Protein Structure Prediction, Protein Folding and Protein Robustness


[Introduction] - [Contacts] - [Research] - [Publications] - [Projects] - [Events] - [Teaching] - [The Giants].


Proteins are polymer chains composed of 20 building blocks, the amino acids that function as the molecular processors of living organisms. In general, 40 to 1000 amino acids connect together to make one protein. Proteins are responsible for almost all functions in living organisms: such as enzymatic catalysis, storage and transport of material, RNA editing, organ development, antibodies and more.
The Protein Structure Prediction problem (PSP) is concerned with the prediction of the 3D structure of a protein given its sequence of amino acids. If we know the protein structure we can infer the protein function. The prediction of protein structure, hence, is a central problem in Science that remains largely unsolved.

   

One computational approach for predicting the 3D structure of a protein is concerned to minimize the energy function derived from physical-chemical and statistical considerations. The energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: bond atoms and non-bond atoms. Our research work shows experimentally that those types of interactions are in conflict, that is, this formidable optimization problem has been modelled trying to mimic the folding process itself. In fact, a multi-objective formulation of the PSP problem is introduced and its applicability studied.
Simply adding the bond and non-bond energies, as many energy functions do, leads to an overall energy landscape (the search space of a given computational problem) that does not perfectly correlate with what the protein 'sees' as it is folding up.


[Introduction] - [Contacts] - [Research] - [Publications] - [Projects] - [Events] - [Teaching] - [The Giants].


Copyright 2002-2012, Giuseppe Nicosia.
Giuseppe Nicosia, Optimization, Combinatorial Optimization, Numerical Optimization, Non Linear Optimization, Large Scale Optimization, Evolutionary Algorithms, Genetic Algorithms, Immune Algorithms, Artificial Immune Systems, Graph Coloring, String Folding, NP-complete problems, Circuit Optimization, Circuit Design, Design For Yield, Device Optimization, Bioinformatics, Structural Bioinformatics, Protein Folding, Protein Structure Prediction, HP model.