A critical component on the regulation of gene expression is protein-DNA interactions. The Cys2-His2 zinc finger (ZF) family of transcription factors is the largest family of nucleic acid binding proteins in eukaryotes and a key participant in the regulation of most genes, being activated in response to a wide variety of stimuli. From a structural point of view, this family of transcription factors (TFs) is highly conserved, consisting in two or more ZF modular domains that work together to recognize specific DNA sequences. It is then apparent that ZFs are an ideal model system to study the fundamental principles governing non-specific and specific protein-DNA binding, and shed light into an essential step in the transcription regulation of genes. The goal of this project is to reveal the finely tuned molecular interactions responsible for the thermodynamics and dynamics of protein recognition and binding using structural-based computational approaches in combination with available selection and binding data from biochemical experiments, and high resolution crystal structures deposited in the Protein Data Bank. Specifically, the role of counter ions and side chains in protein-DNA association will be studied using molecular dynamics sampling as a guiding principle to assess and predict changes in binding free energy due to point mutations, allowing us to explore in much greater detail the molecular mechanism of their interaction.
Understanding protein-DNA interactions is crucial in order to learn how proteins regulate almost every biological process. So far this problem has been studied by biochemical experiments, challenging and expensive structural determination of the complexes, and computational studies focused mostly in sequence-based motif algorithms. This project will incorporate dynamics and molecular biophysics to uncover sequence/structural relationships that might escape current methods. Any progress in this fundamental problem is bound to bring about a better understanding of how gene regulation works cooperatively in a cell. The project will involve the training of graduate students in computational/structural modeling, and efforts will be made to recruit talented students from underrepresented groups in the project. This project can also make a broad impact in the development of new agents that block or down regulate the expression of specific DNA sequences linked to a pathogenic pathway or protein.
Intellectual Merit: We dissected protein-DNA interactions from three different points of views: (a) the dichotomy between non-specific and protein-DNA binding; (b) specificity determinant interactions; and, (c) the role of intrinsic disorder in protein-DNA binding. (a) Molecular dynamics simulations were carried out to show that zinc finger transcription factors have an electrostatic hot spot that attracts phosphate groups. The implication is that these hot spots are responsible for the stickiness of these factors to the backbone of DNA, readily making non-specific interactions. We also showed that specificity is determined by side chain contacts to the bases. These side chains sample structural configurations that make them predisposed to recognize the cognate binding site. These observations revealed differential mechanisms between non-specific DNA interactions modulated by contacts to the DNA backbone and specific interactions driven by preformed side chains contacting DNA bases. Our findings provide strong foundation for the structural basis of the so-called sliding mechanism of protein-DNA recognition. (b) Homology models of mutant zinc finger transcription factors were constructed to analyze the interaction network between DNA and the zinc finger factors in the presence of explicit water molecules. Based on a broad range of structural and biochemical data, we developed a mathematical formalism based on a Multi-Integer Programming (MIP) model to validate the effect of water molecules in the polarization of protein interactions, developing the most accurate contact potential for predict changes in binding free of protein-DNA complexes. (c) Genome wide analysis of the amount of intrinsic disorder in genes show a significantly larger proportion of disorder in genes involved in trancriptional regulation as compared to other functions such as enzymes or binding proteins. We developed a quantitative theory that makes predictions regarding the role of intrinsic disorder in protein function. In particular, we discuss the implications of analytical solutions of a series of fundamental thermodynamic models of protein interactions in which disordered proteins are characterized by positive folding free energies. We validate our predictions by assigning protein function by using the gene ontology classification—in which ‘‘protein binding’’, ‘‘catalytic activity’’, and ‘‘transcription regulator activity’’ are the three largest functional categories—and by performing genome-wide surveys of both the amount of disorder in these functional classes and binding affinities for both prokaryotic and eukaryotic genomes. Specifically, without assuming any a priori structure–function relationship, the theory predicts that both catalytic and low-affinity binding (Kd >10-7 M) proteins prefer ordered structures, whereas only high-affinity binding proteins (found mostly in eukaryotes) can tolerate disorder. Relevant to both transcription and signal trans- duction, the theory also explains how increasing disorder can tune the binding affinity to maximize the specificity of promiscuous interactions. Collectively, these studies provide insight into how natural selection acts on folding stability to optimize protein function. Broader impact: The PI was involved in a number of educational and outreach activities. The grant funded 1 UG, 3 GSs and 1 Post Doc. Dr. Camacho also was involved in the NSF-NIH funded BBSI summer research institute for interdisciplinary training of undergraduate and high school students. Previous mentees now in Computational Biology programs include Ms. Jamie Duke (Yale), and Mr. Andrej Savol (Pitt). Ms. Anna Kirk, a native-american, was granted a scholarship in Applied Math at Oregon State. This past summer the PI is mentoring Mr. Ben Zeldes and Ms. Adriana Diaz as part of our REU funded program. This rewarding experience also motivated a greater involvement in mentoring and diversity, participating in a NSF-sponsored retreat to discuss issues faced by faculty from underrepresented groups and from MSIs. Dr. Camacho also participates in SACNAS as local mentor in Pittsburgh, and in recruitment and curriculum activities. Besides the training and outreach activities already mentioned in the project summary, Dr. Camacho dedicated considerable time and effort to the teaching goals of the Department. As already mentioned, he was actively engaged in a variety of courses offered to graduate students and medical students, in addition to providing regular mentoring to members of his laboratory. He has lectured at the NIH-NSF Bioengineering & Bioinformatics Summer Institute (BBSI) during the 2008 and 2009 summer sessions, and the TecBIO REU 2010 Summer program. Finally, for the last four years, he has been co-teaching/co-developing a new course on Computational Structural Biology for first year graduate students with multi-disciplinary backgrounds. Sharing of resources: the PI’s lab has a long tradition of developing (NSF funded) open access resources to the research community (docking server ClusPro; free energy estimate FastContact; analysis of PPIs Anchor; small-molecule docking http://anchorquery.ccbb.pitt.edu and others; see http://smoothdock.ccbb.pitt.edu). Dr. Camacho has served in review panels for NSF and NIH, as well as for multiple scientific journals. Educational Component and Impacts
