dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains. Author Anat Etzion-Fuchs, David Todd, Mona Singh Publication Year 2021 Type Journal Article Abstract Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT's predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http://protdomain.princeton.edu/dsprint. Keywords Humans, Binding Sites, Ligands, DNA, RNA, Ions, Peptides, Protein Domains, Machine Learning Journal Nucleic Acids Res Volume 49 Issue 13 Pages e78 Date Published 2021 Jul 21 ISSN Number 1362-4962 DOI 10.1093/nar/gkab356 Alternate Journal Nucleic Acids Res PMCID PMC8287948 PMID 33999210 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML