Computational protein design: the computer as a virtual laboratory

CIC nanoGUNE Seminars

Ivan Coluzza, CIC biomaGUNE
nanoGUNE seminar room, Tolosa Hiribidea 76, Donostia - San Sebastian
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Computational protein design: the computer as a virtual laboratory Proteins are one of the most versatile modular assembling systems in nature. Experimentally more than 110 thousand proteins structures have been identified, and more are deposited every day in the Protein Data Bank. Such an enormous structural variability is controlled, in first approximation, by the sequence of amino acid along the peptide chain of each protein. Understanding how the structural and functional properties of the target can be encoded in the sequence of amino acids is the primary objective of Protein Design. One of the most remarkable features of proteins is the fact that a significant amount of information is analogically encoded with an alphabet of just ~20 letters. The use of such a limited set has the advantage that new target structures can be designed (e.g. through evolution) by just changing the orders of the elements along the chain. Moreover, by degrading chains that do not fulfil their purpose, waste in the form of the isolated residues can be efficiently recycled for new chains. Incidentally, this is why living organism can eat each other and use their building blocks for themselves. Unfortunately, Protein Design remains one of the principal challenges across the disciplines of Biology, Physics and Chemistry. The implication of solving such a problem is enormous and branches into material science, drug design, evolution and even cryptography. For instance, in the field of Drug design an efficient computational method to design protein-based ligands for biological targets such as viruses, bacteria or tumours cells, could give a significant boost to the development of new therapies with reduced side effects. In material science, self-assembling is a highly desired property, and soon artificial proteins could represent a new class of designable self-assembling materials. In this presentation, I will introduce the computational approach that and show how it can be used to learn from biological systems to design novel synthetic substances. Inversely by facing and solving the caveats of the bottom-up design of self-assembling materials, understand the importance of the features present in natural systems. **Host** : D. De Sancho