SoluProt: Prediction of Protein Solubility

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130777" target="_blank" >RIV/00216305:26230/18:PU130777 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11808" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11808</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

SoluProt: Prediction of Protein Solubility

Popis výsledku v původním jazyce

Protein solubility poses a major bottleneck in production of many therapeutic and industrially attractive proteins. Experimental solubilization attempts are plagued by relatively low success rates and often lead to the loss of biological activity. Therefore, any advance in computational prediction of protein solubility may reduce the cost of experimental studies significantly. Here, we propose a novel software tool SoluProt for prediction of solubility from protein sequence based on machine learning and TargetTrack database. SoluProt achieved the best accuracy 58.2% and AUC 0.61 of all available tools at an independent balanced test set derived from NESG database. While the absolute prediction performance is rather low, SoluProt can still help to reduce costs of experimental studies significantly by efficient prioritization of protein sequences. The main SoluProt contribution lies in improved preprocessing of noisy training data and sensible selection of sequence features included in the prediction model.

Název v anglickém jazyce

SoluProt: Prediction of Protein Solubility

Popis výsledku anglicky

Protein solubility poses a major bottleneck in production of many therapeutic and industrially attractive proteins. Experimental solubilization attempts are plagued by relatively low success rates and often lead to the loss of biological activity. Therefore, any advance in computational prediction of protein solubility may reduce the cost of experimental studies significantly. Here, we propose a novel software tool SoluProt for prediction of solubility from protein sequence based on machine learning and TargetTrack database. SoluProt achieved the best accuracy 58.2% and AUC 0.61 of all available tools at an independent balanced test set derived from NESG database. While the absolute prediction performance is rather low, SoluProt can still help to reduce costs of experimental studies significantly by efficient prioritization of protein sequences. The main SoluProt contribution lies in improved preprocessing of noisy training data and sensible selection of sequence features included in the prediction model.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

DAZ & WIKT 2018 Proceedings

ISBN

978-80-214-5679-2

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

261-265

Název nakladatele

Brno University of Technology

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

11. 10. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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