Modeling Side Chains in the Three-Dimensional Structure of Proteins for Post-Translational Modifications

Area of Science:

• Biochemistry and Structural Biology
• Computational Biology and Bioinformatics

Background:

• Post-translational modifications (PTMs) critically influence protein structure and function, yet their 3D structural understanding remains limited.
• The Protein Data Bank has a scarcity of 3D structures featuring PTMs, hindering detailed analysis.
• Current protein modeling tools primarily focus on unmodified proteins, neglecting PTMs.

Purpose of the Study:

• To develop and evaluate algorithms for predicting side-chain conformations in proteins with PTMs.
• To address the gap in computational tools for modeling 3D structures of modified proteins.
• To provide accurate modeling of nonstandard amino acid residues arising from PTMs.

Main Methods:

• Collected libraries of frequently observed PTMs from the Protein Data Bank (PDB).
• Implemented novel algorithms for predicting side-chain conformations at PTM sites and surrounding regions.
• Performed comprehensive analyses and comparisons with established software like Rosetta and FoldX.

Main Results:

• Developed algorithms for modeling side-chain conformations in proteins with PTMs.
• Demonstrated that the proposed solutions achieve performance comparable to Rosetta and FoldX.
• Created specialized libraries for common PTMs.

Conclusions:

• The developed algorithms provide a valuable tool for modeling 3D structures of proteins with PTMs.
• These methods show significant potential for future optimization and application in structural biology.
• The source code is publicly available on GitHub for further research and development.