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Acceleration of Binding Site Comparisons by Graph Partitioning.
1Department of Pharmaceutical Chemistry, Philipps-Universität, 35032 Marburg, Germany.
Molecular Informatics
|August 5, 2016
Summary
This study accelerates protein binding site comparison using a graph partitioning method. The new approach significantly speeds up analysis without losing accuracy, enabling faster large-scale database screening.
Area of Science:
- Computational chemistry
- Structural biology
- Bioinformatics
Background:
- Comparing protein binding sites is crucial in computational chemistry.
- Existing methods like Cavbase use graph-based approaches but are computationally intensive.
- Large-scale screening of databases is challenging due to computational demands.
Purpose of the Study:
- To develop a faster method for comparing protein binding sites.
- To enhance the efficiency of the Local Cliques (LC) method.
- To enable rapid screening of large compound databases for potential drug candidates.
Main Methods:
- Partitioning binding-site graphs into smaller, disjoint components based on physicochemical properties.
- Applying the Local Cliques (LC) method to these smaller graphs.
- Comparing the performance and accuracy against the original LC method.
Main Results:
- Achieved significant speed-up in binding site comparison.
- The partitioning approach maintained accuracy compared to the comprehensive method.
- Demonstrated the feasibility of large-scale database screening.
Conclusions:
- Graph partitioning is an effective strategy to accelerate binding site comparison.
- The enhanced LC method offers a computationally efficient solution for large-scale studies.
- This advancement facilitates faster identification of potential drug targets.
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