PredHSP: Sequence Based Proteome-Wide Heat Shock Protein Prediction and Classification Tool to Unlock the Stress Biology

Kumar, Ravindra; Kumari, Bandana; Kumar, Manish

doi:10.1371/journal.pone.0155872

Cited by 19 publications

(20 citation statements)

References 37 publications

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“…Classifications were also made among different families of HSPs where a particular family is considered as the positive set and the remaining families together as negative set. Moreover, performances of prediction models were assessed using leave-one-out cross-validation (LOOCV) technique as similar to the earlier studies (Feng et al, 2013 ; Ahmad et al, 2015 ; Kumar et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…The advancement of relatively cheaper sequencing technologies has witnessed a huge volume of protein sequences that are added to the public databases (Feng et al, 2013 ; Ahmad et al, 2015 ; Kumar et al, 2016 ). Due to lack of experimentally validated structures in the databases, resource intensive traditional method like nuclear magnetic resonance (NMR) has become inappropriate for identifying HSP families in large protein datasets (Redfield, 2004 ; Lange et al, 2012 ; Nasedkin et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the development of computational method for identifying HSPs and their families is essential due to their inexpensive and high throughput nature. To this end, some computational methods i.e., iHSP-PseRAAAC (Feng et al, 2013 ), JPred (Feng et al, 2014 ), JPPRED (Zhang et al, 2015 ), and PredHSP (Kumar et al, 2016 ) have already been developed in the recent past. In iHSP-PseRAAAC, support vector machine (SVM) was used for predicting six different families of HSPs, based on reduced alphabet amino acid composition (AAC) features of protein sequences.…”

Section: Introductionmentioning

confidence: 99%

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ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

et al. 2018

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Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information is available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs, and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is made freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for prediction of HSPs, to the extent of their families and sub-types.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

et al. 2018

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“…Dipeptide composition is a modified form of amino acid composition, which has been used in a number of protein classification methods like nuclear receptor family classification ( Bhasin & Raghava, 2004 ; Kumar et al, 2014b ), membrane protein prediction, ion channels prediction ( Lin & Ding, 2011 ), protein fold prediction ( Shamim, Anwaruddin & Nagarajaram, 2007 ), heat shock protein and its class prediction ( Kumar, Gromiha & Raghava, 2011 ; Kumar, Kumari & Kumar, 2016 ; Reczko & Bohr, 1994 ). Dipeptide composition calculates the number of all possible types of amino acid pairs in a protein sequence in a sliding window mode with step size 1 and window size 2.…”

Section: Methodsmentioning

confidence: 99%

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

Kumar

Kumari

Kumar

2017

PeerJ

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BackgroundThe endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i) proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii) proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum.MethodsThis is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins.ResultsIn this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83.69%. We have also annotated six different proteomes to predict the candidate endoplasmic reticulum resident proteins in them. A webserver, ERPred, was developed to make the method available to the scientific community, which can be accessed at http://proteininformatics.org/mkumar/erpred/index.html.DiscussionWe found that out of 124 proteins of the training dataset, only 66 proteins had endoplasmic reticulum retention signals, which shows that these signals are not an absolute necessity for endoplasmic reticulum resident proteins to remain inside the endoplasmic reticulum. This observation also strongly indicates the role of additional factors in retention of proteins inside the endoplasmic reticulum. Our proposed predictor, ERPred, is a signal independent tool. It is tuned for the prediction of endoplasmic reticulum resident proteins, even if the query protein does not contain specific ER-retention signal.

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“…Later on, Feng et al developed a freely accessible web server to classify the four types of HSP40 [22]. Stimulated by these pioneering works, Ravindra et al recently established a web server called PredHSP, which is based on coupled amino acid composition and support vector machine to identify heat shock proteins and classifies their different families [23]. However, there is no method available that can be used to identify the types of HSPs in plants.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Types of Plant Heat Shock Proteins

Chen

Jin

2017

LOC

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Background: Heat shock proteins (HSPs) ubiquitously expressed in both prokaryotes and eukaryotes. According to their molecular mass and function, HSPs are classified into different families which are structurally different and play distinct functions in biological processes. Although some efforts have been made for identifying the types of HSPs, there is no method available that can be used to identify the types of HSPs in plants. Methods: The amino acid distributions in the different types of HSPs are anazlyed. HSPs are encoded using the reduced amino acid alphabet (RAAA). By comparing the predictive capability of models based on the composition of RAAA with different sizes, the optimal feature vector was obtained. A support vector machine based model was developed to identify the types of HSPs by using the optimal feature vector. Results: The amino acid distributions are different among the different families of HSPs. In the rigorous jackknife test, the proposed method obtained an accuracy of 93.65% for identifying the five families of HSPs in plant. Conclusions: We hope the proposed method will become a useful tool to identify the types of HSPs in plants.

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PredHSP: Sequence Based Proteome-Wide Heat Shock Protein Prediction and Classification Tool to Unlock the Stress Biology

Cited by 19 publications

References 37 publications

ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

Predicting the Types of Plant Heat Shock Proteins

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