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Short linear motifs are 3 to 11 amino acid long peptide patterns that play important regulatory roles in modulating protein activities. Although they are abundant in proteins, it is often difficult to discover them by experiments, because of the low affinity binding and transient interaction of short linear motifs with their partners. Moreover, available computational methods cannot effectively predict short linear motifs, due to their short and degenerate nature. Here we developed a novel approach, FlexSLiM, for reliable discovery of short linear motifs in protein sequences. By testing on simulated data and benchmark experimental data, we demonstrated that FlexSLiM more effectively identifies short linear motifs than existing methods. We provide a general tool that will advance the understanding of short linear motifs, which will facilitate the research on protein targeting signals, protein post-translational modifications, and many others.
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Graph-Based Motif Discovery in Mimotope Profiles of Serum Antibody Repertoire
Phage display technique has a multitude of applications such as epitope mapping, organ targeting, therapeutic antibody engineering and vaccine design. One area of particular importance is the detection of cancers in early stages, where the discovery of binding motifs and epitopes is critical. While several techniques exist to characterize phages, Next Generation Sequencing (NGS) stands out for its ability to provide detailed insights into antibody binding sites on antigens. However, when dealing with NGS data, identifying regulatory motifs poses significant challenges. Existing methods often lack scalability for large datasets, rely on prior knowledge about the number of motifs, and exhibit low accuracy. In this paper, we present a novel approach for identifying regulatory motifs in NGS data. Our method leverages results from graph theory to overcome the limitations of existing techniques.
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- PAR ID:
- 10490255
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Lecture notes in bioinformatics
- ISSN:
- 2366-6323
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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