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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

Recent Progress of Protein Tertiary Structure Prediction.

Qiqige Wuyun¹, Yihan Chen², Yifeng Shen³

¹Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

Molecules (Basel, Switzerland)

|February 24, 2024

Summary

Artificial intelligence (AI) significantly advances protein structure prediction. Deep learning methods like AlphaFold2 achieve high accuracy, guiding researchers in selecting optimal protein structure prediction tools.

Keywords:

AlphaFold2 contact map deep learning distance map end-to-end methods multi-domain proteins protein language model protein tertiary structure prediction template-based modeling template-free modeling

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Area of Science:

Computational and structural bioinformatics
Artificial intelligence in bioinformatics

Background:

Predicting 3D protein structure from amino acid sequences is a long-standing challenge.
Artificial intelligence (AI) has recently accelerated progress in protein structure prediction.

Purpose of the Study:

To review protein structure prediction methodologies, assessments, and databases.
To guide researchers in understanding and selecting appropriate prediction methods.

Main Methods:

Overview of traditional methods: template-based modeling (TBM) and template-free modeling (FM).
Description of deep learning approaches: contact/distance-guided, end-to-end folding, and protein language model (PLM)-based methods.
Inclusion of multi-domain prediction, CASP assessments, and the AlphaFold Protein Structure Database.

Main Results:

AlphaFold2 demonstrates high performance, comparable to experimental structures (CASP14).
Various methods offer different advantages, disadvantages, and application scopes.

Conclusions:

AI, particularly deep learning, has revolutionized protein structure prediction.
Understanding method limitations and contexts is crucial for effective application in protein-related research.