ERC FUNDED PROJECTS

We propose to elucidate the structural design principles of naturally occurring antibody complementarity-determining regions (CDRs) and to computationally design novel antibody functions. Antibodies represent the most versatile known system for molecular recognition. Research has yielded many insights into antibody design principles and promising biotechnological and pharmaceutical applications. Still, our understanding of how CDRs encode specific loop conformations lags far behind our understanding of structure-function relationships in non-immunological scaffolds. Thus, design of antibodies from first principles has not been demonstrated. We propose a computational-experimental strategy to address this challenge. We will: (a) characterize the design principles and sequence elements that rigidify antibody CDRs. Natural antibody loops will be subjected to computational modeling, crystallography, and a combined in vitro evolution and deep-sequencing approach to isolate sequence features that rigidify loop backbones; (b) develop a novel computational-design strategy, which uses the >1000 solved structures of antibodies deposited in structure databases to realistically model CDRs and design them to recognize proteins that have not been co-crystallized with antibodies. For example, we will design novel antibodies targeting insulin, for which clinically useful diagnostics are needed. By accessing much larger sequence/structure spaces than are available to natural immune-system repertoires and experimental methods, computational antibody design could produce higher-specificity and higher-affinity binders, even to challenging targets; and (c) develop new strategies to program conformational change in CDRs, generating, e.g., the first allosteric antibodies. These will allow targeting, in principle, of any molecule, potentially revolutionizing how antibodies are generated for research and medicine, providing new insights on the design principles of protein functional sites.

1 499 930 €

Start date: 2013-09-01, End date: 2018-08-31

"The first decade of Algorithmic Mechanism Design (AMD) concentrated, very successfully, on the design of truthful mechanisms for the allocation of resources among agents with private preferences. Truthful mechanisms are ones that incentivize rational users to report their preferences truthfully. Truthfulness, however, for all its theoretical appeal, suffers from several inherent limitations, mainly its high communication and computation complexities. It is not surprising, therefore, that practical applications forego truthfulness and use simpler mechanisms instead. Simplicity in itself, however, is not sufficient, as any meaningful mechanism should also have some notion of fairness; otherwise agents will stop using it over time. In this project I plan to develop an innovative AMD theoretical framework that will go beyond truthfulness and focus instead on the natural themes of simplicity and fairness, in addition to computational tractability. One of my primary goals will be the design of simple and fair poly-time mechanisms that perform at near optimal levels with respect to important economic objectives such as social welfare and revenue. To this end, I will work toward providing precise definitions of simplicity and fairness and quantifying the effects of these restrictions on the performance levels that can be obtained. A major challenge in the evaluation of non-truthful mechanisms is defining a reasonable behavior model that will enable their evaluation. The success of this project could have a broad impact on Europe and beyond, as it would guide the design of natural mechanisms for markets of tens of billions of dollars in revenue, such as online advertising, or sales of wireless frequencies. The timing of this project is ideal, as the AMD field is now sufficiently mature to lead to a breakthrough and at the same time young enough to be receptive to new approaches and themes."

1 394 600 €

Start date: 2013-11-01, End date: 2018-10-31

Axon growth potential declines during development, contributing to the lack of effective regeneration in the adult central nervous system. What determines the intrinsic growth potential of neurites, and how such growth is regulated during development, disease and following injury is a fundamental question in neuroscience. Although multiple lines of evidence indicate that intrinsic growth capability is genetically encoded, its nature remains poorly defined. Neuronal remodeling of the Drosophila mushroom body offers a unique opportunity to study the mechanisms of various types of axon degeneration and growth. We have recently demonstrated that regrowth of axons following developmental pruning is not only distinct from initial outgrowth but also shares molecular similarities with regeneration following injury. In this proposal we combine state of the art tools from genomics, functional genetics and microscopy to perform a comprehensive study of the mechanisms underlying axon growth during development and following injury. First, we will combine genetic, biochemical and genomic studies to gain a mechanistic understanding of the developmental regrowth program. Next, we will perform extensive transcriptomic analyses and comparisons aimed at defining the genetic programs involved in initial axon growth, developmental regrowth, and regeneration following injury. Finally, we will harness the genetic power of Drosophila to perform a comprehensive functional analysis of genes and pathways, those previously known and new ones that we will discover, in various neurite growth paradigms. Importantly, these functional assays will be performed in the same organism, allowing us to use identical genetic mutations across our analyses. To this end, our identification of a new genetic program regulating developmental axon regrowth, together with emerging tools in genomics, places us in a unique position to gain a broad understanding of axon growth during development and following injury.

2 000 000 €

Start date: 2014-03-01, End date: 2019-02-28

Next generation sequencing (NGS) is revolutionizing all fields of biological research but it fails to extract the full range of information associated with genetic material and is lacking in its ability to resolve variations between genomes. The high degree of genome variation exhibited both on the population level as well as between genetically “identical” cells (even in the same organ) makes genetic and epigenetic analysis on the single cell and single genome level a necessity. Chromosomes may be conceptually represented as a linear one-dimensional barcode. However, in contrast to a traditional binary barcode approach that considers only two possible bits of information (1 & 0), I will use colour and molecular structure to expand the variety of information represented in the barcode. Like colourful beads threaded on a string, where each bead represents a distinct type of observable, I will label each type of genomic information with a different chemical moiety thus expanding the repertoire of information that can be simultaneously measured. A major effort in this proposal is invested in the development of unique chemistries to enable this labelling. I specifically address three types of genomic variation: Variations in genomic layout (including DNA repeats, structural and copy number variations), variations in the patterns of chemical DNA modifications (such as methylation of cytosine bases) and variations in the chromatin composition (including nucleosome and transcription factor distributions). I will use physical extension of long DNA molecules on surfaces and in nanofluidic channels to reveal this information visually in the form of a linear, fluorescent “barcode” that is read-out by advanced imaging techniques. Similarly, DNA molecules will be threaded through a nanopore where the sequential position of “bulky” molecular groups attached to the DNA may be inferred from temporal modulation of an ionic current measured across the pore.

1 627 600 €

Start date: 2013-10-01, End date: 2018-09-30