Theoretical enzyme design using the Kepler scientific workflows on the Grid

Files

1-s2.0-S1877050910001328-main.pdf (1.04 MB)

Links to Files

https://www.sciencedirect.com/science/article/pii/S1877050910001328

Permanent Link

https://doi.org/10.1016/j.procs.2010.04.131
 http://hdl.handle.net/11603/31604

Collections

UMBC Information Systems Department
UMBC Center for Accelerated Real Time Analysis
UMBC Center for Real-time Distributed Sensing and Autonomy
UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
UMBC Joint Center for Earth Systems Technology (JCET)

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-9933-1170

Date

2010-06-01

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Wang, Jianwu, Prakashan Korambath, Seonah Kim, Scott Johnson, Kejian Jin, Daniel Crawl, Ilkay Altintas, Shava Smallen, Bill Labate, and Kendall N. Houk. “Theoretical Enzyme Design Using the Kepler Scientific Workflows on the Grid.” Procedia Computer Science, ICCS 2010, 1, no. 1 (May 1, 2010): 1175–84. https://doi.org/10.1016/j.procs.2010.04.131.

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0 DEED)

Subjects

UMBC Big Data Analytics Lab

Abstract

One of the greatest challenges in computational chemistry is the design of enzymes to catalyze non-natural chemical reactions. We focus on harnessing the distributed parallel computational power of the Grid to automate the inside-out process of enzyme design using scientific workflow systems. This paper presents a scientific workflow based approach to facilitate the inside-out enzyme design process in the Grid execution environment by providing features such as resource consolidation, task parallelism, provenance tracking, fault tolerance and workflow reuse, which results in an automated, pipelined, efficient, extensible, stable, and easy-to-use computational process for enzyme design.