Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Xu, Yan; Ding, Ya-Xin; Ding, Jun; Wu, Lingyun; Xue, Yu

doi:10.1038/srep38318

Cited by 49 publications

(38 citation statements)

References 28 publications

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“…This method is widely recognized in application domains, such as artificial intelligence (AI) algorithms [14], medical disease diagnosis [15], bioengineering [16], and electric engineering [17]. The method has become a much focused classic in the feature selection area given its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

et al. 2017

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Scientific evaluation of partial discharge (PD) severity in gas-insulation switchgear (GIS) can assist in mastering the insulation condition of in-service GIS. Limited theoretical research on the laws of PD deterioration leads to a finite number of evaluation features extracted and subjective features selected for PD severity assessment. Therefore, this study proposes a minimum-redundancy maximum-relevance (mRMR) algorithm-based feature optimization selection method to realize the scientific and reasonable choice of PD severity features. PD ultra-high frequency data of varying severities are produced by simulating four typical insulation defects in GIS, which are then collected in the lab. A 16-dimension feature set describing PD original characteristics is abstracted in phase-resolved partial discharge (PRPD) mode, and the more informative evaluation feature set characterizing PD severity is further excavated by the mRMR method. Finally, a support vector machine (SVM) algorithm is employed as the classifier for intelligent evaluation to compare the evaluation effects of PD severity between the feature set selected by mRMR and the feature set is composed of discharge times, amplitude value, and time intervals obtained traditionally based on discharge change theory. The proposed comparison test showed the effectiveness of the mRMR method in informative feature selection and the accuracy of PD severity assessment for all defined defects.

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Section: Introductionmentioning

confidence: 99%

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

et al. 2017

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“…While these modifications have recently been implicated as important regulators of metabolism, our knowledge regarding their biological significance is still in its infancy 58–60 . Malonyl-lysine and succinyllysine modifications induce dramatic structural changes on lysine residues due to their large size and they frequently occur together 61 . Our data are consistent with these previous observations, however, in the context of DBI proteoforms, the biological consequence of these modifications is yet unknown.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Top-Down Mass Spectrometry Identifies Proteoforms Differentially Released during Mechanical Stimulation of Mouse Skin

et al. 2018

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Mechanotransduction refers to the processes whereby mechanical stimuli are converted into electrochemical signals that allow for the sensation of our surrounding environment through touch. Despite its fundamental role in our daily lives, the molecular and cellular mechanisms of mechanotransduction are not yet well-defined. Previous data suggest that keratinocytes may release factors that activate or modulate cutaneous sensory neuron terminals, including small molecules, lipids, peptides, proteins, and oligosaccharides. This study presents a first step towards identifying soluble mediators of keratinocyte-sensory neuron communication by evaluating the potential for top down mass spectrometry to identify proteoforms released during one minute of mechanical stimulation of mouse skin from naïve animals. Overall, this study identified 47 proteoforms in the secretome of mouse hind paw skin, of which 14 were differentially released during mechanical stimulation, and includes proteins with known and previously unknown relevance to mechanotransduction. Finally, this study outlines a bioinformatic workflow that merges output from two complementary analysis platforms for top down data and demonstrates the utility of this workflow for integrating quantitative and qualitative data.

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“…AAindex 37 is a database that holds numerous physicochemical and biological properties of amino acids. Several mixtures of physicochemical properties have been employed that effectively convert sequences of peptides into mathematical expressions 18 , 38 – 40 . In this study, we used 14 physicochemical properties: hydrophobicity, solvent accessibility, polarity, polarizability, accessibility, PK-N , PK-C , melting point, molecular weight, optical rotation, net charge index of side chains, entropy of formation, heat capacity and absolute entropy.…”

Section: Methodsmentioning

confidence: 99%

SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites

Al-Barakati

McConnell

Hicks

et al. 2018

Sci Rep

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Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew’s correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.

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Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection

Cited by 49 publications

References 28 publications

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

Quantitative Top-Down Mass Spectrometry Identifies Proteoforms Differentially Released during Mechanical Stimulation of Mouse Skin

SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites

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