scholarly journals ArchPRED: a template based loop structure prediction server

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W173-W176 ◽  
Author(s):  
N. Fernandez-Fuentes ◽  
J. Zhai ◽  
A. Fiser
Keyword(s):  
Structure Prediction ◽  
Loop Structure ◽  
Prediction Server

Protein Loop Structure Prediction Using Conformational Space Annealing

2017 ◽  
Vol 57 (5) ◽  
pp. 1068-1078 ◽  
Author(s):  
Seungryong Heo ◽  
Juyong Lee ◽  
Keehyoung Joo ◽  
Hang-Cheol Shin ◽  
Jooyoung Lee
Keyword(s):  
Structure Prediction ◽  
Conformational Space ◽  
Loop Structure

Protein Loop Structure Prediction Methods

2008 ◽  
pp. 3100-3105
Author(s):  
Martin Mönnigmann ◽  
Christodoulos A. Floudas
Keyword(s):  
Structure Prediction ◽  
Prediction Methods ◽  
Loop Structure

Smotifs as structural local descriptors of supersecondary elements: classification, completeness and applications

2014 ◽  
Vol 10 (4) ◽  
Author(s):  
Jaume Bonet ◽  
Andras Fiser ◽  
Baldo Oliva ◽  
Narcis Fernandez-Fuentes
Keyword(s):  
Protein Design ◽  
Structure Prediction ◽  
Protein Structures ◽  
Regular Structure ◽  
Loop Structure ◽  
Apparent Lack ◽  
Knowledge Based ◽  
Limits Of Knowledge ◽  
Folding Dynamics ◽  
And Function

AbstractProtein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.

scholarly journals The Jpred 3 secondary structure prediction server

2008 ◽  
Vol 36 (Web Server) ◽  
pp. W197-W201 ◽  
Author(s):  
C. Cole ◽  
J. D. Barber ◽  
G. J. Barton
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Prediction Server

scholarly journals (PS)2: protein structure prediction server version 3.0

2015 ◽  
Vol 43 (W1) ◽  
pp. W338-W342 ◽  
Author(s):  
Tsun-Tsao Huang ◽  
Jenn-Kang Hwang ◽  
Chu-Huang Chen ◽  
Chih-Sheng Chu ◽  
Chi-Wen Lee ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Server

PREDICTION OF LOOP REGIONS IN PROTEIN SEQUENCE

2008 ◽  
Vol 06 (05) ◽  
pp. 1035-1047 ◽  
Author(s):  
NIKITA V. DOVIDCHENKO ◽  
NATALYA S. BOGATYREVA ◽  
OXANA V. GALZITSKAYA
Keyword(s):  
Protein Sequence ◽  
Structure Prediction ◽  
Degrees Of Freedom ◽  
Window Size ◽  
Antigenic Determinants ◽  
Protein Chain ◽  
Homologous Proteins ◽  
Suggested Approach ◽  
Prediction Server ◽  
Loop Regions

We suggest an algorithm that inputs a protein sequence and outputs a decomposition of the protein chain into a regular part including secondary structures and a nonregular part corresponding to loop regions. We have analyzed loop regions in a protein dataset of 3,769 globular domains and defined the optimal parameters for this prediction: the threshold between regular and nonregular regions and the optimal window size for averaging procedures using the scale of the expected number of contacts in a globular state and entropy scale as the number of degrees of freedom for the angles φ, ψ, and χ for each amino acid. Comparison with known methods demonstrates that our method gives the same results as the well-known ALB method based on physical properties of amino acids (the percentage of true predictions is 64% against 66%), and worse prediction for regular and nonregular regions than PSIPRED (Protein Structure Prediction Server) without alignment of homologous proteins (the percentage of true predictions is 73%). The potential advantage of the suggested approach is that the predicted set of loops can be used to find patterns of rigid and flexible loops as possible candidates to play a structure/function role as well as a role of antigenic determinants.

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