TTNPred | Development of novel computational biology pipeline for the efficient classification of titin SNPs for clinical use

Summary
Mutations in the giant muscle protein titin are a major cause of heart disorders in human populations. Routine DNA screening of patient cohorts is now becoming feasible, with a staggering number of titin truncations and missense single nucleotide polymorphisms (mSNPs) rapidly accumulating in genomics databases (>17,000 mSNPs). While the link between titin truncation and disease is now becoming clarified, detecting the pathogenic potential of mSNPs remains a substantial challenge. In mSNPs classification, bioinformatics evaluation is a necessary first filtering step, but existing predictors are poorly reliable. To address this problem, we aim to develop a new titin-centric scoring function that predicts the mechanistic effect of an mSNP exchange in the titin protein by considering the specific characteristics of its poly-domain chain. For this work, we will build a medium-throughput molecular diagnostic pipeline that harvest existent structural models of titin components in estimating mSNPs-induced changes in free energy and conformational dynamics in the protein. Calculations will be benchmarked against experimentally obtained biophysical and biochemical data. To develop this methodology, we will use a clinically pertinent training set of 75 mSNPs. However, in a subsequent step, stable predictions will be extrapolated to the rest of the titin chain by exploiting the repetition of structural and functional loci within the chain. A titin map of vulnerability “hot-spots” so calculated will be distributed to the research community. Ultimately, we aim to produce a tool that can aid clinicians to identify patients at risk of developing a titin-based heart condition at early disease stages, where intervention is still possible.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/753054
Start date: 01-01-2018
End date: 28-02-2020
Total budget - Public funding: 171 460,80 Euro - 171 460,00 Euro
Cordis data

Original description

Mutations in the giant muscle protein titin are a major cause of heart disorders in human populations. Routine DNA screening of patient cohorts is now becoming feasible, with a staggering number of titin truncations and missense single nucleotide polymorphisms (mSNPs) rapidly accumulating in genomics databases (>17,000 mSNPs). While the link between titin truncation and disease is now becoming clarified, detecting the pathogenic potential of mSNPs remains a substantial challenge. In mSNPs classification, bioinformatics evaluation is a necessary first filtering step, but existing predictors are poorly reliable. To address this problem, we aim to develop a new titin-centric scoring function that predicts the mechanistic effect of an mSNP exchange in the titin protein by considering the specific characteristics of its poly-domain chain. For this work, we will build a medium-throughput molecular diagnostic pipeline that harvest existent structural models of titin components in estimating mSNPs-induced changes in free energy and conformational dynamics in the protein. Calculations will be benchmarked against experimentally obtained biophysical and biochemical data. To develop this methodology, we will use a clinically pertinent training set of 75 mSNPs. However, in a subsequent step, stable predictions will be extrapolated to the rest of the titin chain by exploiting the repetition of structural and functional loci within the chain. A titin map of vulnerability “hot-spots” so calculated will be distributed to the research community. Ultimately, we aim to produce a tool that can aid clinicians to identify patients at risk of developing a titin-based heart condition at early disease stages, where intervention is still possible.

Status

CLOSED

Call topic

MSCA-IF-2016

Update Date

28-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.3. EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions (MSCA)
H2020-EU.1.3.2. Nurturing excellence by means of cross-border and cross-sector mobility
H2020-MSCA-IF-2016
MSCA-IF-2016