Feng Ding

Dr. Feng Ding

Faculty Scholar, CU School of Health Research, Clemson University, Clemson, South Carolina
orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 September 2010 in Proteins: Structure, Function, and Bioinformatics
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Journal Article
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SRSrinivas Ramachandran
Srinivas Ramachandran
PKPradeep Kota
Pradeep Kota
FDFeng Ding
Feng Ding
NDNikolay V Dokholyan
Nikolay V Dokholyan

1 June 2007 in Nature Methods
Verified
Journal Article
Closed access
SYShuangye Yin
Shuangye Yin
FDFeng Ding
Feng Ding
NDNikolay V Dokholyan
Nikolay V Dokholyan

23 February 2012 in RNA
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Journal Article
Open access
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JCJosé Almeida Cruz
José Almeida Cruz
MBMarc-Frédérick Blanchet
Marc-Frédérick Blanchet
MBMichal Boniecki
Michal Boniecki
JBJanusz M. Bujnicki
Janusz M. Bujnicki
SCShi-Jie Chen
Shi-Jie Chen
EWEric Westhof
Eric Westhof
We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

12 July 2017 in Chemical Society Reviews
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Journal Article
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PKPu Chun Ke
Pu Chun Ke
Marc-antoine SaniMarc-antoine Sani
Marc-antoine Sani
FDFeng Ding
Feng Ding
AKAleksandr Kakinen
Aleksandr Kakinen
IJIbrahim Javed
Ibrahim Javed
RMRaffaele Mezzenga
Raffaele Mezzenga
We highlight the role of molecular self-assembly in eliciting the mesoscopic and pathological properties of amyloid proteins. This knowledge is pivotal for the development of theranostics against amyloid diseases.

1 July 2008 in Structure
Verified
Journal Article
Closed access
FDFeng Ding
Feng Ding
DTDouglas Tsao
Douglas Tsao
HNHuifen Nie
Huifen Nie
NDNikolay V Dokholyan
Nikolay V Dokholyan

25 June 2002 in Proceedings of the National Academy of Sciences
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Journal Article
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NDNikolay V Dokholyan
Nikolay V Dokholyan
LLLewyn Li
Lewyn Li
FDFeng Ding
Feng Ding
ESEugene I. Shakhnovich
Eugene I. Shakhnovich
The folding of many small proteins is kinetically a two-state process that represents overcoming the major free-energy barrier. A kinetic characteristic of a conformation, its probability to descend to the native state domain in the amount of time that represents a small fraction of total folding time, has been introduced to determine to which side of the free-energy barrier a conformation belongs. However, which features make a protein conformation on the folding pathway become committed to rapidly descending to the native state has been a mystery. Using two small, well characterized proteins, CI2 and C-Src SH3, we show how topological properties of protein conformations determine their kinetic ability to fold. We use a macroscopic measure of the protein contact network topology, the average graph connectivity , by constructing graphs that are based on the geometry of protein conformations. We find that the average connectivity is higher for conformations with a high folding probability than for those with a high probability to unfold. Other macroscopic measures of protein structural and energetic properties such as radius of gyration, rms distance, solvent-accessible surface area, contact order, and potential energy fail to serve as predictors of the probability of a given conformation to fold.