In-silicoprediction and modeling of theEntamoeba histolyticaproteins: Serine-richEntamoeba histolyticaprotein and 29 kDa Cysteine-rich protease

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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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

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

Seibutsu Butsuri ◽

10.2142/biophys.45.s121_4 ◽

2005 ◽

Vol 45 (supplement) ◽

pp. S121

Author(s):

T. Ishida ◽

S. Nakamura ◽

K. Shimizu

Keyword(s):

Amino Acid ◽

Structure Prediction ◽

Tertiary Structure ◽

Prediction Method ◽

Amino Acid Sequences ◽

Tertiary Structure Prediction

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NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720017500093 ◽

2017 ◽

Vol 15 (03) ◽

pp. 1750009 ◽

Cited By ~ 3

Author(s):

Bruno Grisci ◽

Márcio Dorn

Keyword(s):

Amino Acid ◽

Amino Acid Sequence ◽

Tertiary Structure ◽

Structural Information ◽

Protein Structures ◽

Three Dimensional ◽

Conformational Flexibility ◽

Amino Acid Sequences ◽

Conformational Search ◽

Back Propagation Algorithm

The development of computational methods to accurately model three-dimensional protein structures from sequences of amino acid residues is becoming increasingly important to the structural biology field. This paper addresses the challenge of predicting the tertiary structure of a given amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We present a new method, namely NEAT–FLEX, based on NeuroEvolution of Augmenting Topologies (NEAT) to extract structural features from (ABS) proteins that are determined experimentally. The proposed method manipulates structural information from the Protein Data Bank (PDB) and predicts the conformational flexibility (FLEX) of residues of a target amino acid sequence. This information may be used in three-dimensional structure prediction approaches as a way to reduce the conformational search space. The proposed method was tested with 24 different amino acid sequences. Evolving neural networks were compared against a traditional error back-propagation algorithm; results show that the proposed method is a powerful way to extract and represent structural information from protein molecules that are determined experimentally.

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Predicting protein distance maps according to physicochemical properties

Journal of Integrative Bioinformatics ◽

10.1515/jib-2011-181 ◽

2011 ◽

Vol 8 (3) ◽

pp. 158-175

Author(s):

Gualberto Asencio Cortés ◽

Jesús A. Aguilar-Ruiz

Keyword(s):

Amino Acids ◽

Amino Acid ◽

Physicochemical Properties ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Contact Map ◽

Acid Properties ◽

Amino Acid Properties ◽

3D Surfaces

SummaryThe prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein con- sists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.

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De novo protein design by deep network hallucination

10.1101/2020.07.22.211482 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ivan Anishchenko ◽

Tamuka M. Chidyausiku ◽

Sergey Ovchinnikov ◽

Samuel J. Pellock ◽

David Baker

Keyword(s):

Amino Acid ◽

Protein Design ◽

Structure Prediction ◽

De Novo ◽

Protein Structures ◽

Monte Carlo Sampling ◽

Amino Acid Sequences ◽

Wide Range ◽

Physically Based ◽

Folded Proteins

AbstractThere has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures. We obtained synthetic genes encoding 129 of these network hallucinated sequences, expressed and purified the proteins in E coli, and found that 27 folded to monomeric stable structures with circular dichroism spectra consistent with the hallucinated structures. Thus deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute, alongside traditional physically based models, to the de novo design of proteins with new functions.

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In silico Analysis of Nonsynonymous Genomic Variants within CCM2 Gene Reaffirm the Existence of Dual Cores within Typical PTB Domain

10.1101/2021.11.09.467991 ◽

2021 ◽

Author(s):

Akhil Padarti ◽

Ofek Belkin ◽

Johnathan Abou-Fadel ◽

Jun Zhang

Keyword(s):

Amino Acid ◽

Tertiary Structure ◽

Protein Structures ◽

In Silico Analysis ◽

Cavernous Malformations ◽

Bioinformatic Tools ◽

Dual Functional ◽

Structural Impacts ◽

Protein Conservation ◽

Ptb Domain

Purpose: The objective of this study is to validate the existence of dual cores within the typical phosphotyrosine binding (PTB) domain and to identify potentially damaging and pathogenic nonsynonymous coding single nuclear polymorphisms (nsSNPs) in the canonical PTB domain of the CCM2 gene that causes cerebral cavernous malformations (CCMs). Methods: The nsSNPs within the coding sequence for PTB domain of human CCM2 gene, retrieved from exclusive database search, were analyzed for their functional and structural impact using a series of bioinformatic tools. The effects of the mutations on tertiary structure of the PTB domain in human CCM2 protein were predicted to examine the effect of the nsSNPs on tertiary structure on PTB Cores. Results: Our mutation analysis, through alignment of protein structures between wildtype CCM2 and mutant, indicated that the structural impacts of pathogenic nsSNPs is biophysically limited to only the spatially adjacent substituted amino acid site with minimal structural influence on the adjacent core of the PTB domain, suggesting both cores are independently functional and essential for proper CCM2 function. Conclusion: Utilizing a combination of protein conservation and structure-based analysis, we analyzed the structural effects of inherited pathogenic mutations within the CCM2 PTB domain. Our results indicated that the pathogenic amino acid substitutions lead to only subtle changes locally confined to the surrounding tertiary structure of the PTB core within which it resides, while no structural disturbance to the neighboring PTB core was observed, reaffirming the presence of dual functional cores in the PTB domain.

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Amino acid torsion angles enable prediction of protein fold classification

Scientific Reports ◽

10.1038/s41598-020-78465-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kun Tian ◽

Xin Zhao ◽

Xiaogeng Wan ◽

Stephen S.-T. Yau

Keyword(s):

Protein Structure ◽

Amino Acid ◽

Protein Structure Prediction ◽

Structure Prediction ◽

High Throughput Sequencing ◽

Protein Structures ◽

Amino Acid Sequences ◽

Single Amino Acid ◽

New Approach ◽

Protein Functions

AbstractProtein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.

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Amino acid torsion angles enable prediction of protein fold classification

10.21203/rs.2.20475/v1 ◽

2020 ◽

Author(s):

Kun Tian ◽

Xin Zhao ◽

Xiaogeng Wan ◽

Stephen Yau

Keyword(s):

Protein Structure ◽

Amino Acid ◽

Prediction Model ◽

Structure Prediction ◽

High Throughput Sequencing ◽

Protein Structures ◽

Data Bank ◽

Amino Acid Sequences ◽

Single Amino Acid ◽

New Approach

Abstract Background Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy, are complex and may require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction.Results Here we describe a new approach that uses integration and analysis of torsion angle information from the Protein Data Bank to enable prediction of protein structures from amino acid sequences. Our prediction model performed well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. This new prediction model performs well with an average of 92.5% accuracy for structure classification, which is higher than the previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence.Conclusions The results show that our method provides a new effective and reliable tool for protein structure prediction research.

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