Computational protein design: the computer as a virtual laboratory
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Computational protein design: the computer as a virtual laboratory
CIC nanoGUNE Seminars
- Speaker
-
Ivan Coluzza, CIC biomaGUNE
- When
-
2017/11/13
12:00
- Place
- nanoGUNE seminar room, Tolosa Hiribidea 76, Donostia - San Sebastian
- Add to calendar
-
iCal
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![Computational protein design: the computer as a virtual laboratory](/++theme++dipc-theme/img/event_image_berdea.png)
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