Novel Method of Protein Structure Prediction (NPSPM) based on Short Range Interactions between Amino Acids

The Protein Structure Prediction (PSP) problem is concerned about the prediction of the native tertiary structure of a protein in respect to its amino acids sequence. PSP is a challenging and computationally open problem. Therefore, several researches and methodologies have been developed for it. In this way, developers are working to integrate frameworks in order to improve their capabilities and make their use more straightforward. This paper presents the application of NSGA-II algorithm using structural and energetic properties of protein. The implementation of this algorithm is based on ProtPred-GROMACS (2PG), an evolutionary framework for PSP. This framework is the integration between ProtPred and GROMACS. Six proteins were used to measure the capacity of ab initio predictions. The results were interesting since in all cases the native-like topology was obtained.

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Real-value and confidence prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning

10.1101/236851 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yujuan Gao ◽

Sheng Wang ◽

Minghua Deng ◽

Jinbo Xu

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Conformational Space ◽

Functional Study ◽

Dihedral Angles ◽

Prediction Errors ◽

The Real ◽

Novel Method

AbstractBackgroundProtein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging.MethodIn this study, we present a novel method to predict realvalued angles by combining clustering and deep learning. That is, we first generate certain clusters of angles (each assigned a label) and then apply a deep residual neural network to predict the label posterior probability. Finally, we output real-valued prediction by a mixture of the clusters with their predicted probabilities. At the same time, we also estimate the bound of the prediction errors at each residue from the predicted label probabilities.ResultIn this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds.ConclusionsOur study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

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Amino Acids Pattern-Biased Spiral Search for Protein Structure Prediction

Lecture Notes in Computer Science - PRICAI 2014: Trends in Artificial Intelligence ◽

10.1007/978-3-319-13560-1_12 ◽

2014 ◽

pp. 143-156

Author(s):

Mahmood A. Rashid ◽

Md. Masbaul Alam Polash ◽

M. A. Hakim Newton ◽

Md. Tamjidul Hoque ◽

Abdul Sattar

Keyword(s):

Amino Acids ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction

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Conceptual Framework for Invariant Protein Fragment Library

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1034.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 240-247

Keyword(s):

Amino Acids ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Data Bank ◽

Computational Techniques ◽

Excess Kurtosis ◽

T Score ◽

Fragment Library

Proteins are essential and are present in all life forms and determining its structure is cumbersome, laborious and time consuming. Hence, over 3-4 decades, researchers have been using computational techniques such as template and template free based protein structure prediction from its sequence. This research focuses on developing a conceptual basis for establishing an invariant fragment library which can be used for protein structure prediction. Based on 20 amino acids, fragments can be classified into lengths of 3 to 41 size. Further, they can be classified based on the identical number of amino acids present in the fragment. This encompasses theoretically the number of fragments that can exist and in no way represent the actual possible fragments that can exist in nature. Invariant fragments are ones which are rigid in structure 3-dimensionally and do not change. A formula was arrived at to determine all possible permutations that can exist for length 3 to 41 based on the 20 amino acids. 100 proteins from the Protein Data Bank were downloaded, broken into fragments of 3 to 41 resulting in a total of 6102,102 fragments using Asynchronous Distributed Processing. Then identical fragments in sequence were superimposed and Root Mean Square Deviation (RMSD) values were obtained resulting in roughly 3.2% of the original framgnets.. t-score and z-scores were obtained from which Skewness, Kurtosis and Excess Kurtosis were determined. For invariance, skewness cutoff was set at + 0.1 and using the excess kurtosis, fragments whose distribution were either leptokurtic or platykurtic and were within + 1 standard deviation of the mean value were considered as invariant i.e., if there were no outliers in the distribution and if most of the t-score or z-score values were centered around its average value. Using these cutoff values, fragments were classified and deposited into an invariant fragment library. Roughly 3,81,799 invariant fragments were obtained which is roughly 6.3% of the total number of initial fragments. This would be way less than the number of fragments that one has to either use in homology or de-novo modelling thereby reducing the design space. Further work is underway to set up the entire invariant fragment library which can then be used to predict protein structure by template-based approach.

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A Novel Method Using Abstract Convex Underestimation in Ab-Initio Protein Structure Prediction for Guiding Search in Conformational Feature Space

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2015.2497226 ◽

2016 ◽

Vol 13 (5) ◽

pp. 887-900 ◽

Cited By ~ 7

Author(s):

Xiao-Hu Hao ◽

Gui-Jun Zhang ◽

Xiao-Gen Zhou ◽

Xu-Feng Yu

Keyword(s):

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Feature Space ◽

Conformational Feature ◽

Convex Underestimation ◽

Novel Method

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

Plant Tissue Culture and Biotechnology ◽

10.3329/ptcb.v19i2.5439 ◽

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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