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Related Experiment Video

Updated: Sep 14, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual

Keisuke Uchikawa¹, Kairi Furui¹, Masahito Ohue¹

¹Department of Computer Science, School of Computing, Institute of Science Tokyo, 4259 G3-56 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Kanagawa, Japan.

Biochemistry and Biophysics Reports

|July 21, 2025

Summary

This summary is machine-generated.

Computational virtual screening (VS) in drug discovery is improved by modifying AlphaFold2 protein structures. This approach enhances candidate selection by generating more accurate protein conformations for virtual screening.

Keywords:

AlphaFold2 Conformational changes Protein structures Structure-based drug design Structure-based virtual screening

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Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Structure-based virtual screening (VS) is crucial for drug discovery but often limited by the lack of experimental protein structures.
Advances like AlphaFold2 provide predicted structures, but direct use can yield suboptimal VS performance due to uncaptured ligand-induced conformational changes.

Purpose of the Study:

To develop a method for modifying AlphaFold2-predicted protein structures to improve their suitability for virtual screening.
To generate more accurate protein conformations that better represent ligand-induced transitions for enhanced drug discovery.

Main Methods:

Proposed a novel approach to explore and modify AlphaFold2-predicted protein structures.
Introduced alanine mutations in the ligand-binding site via multiple sequence alignment (MSA) alterations to induce conformational shifts.
Utilized iterative ligand docking simulations to guide structural exploration, optimizing mutation strategies with genetic algorithms or random search.

Main Results:

The proposed method successfully generated protein conformations more amenable to virtual screening.
A genetic algorithm optimization strategy significantly improved VS accuracy when sufficient active compounds were available.
A random search strategy proved more effective with limited active compound data.
The approach showed promise for targets yielding poor screening results with experimentally determined structures.

Conclusions:

Modified AlphaFold2-derived structures offer practical utility for enhancing virtual screening in drug discovery.
This method expands the application of computationally predicted protein models, particularly for targets with challenging structural data.
The findings highlight a viable strategy to overcome limitations of predicted protein structures in structure-based drug design.