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?). 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.
| 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: |
| Cybulski, Larisa E; Martín, Mariana; Mansilla, María C et al. (2010) Membrane thickness cue for cold sensing in a bacterium. Curr Biol 20:1539-44 |
| Fernandez, Ariel; Berry, R Stephen (2010) Golden rule for buttressing vulnerable soluble proteins. J Proteome Res 9:2643-8 |
| Fraser, Christopher M; Fernández, Ariel; Scott, L Ridgway (2010) Dehydron analysis: quantifying the effect of hydrophobic groups on the strength and stability of hydrogen bonds. Adv Exp Med Biol 680:473-9 |
| 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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