Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO.

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.

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop a new deep-learning architecture to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization, and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for the predictive models covering several aspects, such as model interpretability, feature analysis, functional sequential region discovery, etc. Additionally, DeepBIO supports over 20 functional site annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows fast prediction with up to million-scale sequence data, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust, and interpretable prediction, demonstrating the power of deep learning in biological sequence analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional insights at both sequence-level and base-level from biological sequences alone. DeepBIO is publicly available at http://inner.wei-group.net/DeepBIO.

Peptides have recently emerged as therapeutic molecules against various diseases, and the secondary structure of peptides is a crucial determinant of their bioactivity. However, accurately predicting peptide secondary structures remains a challenging task due to the lack of peptide sequence data and low prediction efficiency caused by limitations in feature engineering. Therefore, we developed PHAT, a deep learning framework based on a hypergraph multi-head attention network and transfer learning for the prediction of peptide secondary structures. Comparative results demonstrated the outstanding performance and robustness of PHAT. In particular, PHAT automatically learns a set of biologically meaningful knowledge on secondary sub-structures, overcoming the limitations of "black-box" in deep learning-based models and providing good interpretability. Additionally, we demonstrated that the structure information derived from PHAT significantly improved the performance of downstream tasks such as the prediction of peptide toxicity and protein-peptide binding sites. Importantly, we further explored the applicability of PHAT for contact map prediction, which can aid in the reconstruction of peptide 3-D structures, thus highlighting the versatility of our model. To facilitate the use of PHAT, we establish an online server which is accessible via https://server.wei-group.net/PHAT/. We expect our work to assist in the design of functional peptides and contribute to the advancement of structural biology research.