MOTIVATION: In recent years, protein language models (pLMs) and graph neural networks (GNNs) have demonstrated powerful expressive and reasoning capabilities in modeling protein-RNA/DNA interactions. However, existing methods, which use simple graphs to describe the relationships between residues, struggle to effectively capture the high-order, multi-body residue interactions present in protein-nucleic acid complex structures. In fact, spatially continuous but sequence-wise discontinuous residues often cooperatively determine nucleic acid binding capacity. RESULTS: In this study, we present IKANbind, a computational approach that combines hypergraph representation learning and interpretable Kolmogorov-Arnold Networks (KANs), for identifying nucleic acid binding residues (NBRs) in proteins. By combining the advantages of pLM, hypergraph neural networks and symbolic KAN, IKANbind outperforms existing methods on multiple NBR benchmark datasets. We also demonstrated that the pLM used in IKANbind can implicitly learn the physicochemical properties of binding residues, such as charge and hydrophobicity. In addition, the symbolic KAN, which uses a unique weighted mechanism of decomposable basis functions, can accurately identify the features with the greatest contribution to NBR recognition. We found that polarity and charge make greater contributions to NBR prediction than other physicochemical properties or evolutionary information. Finally, IKANbind achieves promising performance when extended to other ligand-binding residue prediction tasks. AVAILABILITY AND IMPLEMENTATION: IKANbind is freely available at https://github.com/yangfengzhuguet/IKANBind.

Motivation: Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results: We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation: SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information: Supplementary data are available at Bioinformatics online.

We present a system for extracting citation data from Pubmed-indexed papers available online based on a knowledge-based algorithm. We achieve nearly 92% accuracy on a sample of 156 papers from 78 different journals. We describe the issues faced, our approach, the results achieved and the future directions of our work.

SUMMARY: The UniProtKB Sequence/Annotation Version database (UniSave) is a comprehensive archive of UniProtKB/Swiss-Prot and UniProtKB/TrEMBL entry versions. All changed Swiss-Prot and TrEMBL entries are loaded into the UniSave as part of the public bi-weekly UniProtKB releases. Unlike the UniProtKB, which contains only the latest Swiss-Prot and TrEMBL entry versions, the UniSave provides access to previous versions of these entries. AVAILABILITY: http://www.ebi.ac.uk/uniprot/unisave