2P008 Prediction of protein disordered region from amino acid sequences-Improvement of prediction method with tertiary structure prediction

BackgroundAmoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-richEntamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal inE. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriatein-silicomethods.MethodsThe amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out.ResultsThe protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops.DiscussionThe structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

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Protein tertiary structure prediction using hidden Markov model based on lattice

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019500070 ◽

2019 ◽

Vol 17 (02) ◽

pp. 1950007

Author(s):

Farzad Peyravi ◽

Alimohammad Latif ◽

Seyed Mohammad Moshtaghioun

Keyword(s):

Amino Acid ◽

Amino Acid Sequence ◽

Structure Prediction ◽

Tertiary Structure ◽

Hidden Markov ◽

Face Centered Cubic ◽

Tertiary Structure Prediction ◽

Cubic Lattices ◽

Face Centered ◽

Protein Tertiary Structure Prediction

The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.

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In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and peroxiredoxin

10.7287/peerj.preprints.2392 ◽

2016 ◽

Author(s):

Kumar Manochitra ◽

Subhash Chandra Parija

Keyword(s):

Amino Acid ◽

Entamoeba Histolytica ◽

In Silico ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Amino Acid Sequences ◽

Treatment Modalities ◽

Bioinformatic Tools ◽

Quaternary Structures

Background: Amoebiasis is the third most common parasitic cause of morbidity and mortality particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence arises a necessity for a better diagnostic approach. Serine-rich Entamoeba histolytica protein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal in E. histolytica virulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential which are not available till date. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using relevant in-silico methods. Methods:The amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out. Results: The protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be a extracellular protein, peroxiredoxin was a peripheral membrane protein, while Gal/GalAc was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All three proteins exhibited similarity in their structures and were mostly composed of loops. Discussion:The structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of three sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

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In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and peroxiredoxin

10.7287/peerj.preprints.2392v1 ◽

2016 ◽

Author(s):

Kumar Manochitra ◽

Subhash Chandra Parija

Keyword(s):

Amino Acid ◽

Entamoeba Histolytica ◽

In Silico ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Amino Acid Sequences ◽

Treatment Modalities ◽

Bioinformatic Tools ◽

Quaternary Structures

Background: Amoebiasis is the third most common parasitic cause of morbidity and mortality particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence arises a necessity for a better diagnostic approach. Serine-rich Entamoeba histolytica protein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal in E. histolytica virulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential which are not available till date. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using relevant in-silico methods. Methods:The amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out. Results: The protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be a extracellular protein, peroxiredoxin was a peripheral membrane protein, while Gal/GalAc was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All three proteins exhibited similarity in their structures and were mostly composed of loops. Discussion:The structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of three sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

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Amino acid sequence determination, in silico tertiary structure prediction and anticancer activity assessment of l-glutaminase from Bacillus cereus

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-016-0118-5 ◽

2016 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Priyanka Singh ◽

Rathindra Mohan Banik ◽

Priyanka Shah

Keyword(s):

Amino Acid ◽

Amino Acid Sequence ◽

Bacillus Cereus ◽

Anticancer Activity ◽

In Silico ◽

Structure Prediction ◽

Tertiary Structure ◽

Sequence Determination ◽

Tertiary Structure Prediction ◽

Activity Assessment

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Improving protein tertiary structure prediction with conformational propensities of amino acid residues

2016 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2016.7743772 ◽

2016 ◽

Cited By ~ 1

Author(s):

Bruno Borguesan ◽

Jonas Bohrer ◽

Mariel Barbachan e Silva ◽

Leonardo de Lima Correa ◽

Marcio Dorn

Keyword(s):

Amino Acid ◽

Structure Prediction ◽

Tertiary Structure ◽

Amino Acid Residues ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Tertiary Structure Prediction

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Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.26194 ◽

2021 ◽

Author(s):

Ivan Anishchenko ◽

Minkyung Baek ◽

Hahnbeom Park ◽

Naozumi Hiranuma ◽

David E. Kim ◽

...

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Tertiary Structure ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Tertiary Structure Prediction

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Template-based prediction of protein structure with deep learning

BMC Genomics ◽

10.1186/s12864-020-07249-8 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Haicang Zhang ◽

Yufeng Shen

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Tertiary Structure ◽

Query Sequence ◽

Dynamic Programming Algorithm ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Threading ◽

Protein Tertiary Structure Prediction

Abstract Background Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

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