scholarly journals From local structure to a global framework: recognition of protein folds

2014 ◽  
Vol 11 (95) ◽  
pp. 20131147 ◽  
Author(s):  
Agnel Praveen Joseph ◽  
Alexandre G. de Brevern
Keyword(s):  
Local Structure ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Fold Recognition ◽  
Protein Fold ◽  
Major Area ◽  
Huge Amount ◽  
Protein Fold Recognition ◽  
Structural Environment

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

scholarly journals FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network

2020 ◽  
Author(s):  
Jiangyi Shao ◽  
Ke Yan ◽  
Bin Liu
Keyword(s):  
Language Processing ◽  
Cluster Model ◽  
Learning To Rank ◽  
Protein Structures ◽  
Fold Recognition ◽  
Protein Fold ◽  
Retrieval Task ◽  
Similarity Network ◽  
Protein Fold Recognition ◽  
Homology Relationship

Abstract As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.

NONSYMMETRIC TWO-BODY SCORE FUNCTION FOR PROTEIN FOLD RECOGNITION: NEXT NEAREST NEIGHBOR-ADJACENCY OF TWO AMINO ACIDS

2004 ◽  
Vol 15 (08) ◽  
pp. 1087-1094
Author(s):  
MUYOUNG HEO ◽  
MOOKYUNG CHEON ◽  
IKSOO CHANG
Keyword(s):  
Amino Acids ◽  
Nearest Neighbor ◽  
Protein Structures ◽  
Score Function ◽  
Fold Recognition ◽  
Protein Fold ◽  
Score Functions ◽  
Protein Fold Recognition ◽  
Protein Threading ◽  
Perceptron Learning

The usual two-body score (energy) function to recognize native folds of proteins is Miyazawa–Jernigan (MJ) pairwise-contact function. The pairwise-contact parameters between two amino acids in MJ function are symmetric in a sense that a directional order of amino acids sequence along the backbone of a protein is ignored in constructing score parameters. Here we report that we succeeded in constructing a nonsymmetric two-body score function, capturing a directional order of amino acids sequence, by a perceptron learning and a protein threading. We considered pairs of two adjacent amino acids that are separated by two consecutive peptide bonds with the backbone directionality from the N-terminus to the C-terminus of a protein. We also considered the local environmental character, such as the secondary structures and the hydrophobicity (solvation), of amino acids in protein structures. The score is a corresponding propensity for a directional alignment of these two adjacent amino acids with their local environments. The resulting score function simultaneously recognized native folds of 1006 proteins covering all representative proteins with a homology less than 30% among them. The quality of this score function was validated by a threading test of new distinct 382 proteins with a homology less than 90% among them, and it entailed a high success ratio for recognizing native folds of 364 (95.3%) proteins. It showed a good feasibility of designing protein score functions for protein fold recognition by a perceptron learning and a protein threading.

scholarly journals ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yassine Ghouzam ◽  
Guillaume Postic ◽  
Pierre-Edouard Guerin ◽  
Alexandre G. de Brevern ◽  
Jean-Christophe Gelly
Keyword(s):  
Structure Prediction ◽  
Web Server ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm

2020 ◽  
Author(s):  
Jiangyi Shao ◽  
Bin Liu
Keyword(s):  
Structure Prediction ◽  
Transitive Closure ◽  
Source Code ◽  
Fold Recognition ◽  
Protein Fold ◽  
Data Set ◽  
Pagerank Algorithm ◽  
Protein Fold Recognition ◽  
Benchmark Data ◽  
The Relationship

Abstract As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download

scholarly journals Protein fold recognition using geometric kernel data fusion

2014 ◽  
Vol 30 (13) ◽  
pp. 1850-1857 ◽  
Author(s):  
Pooya Zakeri ◽  
Ben Jeuris ◽  
Raf Vandebril ◽  
Yves Moreau
Keyword(s):  
Data Fusion ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition

scholarly journals Expanding our knowledge of the protein universe: Modelling of protein structures

2014 ◽  
Vol 70 (a1) ◽  
pp. C491-C491
Author(s):  
Jürgen Haas ◽  
Alessandro Barbato ◽  
Tobias Schmidt ◽  
Steven Roth ◽  
Andrew Waterhouse ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Model Organism ◽  
Data Bank ◽  
Continuous Model ◽  
Structure Modeling ◽  
Structure Comparison ◽  
Modeling And Prediction

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

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