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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Stochastic Surface Walking Method for Structure Prediction and Pathway Searching.

Cheng Shang¹, Zhi-Pan Liu¹

¹Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Department of Chemistry, Key Laboratory of Computational Physical Science (Ministry of Education), Fudan University, Shanghai 200433, China.

Journal of Chemical Theory and Computation

|November 21, 2015

Summary

This summary is machine-generated.

We developed a new method for finding the lowest energy structure and reaction pathways in complex systems. This unbiased approach uses bias-potential-driven dynamics and Monte Carlo simulations to efficiently explore potential energy surfaces.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Area of Science:

Computational Chemistry
Materials Science
Chemical Physics

Background:

Accurate prediction of molecular structures and reaction pathways is crucial for understanding chemical systems.
Exploring complex potential energy surfaces (PES) presents a significant computational challenge due to high energy barriers and large dimensionality.

Purpose of the Study:

To introduce a novel, unbiased, general-purpose method for potential energy surface searching.
To enable efficient prediction of both global minimum structures and reaction pathways in complex systems.

Main Methods:

The proposed method integrates bias-potential-driven dynamics with Metropolis Monte Carlo simulations.
It allows for smooth perturbation of structural configurations while overcoming high energy barriers.
The approach is applied to locate the global minimum (GM) of Morse clusters and identify reaction pathways.

Main Results:

Successfully located the global minimum for short-ranged Morse clusters up to 103 atoms without prior system information.
Demonstrated the capability to identify complex reaction pathways in high-dimensional systems, exemplified by a nanohelix transformation (222 degrees of freedom).

Conclusions:

The developed method offers an unbiased and general approach for PES searching.
It effectively addresses both structure prediction and pathway identification challenges in complex chemical systems.