Protein secondary structure and solvent accessibility of proteins in decellularized heart valve scaffolds

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

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Genomic Mutations and Changes in Protein Secondary Structure and Solvent Accessibility of SARS-CoV-2 (COVID-19 Virus)

10.1101/2020.07.10.171769 ◽

2020 ◽

Author(s):

Thanh Thi Nguyen ◽

Pubudu N. Pathirana ◽

Thin Nguyen ◽

Henry Nguyen ◽

Asim Bhatti ◽

...

Keyword(s):

Secondary Structure ◽

Solvent Accessibility ◽

Point Mutations ◽

Protein Secondary Structure ◽

Intervention Strategies ◽

Relative Solvent Accessibility ◽

Highly Pathogenic ◽

Coding Regions ◽

Pathogenic Virus ◽

And Control

ABSTRACTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6,324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

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Developing structural profile matrices for protein secondary structure and solvent accessibility prediction

Bioinformatics ◽

10.1093/bioinformatics/btz238 ◽

2019 ◽

Vol 35 (20) ◽

pp. 4004-4010 ◽

Cited By ~ 3

Author(s):

Zafer Aydin ◽

Nuh Azginoglu ◽

Halil Ibrahim Bilgin ◽

Mete Celik

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Solvent Accessibility ◽

3D Structure ◽

Protein Secondary Structure ◽

Amino Acid Position ◽

Statistical Hypothesis ◽

Supplementary Information ◽

Structural Profile ◽

Benchmark Datasets

Abstract Motivation Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. Results In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests. Availability and implementation The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred. Supplementary information Supplementary data are available at Bioinformatics online.

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Dimensionality reduction for protein secondary structure and solvent accesibility prediction

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018500208 ◽

2018 ◽

Vol 16 (05) ◽

pp. 1850020 ◽

Cited By ~ 2

Author(s):

Zafer Aydin ◽

Oğuz Kaynar ◽

Yasin Görmez

Keyword(s):

Secondary Structure ◽

Dimensionality Reduction ◽

Solvent Accessibility ◽

Three Dimensional ◽

Protein Secondary Structure ◽

Extraction Methods ◽

Dimensional Structure ◽

Support Vector ◽

Noise Elimination ◽

Solvent Accessibility Prediction

Secondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction. The reduced feature set is used to train a support vector machine at the second stage of a hybrid classifier. Cross-validation experiments on two difficult benchmarks demonstrate that the dimension of the input space can be reduced substantially while maintaining the prediction accuracy. This will enable the incorporation of additional informative features derived for predicting the structural properties of proteins without reducing the accuracy due to overfitting.

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