Three-dimensional protein structure prediction: Methods and computational strategies.

Area of Science:

• Structural Bioinformatics
• Computational Biology
• Biophysics

Background:

• Determining a protein's three-dimensional (3-D) structure from its amino acid sequence is a fundamental challenge in structural bioinformatics.
• Numerous computational methodologies have been developed to address the 3-D Protein Structure Prediction (3-D-PSP) problem.

Approach:

• This work reviews and categorizes existing computational methods for 3-D protein structure prediction.
• Methods are classified into four main groups: first principles (with or without database information), fold recognition/threading, and comparative modeling/sequence alignment.
• Commonly employed computational techniques include deterministic algorithms, optimization, data mining, and machine learning.

Key Points:

• The review provides a comprehensive overview of current computational strategies for 3-D protein prediction.
• Understanding these diverse approaches is crucial for advancing protein structure determination.
• The integration of various computational techniques drives progress in the field.

Conclusions:

• This review consolidates knowledge on 3-D Protein Structure Prediction (3-D-PSP) methodologies.
• It highlights the importance of computational approaches in solving the protein structure puzzle.
• Future advancements will likely build upon the reviewed methods and strategies.