Contribution to the prediction of the fold code: application to immunoglobulin and flavodoxin cases.
BACKGROUND
Folding nucleus of globular proteins formation starts by the mutual interaction of a group of hydrophobic amino acids whose close contacts allow subsequent formation and stability of the 3D structure. These early steps can be predicted by simulation of the folding process through a Monte Carlo (MC) coarse grain model in a discrete space. We previously defined MIRs (Most Interacting Residues), as the set of residues presenting a large number of non-covalent neighbour interactions during such simulation. MIRs are good candidates to define the minimal number of residues giving rise to a given fold instead of another one, although their proportion is rather high, typically [15-20]% of the sequences. Having in mind experiments with two sequences of very high levels of sequence identity (up to 90%) but different folds, we combined the MIR method, which takes sequence as single input, with the "fuzzy oil drop" (FOD) model that requires a 3D structure, in order to estimate the residues coding for the fold. FOD assumes that a globular protein follows an idealised 3D Gaussian distribution of hydrophobicity density, with the maximum in the centre and minima at the surface of the "drop". If the actual local density of hydrophobicity around a given amino acid is as high as the ideal one, then this amino acid is assigned to the core of the globular protein, and it is assumed to follow the FOD model. Therefore one obtains a distribution of the amino acids of a protein according to their agreement or rejection with the FOD model.


RESULTS
We compared and combined MIR and FOD methods to define the minimal nucleus, or keystone, of two populated folds: immunoglobulin-like (Ig) and flavodoxins (Flav). The combination of these two approaches defines some positions both predicted as a MIR and assigned as accordant with the FOD model. It is shown here that for these two folds, the intersection of the predicted sets of residues significantly differs from random selection. It reduces the number of selected residues by each individual method and allows a reasonable agreement with experimentally determined key residues coding for the particular fold. In addition, the intersection of the two methods significantly increases the specificity of the prediction, providing a robust set of residues that constitute the folding nucleus.
Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland.
Mateusz
M
Banach
Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France.
Nicolas
N
Prudhomme
Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France; RPBS, 35 rue Hélène Brion, 75013, Paris, France.
Mathilde
M
Carpentier
Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France; RPBS, 35 rue Hélène Brion, 75013, Paris, France.
Elodie
E
Duprat
Genetics Department, Agricultural University of Athens, Iera Odos 75, Athens, Greece.
Nikolaos
N
Papandreou
Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland.
Barbara
B
Kalinowska
Protein Structure Prediction group, IMPMC, UPMC & CNRS, Paris, France; RPBS, 35 rue Hélène Brion, 75013, Paris, France.
Jacques
J
Chomilier
Department of Bioinformatics and Telemedicine, Medical College, Jagiellonian University, Krakow, Poland.
Irena
I
Roterman
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001419:Bacteria / Q000737:chemistry / Q000378:metabolism; D001665:Binding Sites; D005418:Flavodoxin / Q000737:chemistry*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D007136:Immunoglobulins / Q000737:chemistry*; D008958:Models, Molecular; D009010:Monte Carlo Method; D017510:Protein Folding; D017433:Protein Structure, Secondary
D005418:Flavodoxin; D007136:Immunoglobulins