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Updated: Jan 21, 2026

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

Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models.

Stephanie Maria Linker^1,2, Aniket Magarkar¹, Jürgen Köfinger²

¹Department of Medicinal Chemistry , Boehringer Ingelheim Pharma , Birkendorfer Straße 65 , 88397 Biberach an der Riß , Germany.

Journal of Chemical Theory and Computation

|August 13, 2019

Summary

This study presents a computational method to predict small-molecule binding sites and modes for drug discovery. It overcomes experimental limitations in fragment-based screening (FBS) by using apoprotein and ligand structures.

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An In Vitro Caseum Binding Assay that Predicts Drug Penetration in Tuberculosis Lesions

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Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Predicting protein-ligand costructures is crucial but challenging in drug discovery.
Experimental methods like X-ray crystallography are often bottlenecks for fragment-based screening (FBS).
Many pharmaceutical targets lack feasible experimental structural determination methods.

Purpose of the Study:

To develop a computational method for predicting binding sites and modes of small molecules.
To enable reliable prediction using only apoprotein and ligand structures.
To address limitations in experimental structure determination for fragment-based drug discovery.

Main Methods:

Utilized molecular dynamics simulations.
Employed Markov-state models.
Developed a fully automated computational protocol.

Main Results:

Successfully predicted binding sites and binding modes for fragment-like molecules.
Demonstrated reliability across diverse target classes and fragments.
Showcased the method's applicability to historical fragment-based screening data.

Conclusions:

The computational method reliably predicts protein-ligand binding sites and modes.
This approach can accelerate fragment-based drug discovery by overcoming experimental bottlenecks.
The protocol offers a valuable tool for drug discovery workflows requiring minimal human intervention.