Our preliminary structure-based investigations show that water exclusion from deficiently packed hydrogen bonds and other pre-formed electrostatic interactions constitutes a driving factor conferring high specificity to protein association. Thus, an evolutionary conserved feature, the under-dehydrated hydrogen bond, termed dehydron, appears to be a structural marker for interactivity. Dehydrons were experimentally and statistically shown to constitute sticky spots on the protein surface and to be abundant at protein-protein interfaces, especially at those that cannot be understood in terms of standard interactions. The dehydron distribution on the surface of soluble proteins constitutes a determinant of the propensity for association and aberrant aggregation. The identification of dehydrons has relied so far on detailed structural information, a limitation precluding a proteomic analysis. This proposal is geared at introducing a sequence-based predictive method to establish the biological relevance of dehydrons and their potential as markers for drugable targets. Thus, we intend to introduce a powerful unsupervised scanning technology to detect signals of interactivity and drugability at a genomic scale. This goal requires constructing a machine-learning discriminator trained on a structural database. The over-all aim is to develop a sequence-based multi-purpose tool to expand the universe of drugable targets, diagnose propensity for aberrant aggregation and make interactomic inferences. The efficacy of our predictor will be tested on five grounds: a) Assaying for amino-acid variability and determining whether residues predicted solely from sequence to be engaged in dehydrons are actually conserved, b) Using a redundancy-free curated PDB sample as training set, we shall determine the accuracy and precision of the sequence-based predictor using a nonhomologous PDB complement set and annotated SwissProt entries as testing sets, c) Contrasting our results with an alternative dehydron predictor based on a reliable sequence-based predictor of native disorder (PONDR(r)). This dehydron predictor is based on a correlation found between the extent of hydrogen-bond packing and the score of structural disorder, d) Contrasting sequence-based diagnosis of amyloidogenic aggregation with SwissProt annotations and other annotated disease-related sequence repositories; e) Contrasting compiled drug-target quality assessments and structural data and screening profiles for protein-ligand associations with the predicted dehydron patterns. Thus, the novel design concept of """"""""drug inhibitor as a wrapper of functional packing defects"""""""" will be explored and validated.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BDMA (01))
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Edmonds, Charles G
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Rice University
Biomedical Engineering
Schools of Engineering
United States
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Fernández, Ariel (2014) Synergizing immunotherapy with molecular-targeted anticancer treatment. Drug Discov Today 19:1427-32
Fernández, Ariel; Lynch, Michael (2011) Non-adaptive origins of interactome complexity. Nature 474:502-5
Schulz, Erica; Frechero, Marisa; Appignanesi, Gustavo et al. (2010) Sub-nanoscale surface ruggedness provides a water-tight seal for exposed regions in soluble protein structure. PLoS One 5:
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Fernandez, Ariel; Chen, Jianping (2009) Human capacitance to dosage imbalance: coping with inefficient selection. Genome Res 19:2185-92
Fernandez, Ariel; Bazan, Soledad; Chen, Jianping (2009) Taming the induced folding of drug-targeted kinases. Trends Pharmacol Sci 30:66-71
Fernandez, Ariel; Sessel, Sean (2009) Selective antagonism of anticancer drugs for side-effect removal. Trends Pharmacol Sci 30:403-10
Fernandez, Ariel; Crespo, Alejandro; Tiwari, Abhinav (2009) Is there a case for selectively promiscuous anticancer drugs? Drug Discov Today 14:1-5

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