS). Specifically, we use a GCN module to extract the global structural features from all spatial neighborhoods, and utilize the GraphSage module to extract local structural features from local spatial neighborhoods. To the best of our knowledge, this is the first work utilizing local structural features for PPI site prediction. We also propose an enhanced residual graph connection to combine the initial node representation, local structural features, and the previous GCN layer's node representation, which enables information transfer between layers and alleviates the over-smoothing problem. Evaluation results demonstrate that RGCNPPIS outperforms state-of-the-art methods on three independent test sets. In addition, the results of ablation experiments and case studies confirm that RGCNPPIS is an effective tool for PPI site prediction.
Collapse
|
21
|
Tang T, Li T, Li W, Cao X, Liu Y, Zeng X. Anti-symmetric framework for balanced learning of protein-protein interactions. Bioinformatics 2024; 40:btae603. [PMID: 39404784 PMCID: PMC11513017 DOI: 10.1093/bioinformatics/btae603] [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: 07/17/2024] [Revised: 09/13/2024] [Accepted: 10/12/2024] [Indexed: 10/29/2024] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. RESULT Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. AVAILABILITY AND IMPLEMENTATION The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.
Collapse
Affiliation(s)
- Tao Tang
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Tianyang Li
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Weizhuo Li
- School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xiaofeng Cao
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410086, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410086, China
| |
Collapse
|
22
|
González-Avendaño M, López J, Vergara-Jaque A, Cerda O. The power of computational proteomics platforms to decipher protein-protein interactions. Curr Opin Struct Biol 2024; 88:102882. [PMID: 39003917 DOI: 10.1016/j.sbi.2024.102882] [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: 03/28/2024] [Revised: 05/31/2024] [Accepted: 06/19/2024] [Indexed: 07/16/2024]
Abstract
Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.
Collapse
Affiliation(s)
- Mariela González-Avendaño
- Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile
| | - Joaquín López
- Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile; Program of Cellular and Molecular Biology, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Ariela Vergara-Jaque
- Center for Bioinformatics, Simulation and Modeling (CBSM), Faculty of Engineering, Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile.
| | - Oscar Cerda
- Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Santiago, Chile; Program of Cellular and Molecular Biology, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, Universidad de Chile, Santiago, Chile.
| |
Collapse
|
23
|
Zheng Y, Li Q, Freiberger MI, Song H, Hu G, Zhang M, Gu R, Li J. Predicting the Dynamic Interaction of Intrinsically Disordered Proteins. J Chem Inf Model 2024; 64:6768-6777. [PMID: 39163306 DOI: 10.1021/acs.jcim.4c00930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and are affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-embedded LSTM network, to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph, which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied, and the data sets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interactions is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.
Collapse
Affiliation(s)
- Yuchuan Zheng
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| | - Qixiu Li
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| | - Maria I Freiberger
- Protein Physiology Lab, Departamento de Quimica Biologica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires-CONICET-IQUIBICEN, Buenos Aires C1428EGA, Argentina
| | - Haoyu Song
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| | - Guorong Hu
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| | - Moxin Zhang
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| | - Ruoxu Gu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Jingyuan Li
- School of Physics, Zhejiang University, Hangzhou 310058, PR China
| |
Collapse
|
24
|
Ouyang J, Gao Y, Yang Y. PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network. BMC Bioinformatics 2024; 25:287. [PMID: 39223474 PMCID: PMC11370006 DOI: 10.1186/s12859-024-05914-3] [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: 05/04/2023] [Accepted: 08/22/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing. RESULTS Here, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it's also useful to analyze interactions in other types of protein complexes.
Collapse
Affiliation(s)
- J Ouyang
- Key Laboratory of Intelligent Computing Information Processing, Xiangtan University, Xiangtan, China
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Y Gao
- Key Laboratory of Intelligent Computing Information Processing, Xiangtan University, Xiangtan, China.
- School of Computer Science, Xiangtan University, Xiangtan, China.
| | - Y Yang
- School of Computer Science, Xiangtan University, Xiangtan, China
| |
Collapse
|
25
|
Carroll M, Rosenbaum E, Viswanathan R. Computational Methods to Predict Conformational B-Cell Epitopes. Biomolecules 2024; 14:983. [PMID: 39199371 PMCID: PMC11352882 DOI: 10.3390/biom14080983] [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: 07/09/2024] [Revised: 08/04/2024] [Accepted: 08/08/2024] [Indexed: 09/01/2024] Open
Abstract
Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.
Collapse
Affiliation(s)
| | | | - R. Viswanathan
- Department of Chemistry and Biochemistry, Yeshiva College, Yeshiva University, New York, NY 10033, USA; (M.C.); (E.R.)
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Pancino N, Gallegati C, Romagnoli F, Bongini P, Bianchini M. Protein-Protein Interfaces: A Graph Neural Network Approach. Int J Mol Sci 2024; 25:5870. [PMID: 38892057 PMCID: PMC11173158 DOI: 10.3390/ijms25115870] [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: 04/14/2024] [Revised: 05/15/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since protein structure can be summarized as a graph, graph neural networks (GNNs) represent the ideal deep learning architecture for the task. In this work, PPI prediction is modeled as a node-focused binary classification task using a GNN to determine whether a generic residue is part of the interface. Biological data were obtained from the Protein Data Bank in Europe (PDBe), leveraging the Protein Interfaces, Surfaces, and Assemblies (PISA) service. To gain a deeper understanding of how proteins interact, the data obtained from PISA were assembled into three datasets: Whole, Interface, and Chain, consisting of data on the whole protein, couples of interacting chains, and single chains, respectively. These three datasets correspond to three different nuances of the problem: identifying interfaces between protein complexes, between chains of the same protein, and interface regions in general. The results indicate that GNNs are capable of solving each of the three tasks with very good performance levels.
Collapse
Affiliation(s)
- Niccolò Pancino
- Department of Information Engineering and Mathematics, University of Siena, Via Roma, 56, 53100 Siena, Italy; (C.G.); (P.B.); (M.B.)
| | | | | | | | | |
Collapse
|
28
|
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 PMCID: PMC11530528 DOI: 10.1038/s41467-024-48801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [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.
Collapse
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.
| |
Collapse
|
29
|
Yao R, Shen Z, Xu X, Ling G, Xiang R, Song T, Zhai F, Zhai Y. Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis. Front Pharmacol 2024; 15:1393415. [PMID: 38799167 PMCID: PMC11116974 DOI: 10.3389/fphar.2024.1393415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/12/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Fei Zhai
- Faculty of Medical Device, Shenyang Pharmaceutical University, Shenyang, China
| | - Yuxuan Zhai
- Faculty of Medical Device, Shenyang Pharmaceutical University, Shenyang, China
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Wang J, Chen S, Yuan Q, Chen J, Li D, Wang L, Yang Y. Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation. J Comput Chem 2024; 45:436-445. [PMID: 37933773 DOI: 10.1002/jcc.27249] [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: 06/26/2023] [Revised: 10/11/2023] [Accepted: 10/21/2023] [Indexed: 11/08/2023]
Abstract
Solubility is one of the most important properties of protein. Protein solubility can be greatly changed by single amino acid mutations and the reduced protein solubility could lead to diseases. Since experimental methods to determine solubility are time-consuming and expensive, in-silico methods have been developed to predict the protein solubility changes caused by mutations mostly through protein evolution information. However, these methods are slow since it takes long time to obtain evolution information through multiple sequence alignment. In addition, these methods are of low performance because they do not fully utilize protein 3D structures due to a lack of experimental structures for most proteins. Here, we proposed a sequence-based method DeepMutSol to predict solubility change from residual mutations based on the Graph Convolutional Neural Network (GCN), where the protein graph was initiated according to predicted protein structure from Alphafold2, and the nodes (residues) were represented by protein language embeddings. To circumvent the small data of solubility changes, we further pretrained the model over absolute protein solubility. DeepMutSol was shown to outperform state-of-the-art methods in benchmark tests. In addition, we applied the method to clinically relevant genes from the ClinVar database and the predicted solubility changes were shown able to separate pathogenic mutations. All of the data sets and the source code are available at https://github.com/biomed-AI/DeepMutSol.
Collapse
Affiliation(s)
- Jing Wang
- Guangzhou institute of technology, Xidian University, Guangzhou, China
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Sheng Chen
- 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
| | - Jianwen Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Danping Li
- School of Telecommunications Engineering, Xidian University, Xi'an, China
| | - Lei Wang
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
32
|
Ashrafzadeh S, Golding GB, Ilie S, Ilie L. Scoring alignments by embedding vector similarity. Brief Bioinform 2024; 25:bbae178. [PMID: 38695119 PMCID: PMC11063651 DOI: 10.1093/bib/bbae178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/20/2024] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
Sequence similarity is of paramount importance in biology, as similar sequences tend to have similar function and share common ancestry. Scoring matrices, such as PAM or BLOSUM, play a crucial role in all bioinformatics algorithms for identifying similarities, but have the drawback that they are fixed, independent of context. We propose a new scoring method for amino acid similarity that remedies this weakness, being contextually dependent. It relies on recent advances in deep learning architectures that employ self-supervised learning in order to leverage the power of enormous amounts of unlabelled data to generate contextual embeddings, which are vector representations for words. These ideas have been applied to protein sequences, producing embedding vectors for protein residues. We propose the E-score between two residues as the cosine similarity between their embedding vector representations. Thorough testing on a wide variety of reference multiple sequence alignments indicate that the alignments produced using the new $E$-score method, especially ProtT5-score, are significantly better than those obtained using BLOSUM matrices. The new method proposes to change the way alignments are computed, with far-reaching implications in all areas of textual data that use sequence similarity. The program to compute alignments based on various $E$-scores is available as a web server at e-score.csd.uwo.ca. The source code is freely available for download from github.com/lucian-ilie/E-score.
Collapse
Affiliation(s)
- Sepehr Ashrafzadeh
- Department of Computer Science, University of Western Ontario, London, N6A 5B7, Ontario, Canada
| | - G Brian Golding
- Department of Biology, McMaster University, Hamilton, L8S 4K1, Ontario, Canada
| | - Silvana Ilie
- Department of Mathematics, Toronto Metropolitan University, Toronto, M5B 2K3, Ontario, Canada
| | - Lucian Ilie
- Department of Computer Science, University of Western Ontario, London, N6A 5B7, Ontario, Canada
| |
Collapse
|
33
|
Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
Collapse
Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| |
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|
35
|
Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
Collapse
Affiliation(s)
| | | | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA; (D.N.K.); (A.D.M.)
| |
Collapse
|
36
|
Zhao C, Wang S. AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction. Comput Biol Med 2024; 169:107822. [PMID: 38091726 DOI: 10.1016/j.compbiomed.2023.107822] [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: 10/06/2023] [Revised: 11/19/2023] [Accepted: 12/04/2023] [Indexed: 02/08/2024]
Abstract
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this paper, we propose an algorithm, DeepMSA+, to generate protein multiple sequence alignments (MSAs) and to construct feature representations based on co-evolutionary information and sequence information derived from MSAs. We also propose an improved deep learning model, AttCON, for training input features to predict protein contact map. The model incorporates an attention module, and by comparing different attention modules, we find a parameter-free attention module suitable for contact map prediction. Additionally, we use the Focal Loss function to better address the data imbalance issue in protein contact map. We also developed a weighted evaluation index (W score) for model evaluation, which takes into account a wide range of metrics. W score is comprehensive in its scope, with a particular focus on the precision of predictions for medium-range and long-range contacts. Experimental results show that AttCON achieves good precision results on datasets from CASP11 to CASP15. Compared to some state-of-the-art methods, it achieves an average improvement of over 5% in both medium-range and long-range predictions, and W score is improved by an average of 2 points.
Collapse
Affiliation(s)
- Che Zhao
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, 650504, Yunnan, China.
| |
Collapse
|
37
|
Fu X, Yuan Y, Qiu H, Suo H, Song Y, Li A, Zhang Y, Xiao C, Li Y, Dou L, Zhang Z, Cui F. AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks. Methods 2024; 222:142-151. [PMID: 38242383 DOI: 10.1016/j.ymeth.2024.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.
Collapse
Affiliation(s)
- Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Haoye Qiu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Haodong Suo
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yingying Song
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Anqi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yupeng Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yazi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, USA
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
| |
Collapse
|
38
|
Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
Collapse
Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
| |
Collapse
|
39
|
Ding H, Li X, Han P, Tian X, Jing F, Wang S, Song T, Fu H, Kang N. MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network. Bioinformatics 2024; 40:btae269. [PMID: 38640481 PMCID: PMC11252844 DOI: 10.1093/bioinformatics/btae269] [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: 02/19/2024] [Revised: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 04/21/2024] Open
Abstract
MOTIVATION Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space. RESULTS In response to this gap, this article proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and E(n) equivariant graph neural network (EGNN). There are two channels in MEG-PPIS: the original graph and the subgraph obtained by graph pooling. The model can iteratively update the features of the original graph and subgraph through the weight-sharing EGNN. Subsequently, the max-pooling operation aggregates the updated features of the original graph and subgraph. Ultimately, the model feeds node features into the prediction layer to obtain prediction results. Comparative assessments against other methods on benchmark datasets reveal that MEG-PPIS achieves optimal performance across all evaluation metrics and gets the fastest runtime. Furthermore, specific case studies demonstrate that our method can predict more true positive and true negative sites than the current best method, proving that our model achieves better performance in the PPIS prediction task. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/dhz234/MEG-PPIS.git.
Collapse
Affiliation(s)
- Hongzhen Ding
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Xue Li
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Peifu Han
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Xu Tian
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Fengrui Jing
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Shuang Wang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Tao Song
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Hanjiao Fu
- School of Humanities and Law, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Na Kang
- The Ninth Department of Health Care Administration, the Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| |
Collapse
|
40
|
Zhang S, Han J, Liu J. Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning. Gigascience 2024; 13:giae080. [PMID: 39484977 PMCID: PMC11528319 DOI: 10.1093/gigascience/giae080] [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: 07/02/2024] [Revised: 08/29/2024] [Accepted: 09/25/2024] [Indexed: 11/03/2024] Open
Abstract
Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computational approaches remain highly challenging due to the limited knowledge of residue binding patterns. The binding pattern of a residue should be characterized by the spatial distribution of its neighboring residues combined with their physicochemical information interaction, which yet cannot be achieved by previous methods. Here, we design GraphRBF, a hierarchical geometric deep learning model to learn residue binding patterns from big data. To achieve it, GraphRBF describes physicochemical information interactions by designing an enhanced graph neural network and characterizes residue spatial distributions by introducing a prioritized radial basis function neural network. After training and testing, GraphRBF shows great improvements over existing state-of-the-art methods and strong interpretability of its learned representations. Applying GraphRBF to the SARS-CoV-2 omicron spike protein, it successfully identifies known epitopes of the protein. Moreover, it predicts multiple potential binding regions for new nanobodies or even new drugs with strong evidence. A user-friendly online server for GraphRBF is freely available at http://liulab.top/GraphRBF/server.
Collapse
Affiliation(s)
- Shizhuo Zhang
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Jiyun Han
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| |
Collapse
|
41
|
Hosseini S, Golding GB, Ilie L. Seq-InSite: sequence supersedes structure for protein interaction site prediction. Bioinformatics 2024; 40:btad738. [PMID: 38212995 PMCID: PMC10796176 DOI: 10.1093/bioinformatics/btad738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/17/2023] [Accepted: 01/10/2024] [Indexed: 01/13/2024] Open
Abstract
MOTIVATION Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. As experimental methods are expensive and time consuming, computational prediction of the interaction sites has been studied extensively. Structure-based programs are the most accurate, while the sequence-based ones are much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. Ideally, we would like a tool that has the quality of the former and the applicability of the latter. RESULTS We provide here the first solution that achieves these two goals. Our new sequence-based program, Seq-InSite, greatly surpasses the performance of sequence-based models, matching the quality of state-of-the-art structure-based predictors, thus effectively superseding the need for models requiring structure. The predictive power of Seq-InSite is illustrated using an analysis of evolutionary conservation for four protein sequences. AVAILABILITY AND IMPLEMENTATION Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/ and as free source code, including trained models and all datasets used for training and testing, at https://github.com/lucian-ilie/Seq-InSite.
Collapse
Affiliation(s)
- SeyedMohsen Hosseini
- Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada
| | - G Brian Golding
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Lucian Ilie
- Department of Computer Science, University of Western Ontario, London, ON N6A 5B7, Canada
| |
Collapse
|
42
|
Zeng X, Meng FF, Li X, Zhong KY, Jiang B, Li Y. GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block. Comput Biol Med 2024; 168:107683. [PMID: 37984202 DOI: 10.1016/j.compbiomed.2023.107683] [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: 08/21/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.
Collapse
Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Fan-Fang Meng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Xin Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Bei Jiang
- Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan, Dali University, Dali, 671000, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
| |
Collapse
|
43
|
Wu J, Liu B, Zhang J, Wang Z, Li J. DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning. BMC Bioinformatics 2023; 24:473. [PMID: 38097937 PMCID: PMC10722729 DOI: 10.1186/s12859-023-05594-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
PURPOSE Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
Collapse
Affiliation(s)
- Jiahui Wu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Bo Liu
- School of Mathematical and Computational Sciences, Massey University, Auckland, 0745, New Zealand.
| | - Jidong Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zhihan Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| |
Collapse
|
44
|
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: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [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/.
Collapse
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
| |
Collapse
|
45
|
Bi X, Liang W, Zhao Q, Wang J. SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data. Bioinformatics 2023; 39:btad662. [PMID: 37941450 PMCID: PMC10666204 DOI: 10.1093/bioinformatics/btad662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/17/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023] Open
Abstract
MOTIVATION Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. RESULTS To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification. AVAILABILITY AND IMPLEMENTATION https://github.com/bixuehua/SSLpheno.
Collapse
Affiliation(s)
- Xuehua Bi
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi 830017, China
| | - Weiyang Liang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| |
Collapse
|
46
|
Kewalramani N, Emili A, Crovella M. State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage. Proteomics 2023; 23:e2200292. [PMID: 37401192 DOI: 10.1002/pmic.202200292] [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: 04/02/2023] [Revised: 05/24/2023] [Accepted: 06/09/2023] [Indexed: 07/05/2023]
Abstract
Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.
Collapse
Affiliation(s)
- Neal Kewalramani
- Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
| | - Andrew Emili
- OHSU Knight Cancer Institute, Portland, Oregon, USA
| | - Mark Crovella
- Department of Computer Science and Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
| |
Collapse
|
47
|
Halsana AA, Chakroborty T, Halder AK, Basu S. DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions. IEEE Trans Nanobioscience 2023; 22:904-911. [PMID: 37028059 DOI: 10.1109/tnb.2023.3251192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.
Collapse
|
48
|
Mou M, Pan Z, Zhou Z, Zheng L, Zhang H, Shi S, Li F, Sun X, Zhu F. A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites. RESEARCH (WASHINGTON, D.C.) 2023; 6:0240. [PMID: 37771850 PMCID: PMC10528219 DOI: 10.34133/research.0240] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023]
Abstract
The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.
Collapse
Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Zhimeng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| |
Collapse
|
49
|
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: 10] [Impact Index Per Article: 5.0] [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.
Collapse
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
| |
Collapse
|
50
|
Wu H, Han J, Zhang S, Xin G, Mou C, Liu J. Spatom: a graph neural network for structure-based protein-protein interaction site prediction. Brief Bioinform 2023; 24:bbad345. [PMID: 37779247 DOI: 10.1093/bib/bbad345] [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: 07/06/2023] [Revised: 08/22/2023] [Accepted: 09/13/2023] [Indexed: 10/03/2023] Open
Abstract
Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.
Collapse
Affiliation(s)
- Haonan Wu
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Jiyun Han
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Shizhuo Zhang
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Gaojia Xin
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Chaozhou Mou
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| |
Collapse
|