Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

Ahmad, Saeed; Kabir, Muhammad; Hayat, Maqsood

doi:10.1016/j.cmpb.2015.07.005

Cited by 94 publications

(29 citation statements)

References 77 publications

(143 reference statements)

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“…So the dipeptide composition (DC) 28,29 , the frequencies of different dipeptides (combinations of two adjacent amino acids), is also used to represent AMP sequence. The DC is a 400-dimensional vector as [ d 1 , d 2 , …, d 400 ] T , in which d i ( i = 1, 2, …, 400) is the occurrence frequency of the i th dipeptide.…”

Section: Methodsmentioning

confidence: 99%

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Wang

Liu

et al. 2017

Sci Rep

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Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.

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Section: Methodsmentioning

confidence: 99%

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Wang

Liu

et al. 2017

Sci Rep

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“…It is important to compare the performance of a newly developed prediction method vis-à-vis the existing one. The method developed by Ahmad et al [ 12 ] does not provide any family wise performance of HSP class prediction. So we compared the performance of PredHSP only with the method developed by Feng et al and which was named as iHSP-PseRAAAC [ 1 ].…”

Section: Resultsmentioning

confidence: 99%

“…Considering the importance of HSPs in cellular metabolism and number of un-annotated sequences in the databases that might be HSPs, development of computational method to identify HSPs and classify their family only on the basis of primary protein sequence will have a far reaching effect. Two attempts have already been made by (i) Feng et al [ 1 ] and (ii) Ahmad et al [ 12 ] regarding HSP protein annotation but only for their classification into different HSP families. But methods have following shortcomings; (i) they do not have provision for classifying HSP family without first verifying that query proteins is HSP or not, (ii) method developed by Ahmad et al [ 12 ], does not provide any web based tool or standalone software for the prediction purpose.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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Heat shock proteins are chaperonic proteins, which are present in every domain of life. They play a crucial role in folding/unfolding of proteins, their sorting and assembly into multi-protein complex, cell cycle control and also protect the cell during stress. Considering the fact that no web-based predictor is available for simultaneous prediction and classification of HSPs, it is imperative to develop a method, which can predict and classify them efficiently. In this study, we have developed coupled amino acid composition and support vector machine based two-tier method, PredHSP that identifies heat shock proteins (1st tier) and classifies it to different families (at 2nd tier). At 1st tier, we achieved maximum accuracy 76.66% with MCC 0.43, while at 2nd tier we achieved maximum accuracy 96.36% with MCC 0.87 for HSP20, 91.91% with MCC 0.83 for HSP40, 95.96% with MCC 0.72 for HSP60, 91.87% with MCC 0.71 for HSP70, 98.43% with MCC 0.70 for HSP90 and 97.48% with MCC 0.71 for HSP100. We have also developed a webserver, as well as standalone package for the use of scientific community, which can be accessed at http://14.139.227.92/mkumar/predhsp/index.html.

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“…The HSPs can also be used as therapeutic targets for cancer treatment (Chatterjee and Burns, 2017 ; Saini and Sharma, 2017 ; Wu et al, 2017 ), diagnosis of neurodegenerative disorders (Kampinga and Bergink, 2016 ), and preventing the onset and progression of atrial fibrillation (Hu et al, 2017 ). Owing to the wide range of activities of HSPs, identification of HSPs, and categorizing them into different families is an important and challenging problem (Feng et al, 2013 ; Ahmad et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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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Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

Cited by 94 publications

References 77 publications

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

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

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

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