Protein Structure Prediction, Protein Folding and Protein Robustness
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.
- V. Cutello, G. Nicosia, M. Pavone, I. Prizzi,
"Protein Multiple Sequence Alignment by Hybrid Bio-Inspired Algorithms",
Nucleic Acids Research - Oxford Journals, doi:10.1093/nar/gkq1052.
- G.Stracquadanio, G. Nicosia,
"Computational Energy-based Redesign of Robust Proteins",
Computers and Chemical Engineering, doi:10.1016/j.compchemeng.2010.04.005.
- G. Nicosia, G.Stracquadanio,
Generalized Pattern Search Algorithm for Peptide Structure Prediction",
Biophysical Journal, 95(10):4988-4999, 2008.
- V. Cutello, G. Nicosia, M. Pavone, J. Timmis,
An Immune Algorithm for Protein Structure Prediction on Lattice Models",
IEEE Transactions on Evolutionary Computation, 11(1):101-117, 2007.
- A. M. Anile, V. Cutello, G. Narzisi, G. Nicosia, S. Spinella,
Determination of protein structure and dynamics combining immune algorithms and pattern search methods",
- V. Cutello, G. Narzisi, G. Nicosia,
A Multi-Objective Evolutionary Approach to the Protein Structure Prediction Problem",
Journal of the Royal Society Interface, Royal Society Publications London,
Download i-paes Algorithm code (C language).
A review paper published by the Journal of the Royal Society Interface,
G. Helles (2008; 5(21): 387--396) ranks our I-PAES algorithm among the best state-of-art folding algorithm;
in fact, I-PAES is the 3rd-4th best folding algorithm.
Copyright 2002-2012, 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.