Protein is essential for almost every biological process and the interaction between proteins and their interacting partners play critical roles in the functioning cells. Since mutations that disrupt the protein-protein interactions result in many diseases, it is important to understand the biochemical mechanisms of protein recognition, which is important for deciphering protein interaction network and designing potent drugs with high specificity against protein targets. Our long term goal is to develop theoretical models for describing protein binding specificity and reliably predict protein-protein interactions. In the proposed project, we have the following specific aims.
Aim 1, we will develop a computational method that combines computer modeling and bioinformatics analysis to characterize the interaction interface between modular domains and their peptide ligands. We will test this method on several modular domains including SH3, SH2 and PDZ domains that bind to specific peptide sequences.
Aim 2, we will systematically predict interacting peptides in the yeast genome of all yeast SH3 domains.
Aim 3, we will experimentally validate the predictions in vitro to assess the performance of the computational method. We will also conduct in vivo experiments to examine the biological significance of a set of selected domain-peptide interactions.

Public Health Relevance

Protein-protein interactions play critical roles in the cell and mutations that disrupt these interactions result in many diseases. It is therefore important to understand the biochemical mechanisms of protein recognition.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
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University of California San Diego
Schools of Arts and Sciences
La Jolla
United States
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Li, Nan; Ainsworth, Richard I; Ding, Bo et al. (2015) Using Hierarchical Virtual Screening To Combat Drug Resistance of the HIV-1 Protease. J Chem Inf Model 55:1400-12
Ding, Bo; Li, Nan; Wang, Wei (2013) Characterizing binding of small molecules. II. Evaluating the potency of small molecules to combat resistance based on docking structures. J Chem Inf Model 53:1213-22
Li, Nan; Stein, Richard S L; He, Wei et al. (2013) Identification of methyllysine peptides binding to chromobox protein homolog 6 chromodomain in the human proteome. Mol Cell Proteomics 12:2750-60
Ding, Bo; Wang, Jian; Li, Nan et al. (2013) Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening. J Chem Inf Model 53:114-22
Stein, Richard S L; Li, Nan; He, Wei et al. (2013) Recognition of methylated peptides by Drosophila melanogaster polycomb chromodomain. J Proteome Res 12:1467-77
Xu, Zheng; Hou, Tingjun; Li, Nan et al. (2012) Proteome-wide detection of Abl1 SH3-binding peptides by integrating computational prediction and peptide microarray. Mol Cell Proteomics 11:O111.010389
Hou, Tingjun; Li, Nan; Li, Youyong et al. (2012) Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models. J Proteome Res 11:2982-95
Stein, Richard S L; Wang, Wei (2011) The recognition specificity of the CHD1 chromodomain with modified histone H3 peptides. J Mol Biol 406:527-41
Hou, Tingjun; Wang, Junmei; Li, Youyong et al. (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69-82
Hou, Tingjun; Wang, Junmei; Li, Youyong et al. (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32:866-77

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