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Abstract Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM‐binding domains in the human proteome, the PDZ domain. These domains bind the extreme C‐terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developedMotifAnalyzer‐PDZ, a program that filters and compares all motif‐satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C‐terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, theMonsiga brevicollisSHANK1 PDZ domain (mbSHANK1), with endogenously‐relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif‐satisfying sequences from over a dozen organisms. Overall,MotifAnalyzer‐PDZis a versatile program to investigate potential PDZ interactions. This proof‐of‐concept work is poised to enable similar types of analyses for other SLiM‐binding domains (e.g.,MotifAnalyzer‐Kinase).MotifAnalyzer‐PDZis available athttp://motifAnalyzerPDZ.cs.wwu.edu.
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BinderSpace: A package for sequence space analyses for datasets of affinity‐selected oligonucleotides and peptide‐based molecules
Abstract Discovery of target‐binding molecules, such as aptamers and peptides, is usually performed with the use of high‐throughput experimental screening methods. These methods typically generate large datasets of sequences of target‐binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low‐throughput experiments or other approaches. Bioinformatics‐based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders.BinderSpaceis an open‐source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text‐based and visual output of motifs, can also provide heat maps of previously measured user‐defined functional properties for all the motif‐containing molecules. Users can also run principal component analysis (PCA) and t‐distributed stochastic neighbor embedding (t‐SNE) analyses on whole datasets and on motif‐related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t‐SNE maps. If points (sequences) in two‐dimensional maps in PCA or t‐SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use ofBinderSpaceon a dataset of oligonucleotides binding to single‐wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein.BinderSpaceis openly accessible to the public via the GitHub website:https://github.com/vukoviclab/BinderSpace.
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- Award ID(s):
- 2106587
- PAR ID:
- 10505124
- Publisher / Repository:
- J. Comput. Chem.
- Date Published:
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 44
- Issue:
- 22
- ISSN:
- 0192-8651
- Page Range / eLocation ID:
- 1836 to 1844
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/jcc.27130
