Mutational signatures in GATA3 transcription factor and its DNA binding domain that stimulate breast cancer and HDR syndrome.

Transcription factors (TFs) play important roles in many biochemical processes. Many human genetic disorders have been associated with mutations in the genes encoding these transcription factors, and so those mutations became targets for medications and drug design. In parallel, since many transcription factors act either as tumor suppressors or oncogenes, their mutations are mostly associated with cancer. In this perspective, we studied the GATA3 transcription factor when bound to DNA in a crystal structure and assessed the effect of different mutations encountered in patients with different diseases and phenotypes. We generated all missense mutants of GATA3 protein and DNA within the adjacent and the opposite GATA3:DNA complex models. We mutated every amino acid and studied the new binding of the complex after each mutation. Similarly, we did for every DNA base. We applied Poisson-Boltzmann electrostatic calculations feeding into free energy calculations. After analyzing our data, we identified amino acids and DNA bases keys for binding. Furthermore, we validated those findings against experimental genetic data. Our results are the first to propose in silico modeling for GATA:DNA bound complexes that could be used to score effects of missense mutations in other classes of transcription factors involved in common and genetic diseases.

Novel Bioinformatics-Based Approach for Proteomic Biomarkers Prediction of Calpain-2 &Caspase-3 Protease Fragmentation: Application to ßII-Spectrin Protein.

The crucial biological role of proteases has been visible with the development of degradomics discipline involved in the determination of the proteases/substrates resulting in breakdown-products (BDPs) that can be utilized as putative biomarkers associated with different biological-clinical significance. In the field of cancer biology, matrix metalloproteinases (MMPs) have shown to result in MMPs-generated protein BDPs that are indicative of malignant growth in cancer, while in the field of neural injury, calpain-2 and caspase-3 proteases generate BDPs fragments that are indicative of different neural cell death mechanisms in different injury scenarios. Advanced proteomic techniques have shown a remarkable progress in identifying these BDPs experimentally. In this work, we present a bioinformatics-based prediction method that identifies protease-associated BDPs with high precision and efficiency. The method utilizes state-of-the-art sequence matching and alignment algorithms. It starts by locating consensus sequence occurrences and their variants in any set of protein substrates, generating all fragments resulting from cleavage. The complexity exists in space O(mn) as well as in O(Nmn) time, where N, m, and n are the number of protein sequences, length of the consensus sequence, and length per protein sequence, respectively. Finally, the proposed methodology is validated against ßII-spectrin protein, a brain injury validated biomarker.

Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders.

Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.