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BITS2007 Meeting
BITS2007 Meeting



26-28 April 2007 Napoli, Italy

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The effect of mutations on protein stability changes: a three class pair residue-discrimination study
 
Motivation
A basic question in protein structural studies is to which extent mutations affect
protein folding stability. This can be asked starting from protein sequence and/or
from structure. In proteomics and genomics studies prediction of protein stability
free energy change (DDG) upon single point mutation may also help the annotation
process. So far methods to address this problem are based on to two different
approaches: development of optimised and different energy functions (Gilis and
Rooman, 1997; Guerois et al., 2002; Zhou and Zhou, 2002) for proteins whose structure
is known and implementations of machine learning approaches when sequences and/or
structures are available (Capriotti et al., 2004; Capriotti et al 2005a; Capriotti et
al 2005b; Cheng et al., 2006). On the other hand experimental DDG values are affected
by uncertainty as measured by standard deviations. Most of the DDG values are nearly
zero (about 32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol); furthermore
both the value and sign of DDG may be either positive or negative for the same
mutation blurring the relationship among mutations and expected DDG value. In order
to overcome this problem we describe a new predictor that discriminates between 3
mutation classes: destabilizing mutations, stabilizing mutations and neutral
mutations, being neutral mutations all those substitutions whose effect is to promote
a protein stability free energy change (DDG) ranging from -0.5 to 0.5 Kcal/mol.


Methods
In this paper a support vector machine (SVM) starting from the protein sequence or
structure discriminates between stabilizing, destabilizing and neutral mutations. 
The machine learning method here presented was trained and tested considering
experimental data selected from the new release of the ProTherm Database (Kumar et
al. 2006). We collect more than 1600 mutations and according to the criterion of
thermodynamic reversibility for each mutation, we double all the thermodynamic data.
Finally, we end up with more than 3200 mutations to train our method with a
cross-validation procedure. Following other previous works of ours (Capriotti et al
2005a; Capriotti et al 2005b) two different SVM methods were developed depending on
the provided information. If the predictions are sequence-based an input vector of 42
elements is used. The input accounts for the residue mutation (encoded in the first 20
elements), the sequence environment (encoded in second 20 elements) and experimental
conditions (temperature and pH reported in the last two element). When the 3D
structure of the mutated proteins is known is it also possible to perform the
prediction with the structure-based method that considers an input vector of 43
elements. The input vector is similar to that of the sequence-based predictor:
however the 20 element vector encoding for the sequence environment is replaced
considering the structural environment and adding also one  element accounting for
the relative solvent accessible area is added.


Results
We rank all the possible substitutions according to a three class-classification
system that aside prediction indicates also the rate of occurrence of the mutations
in the data base and the list of proteins where the mutation effect has been
experimentally detected. We show that the overall accuracy of our predictor is as
high as 52% when performed starting from sequence information and 58% when the
protein structure is available with a mean value correlation coefficient of 0.30 and
0.39 respectively. These values are about 20 points per cent higher than those of a
random predictor; when selecting only the mutations with high effect on the protein
stability (|DDG|>0.5 Kcal) the prediction of the destabilizing and stabilizing
mutations are well balanced and reaches the accuracy values of 71% and 76% with
correlation coefficient of 0.43 and 0.52, respectively when sequence-based and
structure-based predictions are provided.
 
Id: 115
Place: Napoli, Italy
Centro Congressi "Federico II"
Via Partenope 36
Napoli
Starting date:
28-Apr-2007   13:30
Duration: 20'
Contribution type: Oral
Primary Authors: CAPRIOTTI, Emidio (Structural Genomic Unit, Department of Bioinformatics, Centro de Investigacion Principe Felipe (CIPF) Valencia, Spain)
Co-Authors: FARISELLI, Piero (Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy)
ROSSI, Ivan (Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy; BioDec Srl, via Fanin 48, 40127 Bologna, Italy)
CASADIO, Rita (Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy)
Presenters: CAPRIOTTI, Emidio
 
Included in session: Session 7: Structural biology and drug design
Included in track: Structural biology and drug design
 




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