scholarly journals Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling

2016 ◽  
Vol 44 (3) ◽  
pp. 937-943 ◽  
Author(s):  
Antonina Andreeva
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Large Scale ◽  
Structural Variations ◽  
Structural Classification ◽  
Alternative Structures ◽  
Genome Annotations ◽  
Structural Classification Of Proteins

The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling.

scholarly journals Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Romina Valdez ◽  
Khevin Roig ◽  
Diego P. Pinto-Roa ◽  
Jose Colbes
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Dimensional Structure ◽  
Structural Classification ◽  
Data Set ◽  
Contact Prediction

Protein structure prediction is one of the most important problems in Computational Biology; and consists of determining the 3D structure of a protein given its amino acid sequence. A key component that has allowed considerable improvements in recent decades is the prediction of contacts in a protein, since it provides fundamental information about its three-dimensional structure. In the 13th edition of the CASP (Critical Assessment of protein Structure Prediction), a notable progress has been evidenced for both problems with the use of deep learning algorithms. For the contact prediction category, the best methods in CASP13 achieved an average precision of 70%. In the present work, the performance of these methods is analyzed using a larger data set, with 483 proteins from four families according to the structural classification of the SCOP database (Structural Classification of Proteins). The selected methods were evaluated using the CASP metrics, and their results indicate an average contact prediction precision greater than 90%. SPOT-Contact was the method with the best overall performance, and one of the methods with the best performance for each SCOP class. The set of proteins used for the experiments and the implementations made for the analysis are publicly available.

LiveBench-2: Large-scale automated evaluation of protein structure prediction servers

2001 ◽  
Vol 45 (S5) ◽  
pp. 184-191 ◽  
Author(s):  
Janusz M. Bujnicki ◽  
Arne Elofsson ◽  
Daniel Fischer ◽  
Leszek Rychlewski
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Large Scale ◽  
Automated Evaluation

scholarly journals A large-scale experiment to assess protein structure prediction methods

1995 ◽  
Vol 23 (3) ◽  
pp. ii-iv ◽  
Author(s):  
John Moult ◽  
Jan T. Pedersen ◽  
Richard Judson ◽  
Krzysztof Fidelis
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Large Scale ◽  
Prediction Methods ◽  
Scale Experiment

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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