Dataset Information

Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases.

ABSTRACT: PDZ (postsynaptic density (PSD95), discs large (Dlg), and zonula occludens (ZO-1)-dependent interactions are widely distributed within different cell types and regulate a variety of cellular processes. To date, some of these interactions have been identified as targets of small molecules or peptides, mainly related to central nervous system disorders and cancer. Recently, the knowledge of PDZ proteins and their interactions has been extended to various cell types of the immune system, suggesting that their targeting by viral pathogens may constitute an immune evasion mechanism that favors viral replication and dissemination. Thus, the pharmacological modulation of these interactions, either with small molecules or peptides, could help in the control of some immune-related diseases. Deeper structural and functional knowledge of this kind of protein-protein interactions, especially in immune cells, will uncover novel pharmacological targets for a diversity of clinical conditions.

SUBMITTER: Gutierrez-Gonzalez LH

PROVIDER: S-EPMC8588348 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases.

Gutiérrez-González Luis H LH Rivas-Fuentes Selma S Guzmán-Beltrán Silvia S Flores-Flores Angélica A Rosas-García Jorge J Santos-Mendoza Teresa T

Molecules (Basel, Switzerland) 20211021 21

PDZ (postsynaptic density (PSD95), discs large (Dlg), and zonula occludens (ZO-1)-dependent interactions are widely distributed within different cell types and regulate a variety of cellular processes. To date, some of these interactions have been identified as targets of small molecules or peptides, mainly related to central nervous system disorders and cancer. Recently, the knowledge of PDZ proteins and their interactions has been extended to various cell types of the immune system, suggesting ...[more]

PMID: 34770776

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Project description:BACKGROUND: PDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques (e.g. protein microarray screen, phage display) have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage. RESULTS: Here, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches. CONCLUSIONS: Our approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.

Dataset Information

Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases.

Publications

Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases.

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