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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

AlphaFold predicts novel human proteins with knots.

Agata P Perlinska¹, Wanda H Niemyska^1,2, Bartosz A Gren^1,3

¹Centre of New Technologies, University of Warsaw, Warsaw, Poland.

Protein Science : a Publication of the Protein Society

|March 24, 2023

Summary

Researchers discovered 51 knotted proteins in the human proteome using AlphaFold. This study, analyzing over 20,000 proteins, reveals new knot types and their potential folding complexity.

Keywords:

biological function evolution folding new knotted folds

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Area of Science:

Proteomics
Structural Biology
Computational Biology

Background:

Protein knotting, a rare phenomenon, has been known for decades but poorly characterized due to limited data.
Previous limitations in accessing complete proteomes hindered the assessment of knotted protein prevalence and significance.

Purpose of the Study:

To systematically identify and characterize knotted proteins within the entire human proteome.
To leverage advanced protein structure prediction tools to overcome previous data limitations.

Main Methods:

Analysis of over 20,000 human proteins predicted by AlphaFold.
Application of homolog search, clustering, quality assessment, and visual inspection for knot identification.
Classification of structures as knotted, potentially knotted, or artifacts.

Main Results:

Less than 2% of predicted human proteins contained knots.
51 credible knotted proteins were identified, representing 0.2% of the human proteome.
A novel, complex knot type (6_3) was discovered in potentially knotted structures, suggesting intricate folding pathways.

Conclusions:

Machine learning tools like AlphaFold enable comprehensive proteome-wide analysis of protein topology.
The human proteome contains a small but significant number of knotted proteins, including novel types.
The identified knot types provide insights into protein folding mechanisms and structural diversity.