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Application and Comprehensive Analysis of Neighbor Approximated Information Theoretic Configurational Entropy Methods
Shailesh Kumar Panday1, Indira Ghosh1
1School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
Journal of Chemical Theory and Computation
|November 16, 2020
Summary
A new method, Neighbor Approximated Maximum Information Spanning Tree (A-MIST), accurately estimates binding configurational entropy in proteins. This approach offers computational advantages over existing methods, improving efficiency for biophysical process studies.
Area of Science:
- Thermodynamics and Biophysical Chemistry
- Computational Biology and Cheminformatics
Background:
- Binding entropy is crucial for understanding biophysical processes, but its accurate estimation is challenging due to sampling limitations and complex atomic fluctuations.
- Configurational entropy is a major component of binding entropy, necessitating reliable methods for its calculation.
Purpose of the Study:
- To introduce and evaluate the Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for estimating conformational entropy.
- To compare the performance and computational efficiency of A-MIST against conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) methods.
- To identify key structural regions contributing to binding configurational entropy in protein-ligand complexes.
Main Methods:
- Development and application of the Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method.
- Performance evaluation using two protein-ligand binding cases: indirubin-5-sulfonate to PfPK5 and RON2-peptide to PfAMA1.
- Comparative analysis of four entropy estimators (ML, MM, CS, JS) and two discretization schemes for Degrees of Freedom (DFs).
- Implementation in a parallel C++11 code with a Python package for data preprocessing and analysis.
Main Results:
- The Neighbor Approximated Mutual Information Expansion (A-MIE) method demonstrates improved convergence and computational efficiency over MIE.
- A-MIST provides binding entropy estimates comparable to MIST but with significantly reduced computational cost (20-30%).
- Unlike A-MIE/MIE, A-MIST/MIST methods are insensitive to the choice of root atoms, graph search algorithm, and entropy estimator.
Conclusions:
- A-MIST offers a computationally advantageous and robust method for estimating binding configurational entropy.
- The developed A-MIST method and associated software tools enhance the study of biophysical processes involving protein-ligand interactions.
- Accurate estimation of binding entropy is critical for drug discovery and understanding molecular recognition mechanisms.
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