A three-state prediction of single point mutations on protein stability changes

BackgroundA basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (ΔΔG) upon single point mutation may also help the annotation process. The experimental ΔΔG values are affected by uncertainty as measured by standard deviations. Most of the ΔΔG values are nearly zero (about 32% of the ΔΔG data set ranges from −0.5 to 0.5 kcal/mole) and both the value and sign of ΔΔG may be either positive or negative for the same mutation blurring the relationship among mutations and expected ΔΔG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (ΔΔG<−1.0 kcal/mol), stabilizing mutations (ΔΔG>1.0 kcal/mole) and neutral mutations (−1.0≤ΔΔG≤1.0 kcal/mole).ResultsIn this paper a support vector machine starting from the protein sequence or structure discriminates between stabilizing, destabilizing and neutral mutations. We rank all the possible substitutions according to a three state classification system and show that the overall accuracy of our predictor is as high as 56% when performed starting from sequence information and 61% when the protein structure is available, with a mean value correlation coefficient of 0.27 and 0.35, respectively. These values are about 20 points per cent higher than those of a random predictor.ConclusionsOur method improves the quality of the prediction of the free energy change due to single point protein mutations by adopting a hypothesis of thermodynamic reversibility of the existing experimental data. By this we both recast the thermodynamic symmetry of the problem and balance the distribution of the available experimental measurements of free energy changes. This eliminates possible overestimations of the previously described methods trained on an unbalanced data set comprising a number of destabilizing mutations higher than stabilizing ones.

[1]  Piero Fariselli,et al.  I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure , 2005, Nucleic Acids Res..

[2]  M. Michael Gromiha,et al.  CUPSAT: prediction of protein stability upon point mutations , 2006, Nucleic Acids Res..

[3]  Dietmar Schomburg,et al.  Structural analysis and prediction of protein mutant stability using distance and torsion potentials: Role of secondary structure and solvent accessibility , 2006, Proteins.

[4]  T L Blundell,et al.  Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. , 1997, Protein engineering.

[5]  S J Wodak,et al.  Contribution of the hydrophobic effect to protein stability: analysis based on simulations of the Ile-96----Ala mutation in barnase. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Piero Fariselli,et al.  Predicting Free Energy Contribution to the Conformational Stability of Folded Proteins From the Residue Sequence with Radial Basis Function Networks , 1995, ISMB.

[7]  Hongyi Zhou,et al.  Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction , 2002, Protein science : a publication of the Protein Society.

[8]  Piero Fariselli,et al.  A neural-network-based method for predicting protein stability changes upon single point mutations , 2004, ISMB/ECCB.

[9]  L. Serrano,et al.  Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. , 2002, Journal of molecular biology.

[10]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[11]  Piero Fariselli,et al.  Predicting protein stability changes from sequences using support vector machines , 2005, ECCB/JBI.

[12]  P. Kollman,et al.  Exhaustive mutagenesis in silico: Multicoordinate free energy calculations on proteins and peptides , 2000, Proteins.

[13]  K. Takano,et al.  Are the parameters of various stabilization factors estimated from mutant human lysozymes compatible with other proteins? , 2001, Protein engineering.

[14]  Arlo Z. Randall,et al.  Prediction of protein stability changes for single‐site mutations using support vector machines , 2005, Proteins.

[15]  Marianne Rooman,et al.  PoPMuSiC, rationally designing point mutations in protein structures , 2002, Bioinform..

[16]  Akinori Sarai,et al.  ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions , 2005, Nucleic Acids Res..

[17]  D Gilis,et al.  Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence. , 1997, Journal of molecular biology.