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Conserved Binding Sites

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

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Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence

Wayland Yeung¹, Zhongliang Zhou², Sheng Li³

¹Institute of Bioinformatics, University of Georgia, 30602, Georgia, USA.

Briefings in Bioinformatics

|January 11, 2023

Summary

We developed a novel, alignment-free method using protein language models to estimate sequence conservation for predicting functional sites. ESM2 models offer the best performance for this task, enabling efficient analysis of full-length proteins.

Keywords:

deep learning functional site prediction protein language models sequence conservation

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Bioinformatics
Computational Biology
Deep Learning

Background:

Protein language models (PLMs) are emerging deep learning tools in bioinformatics.
Applications include protein structure prediction and design.
Systematic exploration of PLMs for sequence conservation estimation and functional site prediction is lacking.

Purpose of the Study:

To present a novel method for alignment-free sequence conservation estimation using PLM-generated embeddings.
To evaluate the performance of different PLMs for this task.
To demonstrate the applicability of the method for functional site prediction in full-length proteins.

Main Methods:

Utilized sequence embeddings from protein language models for conservation estimation.
Performed comprehensive benchmarks across various publicly available PLMs.
Evaluated performance based on accuracy and computational cost.
Applied the method to full-length protein sequences, including multidomain proteins and fast-evolving regions.

Main Results:

ESM2 models demonstrated the optimal balance between performance and computational cost for conservation estimation.
The embedding-based method is robust to the order of conserved elements, allowing single-run analysis of multidomain proteins.
Identified conserved functional sites, such as phosphorylation motifs, within variable domain insert regions of protein kinases.

Conclusions:

Embedding-based conservation analysis provides a broadly applicable, alignment-free approach for identifying potential functional sites in any full-length protein sequence.
This method enhances the utility of protein language models in bioinformatics research.
The developed scripts are publicly available for practical application.