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Chen Y, Chen G, Chen CYC. MFTrans: A multi-feature transformer network for protein secondary structure prediction. Int J Biol Macromol 2024; 267:131311. [PMID: 38599417 DOI: 10.1016/j.ijbiomac.2024.131311] [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: 05/07/2023] [Revised: 03/21/2024] [Accepted: 03/30/2024] [Indexed: 04/12/2024]
Abstract
In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.
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Affiliation(s)
- Yifu Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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Huang Y, Huang HY, Chen Y, Lin YCD, Yao L, Lin T, Leng J, Chang Y, Zhang Y, Zhu Z, Ma K, Cheng YN, Lee TY, Huang HD. A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning. Int J Mol Sci 2023; 24:14061. [PMID: 37762364 PMCID: PMC10531393 DOI: 10.3390/ijms241814061] [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: 07/27/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.
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Affiliation(s)
- Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yigang Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yang-Chi-Dung Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Tianxiu Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Junlin Leng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yuan Chang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yuntian Zhang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Zihao Zhu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Kun Ma
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yeong-Nan Cheng
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-N.C.)
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-N.C.)
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
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Jiang Y, Wang R, Feng J, Jin J, Liang S, Li Z, Yu Y, Ma A, Su R, Zou Q, Ma Q, Wei L. Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206151. [PMID: 36794291 PMCID: PMC10104664 DOI: 10.1002/advs.202206151] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptide secondary structures and the exploration of downstream tasks. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. The algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. The interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. The importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis is further demonstrated, highlighting the versatility of our models. To facilitate the use of the model, an online server is established which is accessible via http://inner.wei-group.net/PHAT/. The work is expected to assist in the design of functional peptides and contribute to the advancement of structural biology research.
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Affiliation(s)
- Yi Jiang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Ruheng Wang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Jiuxin Feng
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Junru Jin
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Sirui Liang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Zhongshen Li
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Yingying Yu
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Anjun Ma
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOH43210USA
| | - Ran Su
- College of Intelligence and ComputingTianjin UniversityTianjin300350China
| | - Quan Zou
- Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of ChinaChengduSichuan610054China
| | - Qin Ma
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOH43210USA
| | - Leyi Wei
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
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Binette V, Mousseau N, Tuffery P. A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction. J Chem Theory Comput 2022; 18:2720-2736. [PMID: 35298162 DOI: 10.1021/acs.jctc.1c01293] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.
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Affiliation(s)
- Vincent Binette
- Départment de Physique, Université de Montréal, Case postale 6128, succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Normand Mousseau
- Départment de Physique, Université de Montréal, Case postale 6128, succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Pierre Tuffery
- Université de Paris, INSERM U1133, CNRS UMR 8251, F-75205 Paris, France
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