Program: (Lecture abstracts)

9:00-9:30	Registration and coffee
9:30-9:45	Welcoming addresses
9:45-10:30	Keynote Jose Onuchic Center for Theoretical Biological Physics and Physics dept., University of California San Diego	The energy landscape for folding and function: The kinesin story
10:30-10:55	Yossef Kliger Compugen LTD	Modulating conformational changes in proteins: from in silico design to preclinical proof of concept
10:55-12:10	Coffee break and poster session
12:10-12:35	Liran Carmel Genetics dept., Hebrew University of Jerusalem	Gene architecture: Dynamics versus conservation
12:35-13:00	Yanay Ofran Faculty of Life Sciences, Bar-Ilan University	A multilevel approach to function prediction
13:00-13:25	Dima Lukatsky Chemistry dept., Ben-Gurion University of the Negev	Structural similarity statistically enhances interaction propensity of proteins
13:25-14:40	Lunch and poster session
14.40-14.50	Society meeting
14:50-15:15	Tomer Shlomi Computer Science dept., Technion - Israel Institute of Technology	Constraint-based modeling of Human metabolism
15:15-15:40	Ron Milo Plant Sciences dept. The Weizmann Institute of Science	Optimality in Carbon Metabolism
15:40-16:05	Lilach Hadany Plant Sciences dept., Tel-Aviv University	Evolutionary models of genetic variation: a role for the individual?
16:05-16:20	Coffee break
16:20-17:05	Keynote David Harel Computer Science and Applied Mathematics dept., The Weizmann Institute of Science	Can we Computerize an Elephant?
17:05-17:15	Closing remarks and poster prizes

Lecture abstracts

Globally the energy landscape of a folding protein resembles a partially rough funnel with reduced energetic frustration. A consequence of minimizing energetic frustration is that the topology of the native fold also plays a major role in the folding mechanism. Some folding motifs are easier to design than others suggesting the possibility that evolution not only selected sequences with sufficiently small energetic frustration but also selected more easily designable native structures. The overall structure of the on-route and off-route (traps) intermediates for the folding of more complex proteins is also strongly influenced by topology.
Many cellular functions rely on interactions among proteins and between proteins and nucleic acids. The limited success of binding predictions may suggest that the physical and chemical principles of protein binding have to be revisited to correctly capture the essence of protein recognition. Going beyond folding, the power of reduced models to study the physics of protein assembly will be discussed. Since energetic frustration is sufficiently small, native topology-based models, which correspond to perfectly unfrustrated energy landscapes, have shown that binding mechanisms are robust and governed primarily by the protein's native topology. These models impressively capture many of the binding characteristics found in experiments and highlight the fundamental role of flexibility in binding. Deciphering and quantifying the key ingredients for biological self-assembly is invaluable to reading out genomic sequences and understanding cellular interaction networks. Going even beyond binding, we will be discussing the energy landscape for the molecular motor kinesin.

Blocking conformational changes in proteins is a challenging task. Inspired by the susceptibility of viral entry to inhibition by synthetic peptides that block the formation of helix-helix interactions in viral envelope proteins, we developed a computational approach for predicting interacting helices. Using this approach, which combines correlated mutations analysis and Fourier transform, we designed peptides that target gp96 and clusterin, two secreted chaperones known to shift between inactive and active conformations. The gp96-derived peptide inhibited the production of inflammatory cytokines in stimulated human blood mononuclear cells and reduced circulating levels of endotoxin-induced TNF&alpha, IL-6 and IFN&alpha in mice. The clusterin-derived peptide arrested proliferation of several neoplastic cell lines, and significantly enhanced the cytostatic activity of Taxol in vitro and in a xenograft model of lung cancer. Furthermore, the predicted mode of action of both active peptides was experimentally verified: both peptides bind their parent proteins, and their biological activity was abolished in the presence of the peptides corresponding to the counterpart helices. These data demonstrate a novel method for rational design of protein antagonists.

The presence of introns in protein-coding genes is a universal feature of eukaryotic genome organization. Many introns appear in the same position in distant taxa, such as plants and animals. Depending on the methods and data sets used, researchers have reached opposite conclusions on the causes of this high fraction of shared intron positions. Some studies conclude that shared intron positions reflect, almost entirely, a remarkable evolutionary conservation, whereas others attribute it to parallel gain. To resolve these contradictions, we developed a probabilistic model of evolution that allows for variability of intron gain and loss rates over branches of the phylogenetic tree, individual genes, and individual sites. Applying this model to an extended set of conserved eukaryotic genes, we find that parallel gain, on average, accounts for only ~8% of the shared intron positions. However, the distribution of parallel gains over the phylogenetic tree of eukaryotes is highly non-uniform. There are, practically, no parallel gains in closely related lineages, whereas for distant lineages, such as animals and plants, parallel gains appear to contribute up to 20% of the shared intron positions. In any case, the high level of intron position sharing is due, primarily, to evolutionary conservation. Accordingly, numerous introns appear to persist in the same position over hundreds of millions of years of evolution. This is compatible with recent observations of a negative correlation between the rate of intron gain and coding sequence evolution rate of a gene, suggesting that a considerable fraction of the introns are functionally relevant.

In different contexts protein function can mean very different things: it may refer to the nitty-gritty of the protein's biochemical activity; it can mean the cellular process in which the protein is involved; it can refer to its physiological role or to its ostensible connection to phenotype or disease. Indeed, function prediction methods typically focus on a very specific aspect of function. However, the different facets of protein function are tightly related: understanding the biochemical function sheds light on the physiological function and may help explain the effect of the on phenotype. I will show that system level analysis can unravel the molecular mechanisms of individual proteins, and that understanding the molecular details of individual proteins can illuminate physiology beyond the cellular level. Furthermore, I will describe a multilevel approach for function prediction that combines molecular data with cellular, physiological, clinical and network data to promote function prediction at all levels.

We study statistical properties of interacting protein interfaces and predict two strong, related effects: (i) statistically enhanced self-attraction of proteins; (ii) statistically enhanced attraction of proteins with similar structures. The effects originate in the fact that the probability to find a pattern self-match between two identical, interacting protein interfaces is always higher compared with the probability for a pattern match between two different, promiscuous protein interfaces. This theoretical finding explains statistical prevalence of homodimers in protein-protein interaction networks reported earlier. Further, our findings are confirmed by the analysis of curated database of protein complexes that showed highly statistically significant overrepresentation of dimers formed by structurally similar proteins with highly divergent sequences ("superfamily heterodimers"). We predict that significant fraction of heterodimers evolved from homodimers with the negative design evolutionary pressure applied against promiscuous homodimer formation. This is achieved through the formation of highly specific contacts formed by charged residues as demonstrated both in model and real superfamily heterodimers. In addition we introduce the notion of structural correlations of amino acid interface density. We predict that protein interfaces with enhanced structural correlations are statistically more promiscuous as compared with proteins possessing a lower degree of interface structural correlations.
References:
1. D. B. Lukatsky and E. I. Shakhnovich, Statistically Enhanced Promiscuity of Structurally Correlated Patterns, Phys. Rev. E 77, 020901(R) (2008).
2. D. B. Lukatsky, B. E. Shakhnovich, J. Mintseris, and E. I. Shakhnovich, Structural Similarity Enhances Interaction Propensity of Proteins, J. Mol. Biol. 365, 1596 (2007).
3. D. B. Lukatsky, K. B. Zeldovich, and E. I. Shakhnovich, Statistically Enhanced Self-Attraction of Random Patterns, Phys. Rev. Lett. 97, 178101 (2006).

Research into human metabolism and its regulation has expanded rapidly due to the emergence of metabolic diseases such as diabetes and obesity as major sources of morbidity and mortality, with metabolic enzymes and their regulators increasingly emerging as viable drug targets. Computational modeling of human metabolism via traditional kinetic approaches is problematic due to lack of accurate information of kinetic constants and enzyme and metabolite intracellular concentrations. Constraint-based modeling is a computational approach for analyzing large-scale metabolic networks that bypasses this hurdle by relying solely on simple physical-chemical constraints. However, despite the progress in applying constraint-based modeling to studying the metabolism of microorganisms, large-scale modeling of human metabolism is still in its infancy. We present two novel constraint-based methods for studying human metabolism via existing comprehensive reconstructions of the global human metabolic network: (i) A method for predicting tissue-specific metabolism, by integrating tissue-specific gene- and protein expression data with a metabolic network model. The tissue specificity of many metabolic disease-causing genes is shown to go significantly beyond that manifested in their expression level, giving rise to new predictions concerning their involvement in different tissues. (ii) A method for systematically predicting metabolic biomarkers for in-born errors of metabolism. The predicted biomarkers can be used for early diagnosis of metabolic disorders via biofluid metabolomics, which detects specific metabolites whose concentration is altered due to genomic mutations. Overall, our results lay down the computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Carbon metabolism uses a complex series of enzymatic steps to fix carbon dioxide into sugars and then convert them into metabolic precursors. Can we gain insight into the constraints that shape evolution's solution to this task? We use all known classes of enzymes that work on carbohydrates to generate all possible paths between every two metabolites. We find that central metabolism is built as a minimal walk between the twelve precursor metabolites that form the basis for biomass: Every pair of consecutive precursors in the network is connected by the minimal number of enzymatic steps. Similarly, input sugars are converted into precursors by the shortest possible enzymatic paths. This suggests an optimality principle for the structure of central metabolism. The approach is applied to design new carbon fixation pathways which are potential alternatives to the Calvin-Benson cycle.

Genetic variation provides the raw material for evolutionary change. In most population genetics models, variation is assumed to be generated at a uniform rate, depending on the genes coding for variation but not on the state of the individual. In this talk I discuss the implications of a new assumption - that the generation of genetic variation is itself plastic, so that genetic variation is generated at higher rates when the individual is not doing well. Using computational models, we found that plastic genetic variation can evolve under a wide parameter range, and might help explain the evolution of sex and the mechanisms of complex adaptation. Theoretical models and experimental evidence will be discussed.

The talk shows the way techniques from computer science and software engineering can be applied beneficially to research in the life sciences. We will discuss the idea of comprehensive and realistic modeling of biological systems, where we try to understand and an entire system in detail, utilizing in the modeling effort all that is known about it. I will address the motivation for such modeling and the philosophy underlying the techniques for carrying it out, as well as the crucial question of when such models are to be deemed valid, or complete. The examples I will present will be from among the biological modeling efforts my group has been involved in: T cell development in the thymus, lymph node behavior, organogenesis of the pancreas, fate determination in the reproductive system of C. elegans , and a generic cell model. The ultimate dream is to produce an interactive, dynamic, computerized model of an entire multi-cellular organism.