
@article {55790,
	title = {Go-Kit: A Tool To Enable Energy Landscape Exploration of Proteins.},
	journal = {J Chem Inf Model},
	year = {2019},
	month = {2019 Apr 12},
	abstract = {<p>Coarse-grained Go̅-like models, based on the principle of minimal frustration, provide valuable insight into fundamental questions in the field of protein folding and dynamics. In conjunction with commonly used molecular dynamics (MD) simulations, energy landscape exploration methods like discrete path sampling (DPS) with Go̅-like models can provide quantitative details of the thermodynamics and kinetics of proteins. Here we present Go-kit, a software that facilitates the setup of MD and DPS simulations of several flavors of Go̅-like models. Go-kit is designed for use with MD (GROMACS) and DPS (PATHSAMPLE) simulation engines that are open source. The Go-kit code is written in python2.7 and is also open source. A case study for the ribosomal protein S6 is discussed to illustrate the utility of the software, which is available at https://github.com/gokit1/gokit .</p>
},
	issn = {1549-960X},
	doi = {10.1021/acs.jcim.9b00007},
	author = {Neelamraju, Sridhar and Wales, David J and Gosavi, Shachi}
}
