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Updated: Jun 3, 2025

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

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function

Chenyue Zhang¹, Qinxin Wang², Yiyang Li¹

¹NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China.

|January 8, 2025

Summary

Multiple sequence alignment (MSA) is crucial for predicting molecular structures and functions, especially with AI advancements. This review details MSA

Keywords:

RNA deep learning function prediction multiple sequence alignment pairwise sequence alignment protein complex protein language model protein monomer protein structure prediction

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Multiple sequence alignment (MSA) is a cornerstone technique in biological sciences.
It is essential for predicting molecular structures and functions of proteins and nucleic acids.
MSAs are increasingly vital for artificial intelligence (AI) applications in biology.

Purpose of the Study:

To review the historical evolution and significance of MSA in molecular structure and function prediction.
To explore traditional and emerging AI-based methodologies for MSA.
To provide insights into MSA's evolving role in structural prediction research.

Main Methods:

Review of traditional MSA methodologies for protein monomers, complexes, and RNA.
Exploration of AI-based alternatives, including protein language models.
Analysis of strengths, limitations, and applications of various MSA approaches.

Main Results:

MSAs are foundational for both traditional sequence comparison and AI-driven structure prediction.
AI methods offer complementary or alternative approaches to traditional MSAs.
Accurate and efficient MSAs are critical for remote homology detection and spatial restraints in AI models.

Conclusions:

MSA remains indispensable for molecular structure and function prediction.
The integration of AI is transforming MSA applications and methodologies.
Researchers need to understand the evolving landscape of MSA tools for informed decision-making.