LPIcom: Prediction of interaction between ligand and protein

Welcome to the official documentation for LPIcom, a comprehensive web server designed to facilitate the understanding of protein-ligand interactions. As understanding how proteins interact with a wide range of ligands is a major challenge in systems biology, LPIcom provides tools to analyze, compare, and predict interacting residues for over 800 different ligands. Web Server: http://crdd.osdd.net/raghava/lpicom/ Citation Singh, H., Srivastava, H. K., & Raghava, G. P. S. (2016). A web server for analysis, comparison and prediction of protein ligand binding sites. Biology Direct, 11, 14. https://doi.org/10.1186/s13062-016-0118-5 GitHub:-https://github.com/Manish-IIITD-repository/LPIcom About the Platform LPIcom was developed to bridge the gap in ligand-binding site prediction, which has traditionally been limited to a small number of well-studied ligands. By utilizing data from the Protein Data Bank (PDB), LPIcom offers insights into the binding preferences and structural motifs of 824 unique ligands. Database Criteria Ligand Inclusion: Every ligand included in the server has at least 30 experimentally determined protein-binding sites in the PDB. Binding Site Definition: Interacting residues are defined based on a distance-based cutoff between the protein atoms and the ligand atoms. Key Modules 1. Analysis Module This module allows users to identify residue preferences and binding motifs for a specific ligand. Residue Propensity: Calculates which amino acids are frequently involved in binding (e.g., Glycine, Lysine, and Arginine are preferred in ATP binding sites). Motif Discovery: Identifies conserved sequence patterns that characterize the binding site of a given ligand. 2. Comparison Module This module enables the comparison of binding sites across multiple ligands to identify similarities in their interaction profiles. Clustering: Groups ligands based on the similarity of their interacting residues. Cross-Ligand Insights: Useful for discovering why certain ligands (like ATP, ADP, and GTP) share similar binding environments. 3. Prediction Module A propensity-based method to predict potential ligand-binding residues within a protein sequence or structure. High-Throughput Screening: Allows for the rapid identification of potential interaction sites for hundreds of ligands. Technical Overview LPIcom utilizes a robust computational framework to process and visualize complex interaction data. Data Source: Derived from the PDB using the CCPDB (Compilation and Creation of data sets from Protein Data Bank) tool. Metrics: Uses standard propensity scores and residue conservation metrics to define binding signatures. Visualizations: Integrated tools for viewing sequence logos and interaction clusters. Applications Drug Discovery: Identifying potential off-target interactions by comparing binding site similarities across different drug classes. Protein Engineering: Designing or modifying binding sites to alter ligand specificity. Functional Annotation: Predicting the function of uncharacterized proteins by identifying their potential ligand-binding partners. Contact & Authors Prof. Gajendra P. S. Raghava Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi), New Delhi, India. Email: [e-mail] / [e-mail] License This resource is open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License, permitting unrestricted use and distribution provided the original work is properly credited.