NDLI: Prediction of protein solubility in E. coli

Content Provider	IEEE Xplore Digital Library
Author	Samak, T. Gunter, D. Zhong Wang
Copyright Year	2012
Description	Author affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA (Samak, T.; Gunter, D.; Zhong Wang)
Abstract	Gene synthesis is a key step to convert digitally predicted proteins to functional proteins. However, it is a relatively expensive and labor-intensive process. About 30–50% of the synthesized proteins are not soluble, thereby further reduces the efficacy of gene synthesis as a method for protein function characterization. Solubility prediction from primary protein sequences holds the promise to dramatically reduce the cost of gene synthesis. This work presents a framework that creates models of solubility from sequence information. From the primary protein sequences of the genes to be synthesized, sequence features can be used to build computational models for solubility. This way, biologists can focus the effort on synthesizing genes that are highly likely to generate soluble proteins. We have developed a framework that employs several machine learning algorithms to model protein solubility. The framework is used to predict protein solubility in the Escherichia coli expression system. The analysis is performed on over 1,600 quantified proteins. The approach successfully predicted the solubility with more than 80% accuracy, and enabled in depth analysis of the most important features affecting solubility. The analysis pipeline is general and can be applied to any set of sequence features to predict any binary measure. The framework also provides the biologist with a comprehensive comparison between different learning algorithms, and insightful feature analysis.
Starting Page	1
Ending Page	8
File Size	366046
Page Count	8
File Format	PDF
ISBN	9781467344678
e-ISBN	9781467344661
DOI	10.1109/eScience.2012.6404416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machines Accuracy Biological system modeling Computational modeling Pipelines Predictive models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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