Function2Form Bridge - Towards synthetic protein holistic performance-prediction

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dc.contributor.author Yallapragada, V. V. B.
dc.contributor.author Walker, Sidney P.
dc.contributor.author Devoy, Ciaran
dc.contributor.author Buckley, Stephen
dc.contributor.author Flores, Yensi
dc.contributor.author Tangney, Mark
dc.date.accessioned 2019-10-17T09:08:58Z
dc.date.available 2019-10-17T09:08:58Z
dc.date.issued 2019-10-07
dc.identifier.citation Yallapragada, V. V. B., Walker, S. P., Devoy, C., Buckley, S., Flores, Y. and Tangney, M. (2019) 'Function2Form Bridge - Towards synthetic protein holistic performance-prediction', Proteins. doi: 10.1002/prot.25825 en
dc.identifier.issn 0887-3585
dc.identifier.uri http://hdl.handle.net/10468/8789
dc.identifier.doi 10.1002/prot.25825 en
dc.description.abstract Protein engineering and synthetic biology stand to benefit immensely from recent advances in in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple â partsâ /functions, and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge), attempts to address this by combining the scores of different design parameters pertaining to the protein being analysed into a single easily interpreted output describing overall performance. The strategy comprises 1. A mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance); 2. The F2F-Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing accessibility of synthetic protein building and de novo protein design. en
dc.description.sponsorship Health Research Board (MRCG2016-25) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher John Wiley & Sons, Inc. en
dc.relation.uri https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25825
dc.rights © 2019, Wiley Periodicals, Inc. All rights reserved. This is the peer reviewed version of the following article: Yallapragada, V. V. B., Walker, S. P., Devoy, C., Buckley, S., Flores, Y. and Tangney, M. (2019) 'Function2Form Bridge - Towards synthetic protein holistic performance-prediction', Proteins, doi: 10.1002/prot.25825, which has been published in final form at https://doi.org/10.1002/prot.25825. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. en
dc.subject Synthetic biology en
dc.subject High throughput in silico screening en
dc.subject In silico modelling en
dc.subject Antibody screening en
dc.subject Protein scoring en
dc.subject Community‐based data reporting Machine learning en
dc.subject De novo protein design en
dc.title Function2Form Bridge - Towards synthetic protein holistic performance-prediction en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother John Mark Tangney, Biosciences Institute, University College Cork, Cork, Ireland. +353-21-490-3000 Email: m.tangney@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher. en
dc.check.date 2020-10-07
dc.date.updated 2019-10-17T08:50:34Z
dc.description.version Accepted Version en
dc.internal.rssid 499906712
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Health Research Board en
dc.contributor.funder Breakthrough Cancer Research, Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Proteins en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress m.tangney@ucc.ie en
dc.internal.bibliocheck In press. Check vol / issue / page range. Amend citation and rights statement as necessary. en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2273/IE/Alimentary Pharmabiotic Centre (APC) - Interfacing Food & Medicine/ en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Career Development Award/15/CDA/3630/IE/Edible Probiotics As Cancer Theranostics/ en
dc.identifier.eissn 1097-0134


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