Structural profile matrices for predicting structural properties of proteins

2020 ◽  
Vol 18 (04) ◽  
pp. 2050022
Author(s):  
Nuh Azginoglu ◽  
Zafer Aydin ◽  
Mete Celik
Keyword(s):  
Structural Properties ◽  
Cross Validation ◽  
Solvent Accessibility ◽  
3D Structure ◽  
Computational Cost ◽  
Protein Secondary Structure ◽  
Structural Profile ◽  
Target Template ◽  
Structure Of Proteins ◽  
Validation Experiments

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment methods in LOMETS server, gap affine alignment method, ScanProsite, PfamScan, and HHblits. The contribution of each template is weighted by its similarity to target, which is assessed by several sequence alignment scores. For comparison, the SPMs are also computed using Homolpro, which uses BLAST for target template alignments and does not assign weights to templates. Incorporating the SPMs into DSPRED classifier, the prediction accuracy improves significantly as demonstrated by cross-validation experiments on two difficult benchmarks. The most accurate predictions are obtained using the SPMs derived by threading methods in LOMETS server. On the other hand, the computational cost of computing these SPMs was the highest.

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

2019 ◽  
Vol 35 (20) ◽  
pp. 4004-4010 ◽  
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.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

scholarly journals Interclass Interference Suppression in Multi-Class Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 450
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Na Jiang ◽  
Ran Cheng ◽  
Xianling Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Validation ◽  
Selection Process ◽  
Interference Suppression ◽  
Generalization Ability ◽  
Suppression Effect ◽  
Binary Classifiers ◽  
The One ◽  
Fold Cross Validation ◽  
Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

scholarly journals Structural and Technological Approach to Reveal the Role of the Lipid Phase in the Formation of Soy Emulsion Gels with Chia Oil

Gels ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Ana M. Herrero ◽  
Claudia Ruiz-Capillas
Keyword(s):  
Raman Spectroscopy ◽  
Structural Properties ◽  
Structural Changes ◽  
Protein Secondary Structure ◽  
Lipid Phase ◽  
Α Helix ◽  
Β Sheet ◽  
Shed Light ◽  
Chia Oil

Considerable attention has been paid to emulsion gels (EGs) in recent years due to their interesting applications in food. The aim of this work is to shed light on the role played by chia oil in the technological and structural properties of EGs made from soy protein isolates (SPI) and alginate. Two systems were studied: oil-free SPI gels (SPI/G) and the corresponding SPI EGs (SPI/EG) that contain chia oil. The proximate composition, technological properties (syneresis, pH, color and texture) and structural properties using Raman spectroscopy were determined for SPI/G and SPI/EG. No noticeable (p > 0.05) syneresis was observed in either sample. The pH values were similar (p > 0.05) for SPI/G and SPI/EG, but their texture and color differed significantly depending on the presence of chia oil. SPI/EG featured significantly lower redness and more lightness and yellowness and exhibited greater puncture and gel strengths than SPI/G. Raman spectroscopy revealed significant changes in the protein secondary structure, i.e., higher (p < 0.05) α-helix and lower (p < 0.05) β-sheet, turn and unordered structures, after the incorporation of chia oil to form the corresponding SPI/EG. Apparently, there is a correlation between these structural changes and the textural modifications observed.

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

2012 ◽  
Vol 1 (1) ◽  
pp. 79-87 ◽  
Author(s):  
Shangping Wang ◽  
Harriëtte Oldenhof ◽  
Andres Hilfiker ◽  
Michael Harder ◽  
Willem F. Wolkers
Keyword(s):  
Secondary Structure ◽  
Heart Valve ◽  
Solvent Accessibility ◽  
Protein Secondary Structure

Ensemble of Meta-Models Based on Local Error Measure Using Cross-Validation

2009 ◽  
Author(s):  
Dohyun Park ◽  
Yongbin Lee ◽  
Dong-Hoon Choi
Keyword(s):  
Prediction Accuracy ◽  
Cross Validation ◽  
Polynomial Regression ◽  
Computational Cost ◽  
Support Vector ◽  
Local Error ◽  
Test Cases ◽  
Meta Model ◽  
Lower Accuracy ◽  
Cross Validation Error

Many meta-models have been developed to approximate true responses. These meta-models are often used for optimization instead of computer simulations which require high computational cost. However, designers do not know which meta-model is the best one in advance because the accuracy of each meta-model becomes different from problem to problem. To address this difficulty, research on the ensemble of meta-models that combines stand-alone meta-models has recently been pursued with the expectation of improving the prediction accuracy. In this study, we propose a selection method of weight factors for the ensemble of meta-models based on v-nearest neighbors’ cross-validation error (CV). The four stand-alone meta-models we employed in this study are polynomial regression, Kriging, radial basis function, and support vector regression. Each method is applied to five 1-D mathematical examples and ten 2-D mathematical examples. The prediction accuracy of each stand-alone meta-model and the existing ensemble of meta-models is compared. Ensemble of meta-models shows higher accuracy than the worst stand-alone model among the four stand-alone meta-models at all test examples (30 cases). In addition, the ensemble of meta-models shows the highest accuracy for the 5 test cases. Although it has lower accuracy than the best stand-alone meta-model, it has almost same RMSE values (less than 1.1) as the best standalone model in 16 out of 30 test cases. From the results, we can conclude that proposed method is effective and robust.

scholarly journals Hermes: an ensemble machine learning architecture for protein secondary structure prediction

2019 ◽  
Author(s):  
Larry Bliss ◽  
Ben Pascoe ◽  
Samuel K Sheppard
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Cross Validation ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Lower Boundary ◽  
Protein Secondary Structure ◽  
Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

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