
@article {26283,
	title = {Characterizing the conformational landscape of MDM2-binding p53 peptides using Molecular Dynamics simulations.},
	journal = {Sci Rep},
	volume = {7},
	year = {2017},
	month = {2017 Nov 15},
	pages = {15600},
	abstract = {<p>The conformational landscapes of p53 peptide variants\&nbsp;and phage derived peptide (12/1) variants, all known to bind to MDM2, are studied using hamiltonian replica exchange molecular dynamics simulations. Complementing earlier observations, the current study suggests that the p53 peptides largely follow the {\textquoteright}conformational selection{\textquoteright} paradigm in their recognition of and complexation by MDM2 while the 12/1 peptides likely undergo some element of conformational selection but are mostly driven by {\textquoteright}binding induced folding{\textquoteright}. This hypothesis is further supported by pulling simulations that pull the peptides away from their bound states with MDM2. This data extends the earlier mechanisms proposed to rationalize the entropically driven binding of the p53 set and the enthalpically driven binding of the 12/1 set. Using our hypothesis, we suggest mutations to the 12/1 peptide that increase its helicity in simulations and may, in turn, shift the binding towards conformational selection. In summary, understanding the conformational landscapes of the MDM2-binding peptides may suggest new peptide designs with bespoke binding mechanisms.</p>
},
	issn = {2045-2322},
	doi = {10.1038/s41598-017-15930-4},
	author = {Yadahalli, Shilpa and Li, Jianguo and Lane, David P and Gosavi, Shachi and Verma, Chandra S}
}
@article {11636,
	title = {The Multifaceted Roles of Molecular Dynamics Simulations in Drug Discovery.},
	journal = {Curr Pharm Des},
	volume = {22},
	year = {2016},
	month = {2016},
	pages = {3585-600},
	abstract = {<p>Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily, combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In silico methods are playing key roles in the discovery of a growing number of marketed drugs. The use of computational approaches, particularly molecular dynamics, in drug design is rapidly gaining momentum and acceptance as an essential part of the toolkit for modern drug discovery. From analysing atomistic details for explaining experimentally observed phenomena, to designing drugs with increased efficacy and specificity, the insight that such simulations can provide is generating new ideas and applications that have previously been unexplored. Here we discuss physics-based simulation methodologies and applications in drug design: from locating pockets to designing novel lead compounds, from small molecules to peptides. With developments in hardware, software and theory, the improved predictive abilities of in silico efforts are becoming an essential part of efficient, economic and accurate drug development strategies.</p>
},
	issn = {1873-4286},
	author = {Fox, Stephen John and Li, Jianguo and Sing Tan, Yaw and Nguyen, Minh N and Pal, Arumay and Ouaray, Zohra and Yadahalli, Shilpa and Kannan, Srinivasaraghavan}
}
