FragIt: A Tool to Prepare Input Files for Fragment Based Quantum Chemical Calculations
arXiv:1205.5025 · doi:10.1371/journal.pone.0044480
Abstract
Near linear scaling fragment based quantum chemical calculations are becoming increasingly popular for treating large systems with high accuracy and is an active field of research. However, it remains difficult to set up these calculations without expert knowledge. To facilitate the use of such methods, software tools need to be available to support these methods and help to set up reasonable input files which will lower the barrier of entry for usage by non-experts. Previous tools relies on specific annotations in structure files for automatic and successful fragmentation such as residues in PDB files. We present a general fragmentation methodology and accompanying tools called FragIt to help setup these calculations. FragIt uses the SMARTS language to locate chemically appropriate fragments in large structures and is applicable to fragmentation of any molecular system given suitable SMARTS patterns. We present SMARTS patterns of fragmentation for proteins, DNA and polysaccharides, specifically for D-galactopyranose for use in cyclodextrins. FragIt is used to prepare input files for the Fragment Molecular Orbital method in the GAMESS program package, but can be extended to other computational methods easily.
arXiv comment27 pages, 9 figures
References in corpus (1)
Cited by in corpus (6)
- Mapping Enzymatic Catalysis using the Effective Fragment Molecular Orbital Method: Towards all ab initio Biochemistry
- Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding
- In silico prediction of mutant HIV-1 proteases cleaving a target sequence
- Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method
- A computational method for the systematic screening of reaction barriers in enzymes: Searching for Bacillus circulans xylanase mutants with greater activity towards a synthetic substrate
- High Througput pKa Prediction Using Semi Empirical Methods