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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: Jan 19, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.

Wenchuan Wang¹, Robert Langlois², Marina Langlois²

¹SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.

Frontiers in Genetics

|September 24, 2019

Summary

This study introduces a novel multiple-instance learning algorithm for protein function prediction. The method accurately identifies functionally important residues without relying on homology, improving DNA and RNA binding protein annotation.

Keywords:

DNA binding proteins RNA binding proteins decision trees machine learning multiple-instance learning protein function annotation protein sequence and structural analysis semi supervised learning

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Last Updated: Jan 19, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Protein function annotation is crucial for understanding cellular processes.
Current methods often focus on whole proteins, limiting residue-level insights.
High-throughput computational methods are needed for efficient protein function characterization.

Purpose of the Study:

To develop a computational method for residue-level protein function prediction.
To identify functionally relevant residues without prior homology or residue-level annotation.
To apply multiple-instance learning for protein function annotation.

Main Methods:

Developed a novel multiple-instance learning algorithm based on AdaBoost.
Applied the algorithm to benchmark datasets for DNA-binding and RNA-binding proteins.
Evaluated performance against existing protein function prediction approaches.

Main Results:

The algorithm achieved high accuracy in annotating DNA-binding and RNA-binding proteins.
Successfully identified functionally relevant residues involved in molecular binding.
Demonstrated superior performance compared to previous methods on specific benchmarks.

Conclusions:

Multiple-instance learning offers a powerful framework for residue-level protein function prediction.
The developed algorithm enhances the accuracy of protein function annotation.
This approach facilitates a deeper understanding of protein mechanisms at the residue level.