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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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PM-BioPred: A Web-Server for Prediction of Compound Bioactivity Against Plant and Microbial Proteins.

Sneha Murmu¹, Himanshushekhar Chaurasia², Soumya Sharma¹

¹ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.

Journal of Molecular Biology

|March 19, 2026

Summary

Researchers developed PM-BioPred, a machine learning tool predicting compound bioactivity against plant and microbial proteins. This aids agricultural biotechnology and antimicrobial drug discovery by screening potential agrochemicals and therapeutics.

Keywords:

PM-BioPred bioactivity compound-protein interaction machine learning pesticidal-activity

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Agricultural Biotechnology
Microbial Pathogenesis
Computational Chemistry

Background:

Protein-ligand interactions are crucial for plant physiology, immunity, and microbial pathogenicity.
Experimental methods for determining these interactions are time-consuming and limited.
Existing computational approaches often rely on restrictive modeling.

Purpose of the Study:

To develop accurate computational models for predicting compound bioactivity against plant and microbial proteins.
To create a user-friendly web server (PM-BioPred) for researchers to screen potential bioactive compounds.
To facilitate advancements in agricultural biotechnology and antimicrobial strategies.

Main Methods:

Curated a dataset of experimentally validated active and inactive compounds against plant and microbial targets.
Developed machine learning-based classification models to predict compound bioactivity.
Integrated models into a web-accessible prediction server, PM-BioPred.

Main Results:

Achieved 86% accuracy in predicting bioactivity for plant proteins.
Exceeded 90% accuracy in predicting bioactivity for microbial proteins.
PM-BioPred provides reliable predictions for compound-protein interactions.

Conclusions:

PM-BioPred is a valuable resource for early-stage screening of agrochemicals and antimicrobial candidates.
The platform supports research in plant-pathogen interactions and compound repurposing.
Machine learning models offer an efficient alternative to experimental methods for bioactivity prediction.