Identifying protein-peptide interaction pairs and their corresponding binding residues is crucial and can greatly facilitate peptide therapeutic design as well as improve our understanding of protein function mechanisms. Recently, several computational approaches have been proposed to solve the protein-peptide interaction prediction problem. However, most existing prediction methods cannot simultaneously predict protein-peptide interaction pairs and their binding residues directly from the protein and peptide sequences. Here, we developed a Comprehensive Protein-Peptide Interaction prediction Framework (CPPIF), to predict both binary protein-peptide interaction and their binding residues. We also constructed a benchmark dataset containing more than 8900 protein-peptide interacting pairs with non-covalent interactions and their corresponding binding residues to systematically evaluate the performances of existing models. Comprehensive evaluation on the benchmark datasets demonstrated that CPPIF can successfully predict the non-covalent protein-peptide interactions that cannot be effectively captured by previous prediction methods. Moreover, CPPIF outperformed other state-of-the-art methods in predicting binding residues in the peptides and achieved good performance in the identification of important binding residues in the proteins.

In this work, we propose MEDICO, a multiview deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 inhibitor discovery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multiview representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, novel, and unique molecules under benchmarking comparisons, particularly achieving $\tilde {8}5 \%$ improvement compared with the state-of-the-art methods in terms of validity. Importantly, we showcase that the multiview deep learning model enables us to generate not only the molecules structurally similar to the targeted molecules but also the molecules with desired chemical properties. Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that we successfully generate new small molecules with desired drug-like properties for the Mpro by integrating molecular docking into our model as a chemical priori, potentially accelerating the de novo design of COVID-19 drugs. Furthermore, we apply MEDICO to the structural optimization of three well-known Mpro inhibitors (N3, 11a, and GC376) and achieve $\tilde {8}8 \%$ improvement compared with the origin inhibitors in their binding affinity to Mpro, demonstrating the application value of our model for the development of therapeutics for SARS-CoV-2 infection.

MOTIVATION: 5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios. RESULTS: Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/Challis-yin/NanoCon.

SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on third-party tools or complex data preprocessing for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers) -based contrastive learning framework to predict the protein-peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of feature engineering. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structures and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Interestingly, the interpretable analysis of our model highlights the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ruheng-W/PepBCL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.