Abstract
Algorithms for discrete optimization of proteins play a central role in recent advances in protein structure prediction and design. We wish to improve the resources available for computational biologists to rapidly prototype such algorithms and to easily scale these algorithms to many processors. To that end, we describe the implementation and use of two new open source resources, citing potential benefits over existing software. We discuss CHOMP, a new object-oriented library for macromolecular optimization, and SHARPEN, a framework for scaling CHOMP scripts to many computers. These tools allow users to develop new algorithms for a variety of applications including protein repacking, protein-protein docking, loop rebuilding, or homology model remediation. Particular care was taken to allow modular energy function design; protein conformations may currently be scored using either the OPLSaa molecular mechanical energy function or an all-atom semiempirical energy function employed by Rosetta. © 2009 Wiley Periodicals, Inc.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 999-1005 |
| Number of pages | 7 |
| Journal | Journal of Computational Chemistry |
| Volume | 30 |
| Issue number | 6 |
| DOIs | |
| State | Published - Apr 30 2009 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: The authors thank Frances H. Arnold for supporting the project: the Jane Coffin Childs foundation and KAUST for postdoctoral support of C.D.S: Mani Chandy for teaching the Distributed Systems course: Jason Paryani for porting the CCD code-, Rarn Kimdasamy for implementing amino acid variants: Phillip Romero and Ben Allen for helpful discussions: Vijay Pande for access to the Folding@Home code: and David Baker for access to the Rosetta code.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.