Cargando…
RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data
S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467992/ https://www.ncbi.nlm.nih.gov/pubmed/34576064 http://dx.doi.org/10.3390/ijms22189901 |
_version_ | 1784573545743908864 |
---|---|
author | Bandyopadhyay, Soumyendu Sekhar Halder, Anup Kumar Zaręba-Kozioł, Monika Bartkowiak-Kaczmarek, Anna Dutta, Aviinandaan Chatterjee, Piyali Nasipuri, Mita Wójtowicz, Tomasz Wlodarczyk, Jakub Basu, Subhadip |
author_facet | Bandyopadhyay, Soumyendu Sekhar Halder, Anup Kumar Zaręba-Kozioł, Monika Bartkowiak-Kaczmarek, Anna Dutta, Aviinandaan Chatterjee, Piyali Nasipuri, Mita Wójtowicz, Tomasz Wlodarczyk, Jakub Basu, Subhadip |
author_sort | Bandyopadhyay, Soumyendu Sekhar |
collection | PubMed |
description | S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male—15%, female—15%, and combined—7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets. |
format | Online Article Text |
id | pubmed-8467992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84679922021-09-27 RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data Bandyopadhyay, Soumyendu Sekhar Halder, Anup Kumar Zaręba-Kozioł, Monika Bartkowiak-Kaczmarek, Anna Dutta, Aviinandaan Chatterjee, Piyali Nasipuri, Mita Wójtowicz, Tomasz Wlodarczyk, Jakub Basu, Subhadip Int J Mol Sci Article S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male—15%, female—15%, and combined—7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets. MDPI 2021-09-14 /pmc/articles/PMC8467992/ /pubmed/34576064 http://dx.doi.org/10.3390/ijms22189901 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bandyopadhyay, Soumyendu Sekhar Halder, Anup Kumar Zaręba-Kozioł, Monika Bartkowiak-Kaczmarek, Anna Dutta, Aviinandaan Chatterjee, Piyali Nasipuri, Mita Wójtowicz, Tomasz Wlodarczyk, Jakub Basu, Subhadip RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title | RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title_full | RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title_fullStr | RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title_full_unstemmed | RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title_short | RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data |
title_sort | rfcm-palm: in-silico prediction of s-palmitoylation sites in the synaptic proteins for male/female mouse data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467992/ https://www.ncbi.nlm.nih.gov/pubmed/34576064 http://dx.doi.org/10.3390/ijms22189901 |
work_keys_str_mv | AT bandyopadhyaysoumyendusekhar rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT halderanupkumar rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT zarebakoziołmonika rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT bartkowiakkaczmarekanna rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT duttaaviinandaan rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT chatterjeepiyali rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT nasipurimita rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT wojtowicztomasz rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT wlodarczykjakub rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata AT basusubhadip rfcmpalminsilicopredictionofspalmitoylationsitesinthesynapticproteinsformalefemalemousedata |