Many proteins recognize specific sequence motifs in DNA and form stable protein-DNA complexes to perform a range of biological functions associated with transcription, repair and packaging. Although, DNA sequences recognized by most proteins are relatively easily described by small sequence motifs, similar motif-like alphabet in proteins is rarely observed. Sequence and structural elements of proteins interact with DNA in a complex, cooperative and dynamic manner. We have been investigating sequence, structural, evolutionary and thermodynamic patterns of DNA-binding proteins to establish the broad principles of DNA-recognition by them. Our studies have revealed several interesting features useful for understanding and predicting DNA-binding proteins, their binding sites and pinpoint the most significant players in the stability of these complexes.We have developed these leads into novel algorithms and web-based tools for public use.

.

Les champignons possèdent une très grande variété d'enzymes auxnombreuses applications potentielles. Nos travaux visent à étudier l'ensemble de cette diversité enzymatique afin de comprendre commentelle a pu prendre place et se maintenir au cours du temps. En utilisantdes méthodes d'apprentissage et de fouille de données nous essayonségalement de voir dans quelle mesure la capacité enzymatique deschampignons permet de rendre compte de leur évolution

A major challenge facing biological research is the assignment of a function to uncharacterized genomic sequences. I will describe an approach for the functional characterization of gene products, which combines high-throughput bioinformatics and low-throughput, targeted experiments. The prediction of gene functions is based on the logic analysis of the evolutionary relationships existing between known and unknown genes involved in the same metabolic process. Bioinformatic predictions are validated in tageted experiments through the structural and functional characterization of gene products. Examples will be presented illustrating the application of such method to the study of the enzymes involved in purine catabolism. However, the method has a general applicability in the basic task of finding a function for unknown genes

A major challenge facing biological research is the assignment of a function to uncharacterized genomic sequences. I will describe an approach for the functional characterization of gene products, which combines high-throughput bioinformatics and low-throughput, targeted experiments. The prediction of gene functions is based on the logic analysis of the evolutionary relationships existing between known and unknown genes involved in the same metabolic process. Bioinformatic predictions are validated in tageted experiments through the structural and functional characterization of gene products. Examples will be presented illustrating the application of such method to the study of the enzymes involved in purine catabolism. However, the method has a general applicability in the basic task of finding a function for unknown genes

Many families of homologous proteins show a remarkable degree of structural and functional conservation, despite the large variability in their amino acid sequences. We have developed a statistical-mechanics inspired inference approach to link this variability (easy to observe) to structure (hard to observe), i.e. to infer directly co-evolving residue pairs which turn our to form native contacts in the folded protein with high accuracy. The gained information is used to guide tertiary (and quaternary) structure prediction. As a specific example, I will discuss the auto-phosphorylation complex of histidine kinases, which are involved in the majority of signal transduction systems in the bacteria. Only a multidisciplinary approach integrating statistical genomics, biophysical protein simulation, and mutagenesis experiments, allows us to predict and verify the - so far unknown - active kinase structure

Phenotypic states of individual cells in a genetically identical population are heterogeneous and often correlated with the fitness of individual cells.The correlations between phenotypic states such as gene expression levels and fitness introduce complexities to understand the causes of cell population behaviors because the average of phenotypic states of cell population is not identical to the typical state of individual cells.Although this problem was well recognized by the researchers in 1930's-60's and several non-trivial theoretical results have been derived, the issue has not been fully addressed experimentally because of technical difficulties.We are currently working on this problem using modern single-cell measurement tools.In this seminar, I will first present our single-cell study on microbial persistence that eventually led us to the aforementioned problem.Then, I will present the experimental examination on the mathematical relation that links the growth rates of individual cells to that of cell population.I will also explain the newly developed microfluidic…