scholarly journals Quality assessment for the putative intrinsic disorder in proteins

2018 ◽  
Vol 35 (10) ◽  
pp. 1692-1700 ◽  
Author(s):  
Gang Hu ◽  
Zhonghua Wu ◽  
Christopher J Oldfield ◽  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Quality Assessment ◽  
Propensity Scores ◽  
Input Sequence ◽  
Predictive Performance ◽  
Intrinsic Disorder ◽  
Supplementary Information ◽  
Innovative Design ◽  
Design Elements ◽  
Protein Intrinsic Disorder ◽  
Disorder Predictor

Abstract Motivation While putative intrinsic disorder is widely used, none of the predictors provides quality assessment (QA) scores. QA scores estimate the likelihood that predictions are correct at a residue level and have been applied in other bioinformatics areas. We recently reported that QA scores derived from putative disorder propensities perform relatively poorly for native disordered residues. Here we design and validate a general approach to construct QA predictors for disorder predictions. Results The QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions) toolbox of methods accommodates a diverse set of ten disorder predictors. It builds upon several innovative design elements including use and scaling of selected physicochemical properties of the input sequence, post-processing of disorder propensity scores, and a feature selection that optimizes the predictive models to a specific disorder predictor. We empirically establish that each one of these elements contributes to the overall predictive performance of our tool and that QUARTER’s outputs significantly outperform QA scores derived from the outputs generated the disorder predictors. The best performing QA scores for a single disorder predictor identify 13% of residues that are predicted with 98% precision. QA scores computed by combining results of the ten disorder predictors cover 40% of residues with 95% precision. Case studies are used to show how to interpret the QA scores. QA scores based on the high precision combined predictions are applied to analyze disorder in the human proteome. Availability and implementation http://biomine.cs.vcu.edu/servers/QUARTER/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gang Hu ◽  
Akila Katuwawala ◽  
Kui Wang ◽  
Zhonghua Wu ◽  
Sina Ghadermarzi ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Intrinsic Disorder ◽  
Critical Assessment ◽  
Disordered Proteins ◽  
Computational Tool ◽  
Cellular Functions ◽  
Disorder Prediction ◽  
Protein Intrinsic Disorder ◽  
Sequence Profiles ◽  
Better Than

AbstractIdentification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/

scholarly journals ‘Neo-Crosssexual’ fashion in contemporary men’s suits

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hoe Ryung Lee ◽  
Jongsun Kim ◽  
Jisoo Ha
Keyword(s):  
Gender Issue ◽  
Fashion Design ◽  
Personal Style ◽  
Cultural Shift ◽  
Innovative Design ◽  
Design Elements ◽  
Binary Constraints ◽  
Binary Associations ◽  
Design Characteristics ◽  
Novel Design

AbstractToday, fashionable menswear is appropriating traditionally feminine design elements on an unparalleled international scale. This phenomenon should not be interpreted purely as a subversive gender issue, but should also be viewed as an expression of personal style and taste. In order to properly describe this phenomenon, the term ‘crosssexual’ must be introduced to English fashion vocabulary. This paper examines the innovative design characteristics of contemporary crosssexual menswear emerging prominently in men’s suits since 2015 and also raises the necessity of coining a new term: ‘neo-crosssexual’. Ultimately, this paper reveals how neo-crosssexual fashion employs a great variety of innovative silhouettes and novel design elements—ranging from structural or decorative details to colors, fabrics, patterns, and accessories—all of which enable the wearer to express whatever image of themselves they desire. Previous studies have interpreted crosssexual fashion as men simply portraying themselves as effeminate by wearing such clothing, but this conclusion is reductive and reveals there is still an unconscious recognition of limiting binary associations. The richness of neo-crosssexual fashion design, rather, gives individuals freedom to choose a sartorial image devoid of binary constraints. Conventionally classified ‘feminine designs’ are now perceived as creative and functional means to fluidity. This positive cultural shift has led to an increasing number of men choosing to wear suits that incorporate ‘feminine’ design elements, freeing this traditional garment from its limited formal use and symbolic hegemonic power.

scholarly journals PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

2019 ◽  
Vol 35 (14) ◽  
pp. i510-i519 ◽  
Author(s):  
Soufiane Mourragui ◽  
Marco Loog ◽  
Mark A van de Wiel ◽  
Marcel J T Reinders ◽  
Lodewyk F A Wessels
Keyword(s):  
Cell Lines ◽  
Drug Response ◽  
Domain Adaptation ◽  
Predictive Performance ◽  
Human Tumors ◽  
Supplementary Information ◽  
Patient Response ◽  
Common Information ◽  
Response Predictors ◽  
Clinical Models

Abstract Motivation Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting. Results We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors. Availability and implementation PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE). Supplementary information Supplementary data are available at Bioinformatics online.

Troponins, intrinsic disorder, and cardiomyopathy

2016 ◽  
Vol 397 (8) ◽  
pp. 731-751 ◽  
Author(s):  
Insung Na ◽  
Min J. Kong ◽  
Shelby Straight ◽  
Jose R. Pinto ◽  
Vladimir N. Uversky
Keyword(s):  
Posttranslational Modifications ◽  
Cardiac Troponin ◽  
Troponin I ◽  
Troponin T ◽  
Intrinsic Disorder ◽  
Functional Changes ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Structural And Functional Properties ◽  
Protein Intrinsic Disorder

Abstract Cardiac troponin is a dynamic complex of troponin C, troponin I, and troponin T (TnC, TnI, and TnT, respectively) found in the myocyte thin filament where it plays an essential role in cardiac muscle contraction. Mutations in troponin subunits are found in inherited cardiomyopathies, such as hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). The highly dynamic nature of human cardiac troponin and presence of numerous flexible linkers in its subunits suggest that understanding of structural and functional properties of this important complex can benefit from the consideration of the protein intrinsic disorder phenomenon. We show here that mutations causing decrease in the disorder score in TnI and TnT are significantly more abundant in HCM and DCM than mutations leading to the increase in the disorder score. Identification and annotation of intrinsically disordered regions in each of the troponin subunits conducted in this study can help in better understanding of the roles of intrinsic disorder in regulation of interactomes and posttranslational modifications of these proteins. These observations suggest that disease-causing mutations leading to a decrease in the local flexibility of troponins can trigger a whole plethora of functional changes in the heart.

Exploring Protein Intrinsic Disorder with MobiDB

2020 ◽  
pp. 127-143
Author(s):  
Alexander Miguel Monzon ◽  
András Hatos ◽  
Marco Necci ◽  
Damiano Piovesan ◽  
Silvio C. E. Tosatto
Keyword(s):  
Intrinsic Disorder ◽  
Protein Intrinsic Disorder

scholarly journals SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

2019 ◽  
Vol 36 (4) ◽  
pp. 1074-1081 ◽  
Author(s):  
Bin Yu ◽  
Wenying Qiu ◽  
Cheng Chen ◽  
Anjun Ma ◽  
Jing Jiang ◽  
...  
Keyword(s):  
Drug Design ◽  
Protein Function ◽  
Predictive Performance ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Source Codes ◽  
Extreme Gradient Boosting ◽  
Auto Correlation Function ◽  
Abnormal Mitochondria ◽  
Leave One Out

Abstract Motivation Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design. Results We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases. Availability and implementation The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/. Supplementary information Supplementary data are available at Bioinformatics online.

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