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Updated: Oct 20, 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

Improved 3-D Protein Structure Predictions using Deep ResNet Model.

S Geethu¹, E R Vimina²

¹Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, Ernakulam, India. geethus@asas.kh.students.amrita.edu.

The Protein Journal

|September 12, 2021

Summary

This summary is machine-generated.

A new deep ResNet method improves protein structure prediction by generating homologous sequences for feature input. This novel approach outperforms existing methods like AlphaFold in accuracy for over half of the tested protein sequences.

Keywords:

3-D protein structure prediction CASP Deep ResNet Architecture Distance prediction Experimental and Computational techniques Protein

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Area of Science:

Computational Biology
Structural Bioinformatics
Deep Learning

Background:

Protein Structure Prediction (PSP) remains a significant challenge in computational biology.
While co-evolutionary methods have advanced PSP, deep learning approaches are increasingly integrated for improved performance.

Purpose of the Study:

To propose a novel deep ResNet-based methodology for predicting inter-residue distances and dihedral angles.
To enhance protein structure prediction accuracy using generated homologous sequences and deep learning.

Main Methods:

A deep ResNet architecture was employed to predict inter-residue distance and dihedral angles.
125 homologous sequences were generated on average from a customized database to create input features.
A pool of protein structures was generated, with the lowest potential energy structure selected as the final prediction.

Main Results:

The proposed method demonstrated superior performance for 52% of target sequences compared to AlphaFold (10%), Zhang (22.9%), and RaptorX (6%).
The model achieved a Template Modeling (TM) score of 0.69, outperforming AlphaFold (0.67), Zhang (0.65), and RaptorX (0.58).
Accuracy greater than or equal to 0.80 was achieved for 37.5% of the target sequences.

Conclusions:

The novel deep ResNet methodology offers a significant advancement in protein structure prediction.
The integration of generated homologous sequences and deep learning enhances prediction accuracy and reliability.