Pairwise alignment of protein interaction networks.

With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost.     

Spatial and temporal dynamics of the cardiac mitochondrial proteome

Mitochondrial proteins alter in their composition and quantity drastically through time and space in correspondence to changing energy demands and cellular signaling events. The integrity and permutations of this dynamism are increasingly recognized to impact the functions of the cardiac proteome in health and disease. This article provides an overview on recent advances in defining the spatial and temporal dynamics of mitochondrial proteins in the heart. Proteomics techniques to characterize dynamics on a proteome scale are reviewed and the physiological consequences of altered mitochondrial protein dynamics are discussed. Lastly, we offer our perspectives on the unmet challenges in translating mitochondrial dynamics markers into the clinic.     

Comprehensive computational design of mCreI homing endonuclease cleavage specificity for genome engineering

Homing endonucleases (HEs) cleave long (～ 20 bp) DNA target sites with high site specificity to catalyze the lateral transfer of parasitic DNA elements. In order to determine whether comprehensive computational design could be used as a general strategy to engineer new HE target site specificities, we used RosettaDesign (RD) to generate 3200 different variants of the mCreI LAGLIDADG HE towards 16 different base pair positions in the 22 bp mCreI target site. Experimental verification of a range of these designs demonstrated that over 2/3 (24 of 35 designs, 69%) had the intended new site specificity, and that 14 of the 15 attempted specificity shifts (93%) were achieved. These results demonstrate the feasibility of using structure-based computational design to engineer HE variants with novel target site specificities to facilitate genome engineering.     

Inferring functional information from domain co-evolution

MOTIVATION: Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains. RESULTS: We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.     

Temporal control of neurogenin3 activity in pancreas progenitors reveals competence windows for the generation of different endocrine cell types

All pancreatic endocrine cells, producing glucagon, insulin, somatostatin, or PP, differentiate from Pdx1+ progenitors that transiently express Neurogenin3. To understand whether the competence of pancreatic progenitors changes over time, we generated transgenic mice expressing a tamoxifen-inducible Ngn3 fusion protein under the control of the pdx1 promoter and backcrossed the transgene into the ngn3(-/-) background, devoid of endogenous endocrine cells. Early activation of Ngn3-ER(TM) almost exclusively induced glucagon+ cells, while depleting the pool of pancreas progenitors. As from E11.5, Pdx1+ progenitors became competent to differentiate into insulin+ and PP+ cells. Somatostatin+ cells were generated from E14.5, while the competence to make glucagon+ cells was dramatically decreased. Hence, pancreas progenitors, similar to retinal or cortical progenitors, go through competence states that each allow the generation of a subset of cell types. We further show that the progenitors acquire competence to generate late-born cells in a mechanism that is intrinsic to the epithelium.