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Aditya Unnithan

dblp:67/3724 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein structure prediction
protein folding
0.112006
iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006
Bioinformatics and computational biology › protein structure prediction › protein folding
protein folding simulation
0.112006
iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006
Bioinformatics and computational biology
protein structure prediction
0.112006
iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006
Bioinformatics and computational biology
thermodynamic analysis
0.112006
iFold: a platform for interactive folding simulations of proteins · Bioinform. 2006

Methods — techniques the papers use, named apart from their topics

gō-interactions · 0.1discrete molecular dynamics · 0.1coarse-grained model · 0.1
YearPublicationVenuePosition
2006 iFold: a platform for interactive folding simulations of proteins
abstract
UNLABELLED: We built a novel web-based platform for performing discrete molecular dynamics simulations of proteins. In silico protein folding involves searching for minimal frustration in the vast conformational landscape. Conventional approaches for simulating protein folding insufficiently address the problem of simulations in relevant time and length scales necessary for a mechanistic understanding of underlying biomolecular phenomena. Discrete molecular dynamics (DMD) offers an opportunity to bridge the size and timescale gaps and uncover the structural and biological properties of experimentally undetectable protein dynamics. The iFold server supports large-scale simulations of protein folding, thermal denaturation, thermodynamic scan, simulated annealing and p(fold) analysis using DMD and coarse-grained protein model with structure-based Gō-interactions between amino acids. AVAILABILITY: http://ifold.dokhlab.org
Shantanu Sharma 0002, Feng Ding 0002, Huifen Nie, Daniel Watson, Aditya Unnithan, Jameson Lopp, Diane Pozefsky, Nikolay V. Dokholyan
Bioinform.5