Molecular interactions between proteins and DNA play crucial roles in many fundamental biological processes, such as DNA modification and gene regulation. DNA-binding proteins encompass various degrees of binding specificity, ranging from highly specific to non-specific. This proposal aims to investigate the dynamic structural features for achieving high degree of protein-DNA binding specificity. Current readout mechanisms are based on analysis of the snapshots of protein-DNA complex structures. Protein-DNA recognition, however, is a dynamic process involving structural fitting between proteins and DNA. More complete description is needed to fully understand specificity in protein-DNA recognition. With increasing number of high-resolution protein-DNA complex structures (holo) and their corresponding unbound protein structures (apo) in Protein Data Bank, we attempt to perform systematic studies to decipher the structural and dynamic code encrypting binding specificity between proteins and DNA using datasets with different degrees of binding specificity. Specifically, we will implement two individual yet highly complementary approaches: (1) a comparative analysis of holo-apo pairs that cover the specificity landscape to investigate the dynamic features that contribute to specific protein-DNA interaction during the recognition process; and (2) an integrative method by combining sequence analysis, homology modeling and comparative molecular dynamics (MD) simulations to study the role of flexibility in binding specificity of homeodomains.
This proposed work investigates the origin and nature for specific protein-DNA interactions. Specific protein-DNA interaction is critical in many biological processes and altered specificity by mutations can cause diseases including cancer. The results from this project will help us better understand the mechanisms of specific protein-DNA interactions and can provide guidance in the design of new proteins with novel binding specificity, an important area in biotechnology and medicine.
| Farrel, Alvin; Guo, Jun-Tao (2017) An efficient algorithm for improving structure-based prediction of transcription factor binding sites. BMC Bioinformatics 18:342 |
| Lin, Maoxuan; Whitmire, Sarah; Chen, Jing et al. (2017) Effects of short indels on protein structure and function in human genomes. Sci Rep 7:9313 |
| Farrel, Alvin; Murphy, Jonathan; Guo, Jun-Tao (2016) Structure-based prediction of transcription factor binding specificity using an integrative energy function. Bioinformatics 32:i306-i313 |
| Corona, Rosario I; Guo, Jun-Tao (2016) Statistical analysis of structural determinants for protein-DNA-binding specificity. Proteins 84:1147-61 |
