المؤلفون: Jee Eun Kang, Ji Hae Jun, Jung Hyun Kwon, Ju-Hyun Lee, Kidong Hwang, Sungjong Kim, Namhee Jeong

المصدر: Genes; Volume 14; Issue 7; Pages: 1476

مصطلحات موضوعية: liquid–liquid phase separation, protein oligomerization, GO, domain–domain interaction, domain linker, intrinsically disordered regions, domain–peptide interaction, beta sheet, transmembrane helices, post-translational modification

جغرافية الموضوع: agris

الوصف: Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool–liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein–nucleic acid interaction (PNI) and protein–protein interaction (PPI) analysis based on domain–domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain–motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms.

وصف الملف: application/pdf

العلاقة: Bioinformatics; https://dx.doi.org/10.3390/genes14071476Test

الإتاحة: https://doi.org/10.3390/genes14071476Test

المؤلفون: Qian Liu, Jing Lin, Li Wen, Shaozhou Wang, Peng Zhou, Li Mei, Shuyong Shang

المصدر: Frontiers in Genetics, Vol 12 (2022)

مصطلحات موضوعية: computational peptidology, peptide quantitative structure-activity relationship, domain-peptide interaction, amino acid descriptor, statistical modeling, machine learning, Genetics, QH426-470

الوصف: The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The Rprd2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fgene.2021.800857/fullTest; https://doaj.org/toc/1664-8021Test

الوصول الحر: https://doaj.org/article/8f69f325eac0460b9b40f18acba29943Test

المؤلفون: Shao, Xiaojian, Tan, Chris S. H., Voss, Courtney, Li, Shawn S. C., Deng, Naiyang, Bader, Gary D.

المصدر: Biochemistry Publications

مصطلحات موضوعية: Regression framework, Quantitative prediction, PDZ domain–peptide interaction, Biochemistry

الوصف: MOTIVATION: Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders. RESULTS: We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. AVAILABILITY AND IMPLEMENTATION: The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinityTest.

العلاقة: https://ir.lib.uwo.ca/biochempub/143Test; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031032Test/

الإتاحة: https://doi.org/10.1093/bioinformatics/btq657Test
https://ir.lib.uwo.ca/biochempub/143Test
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031032Test/