The paper describes a new method for the analysis of protein sequences - the method of analysis of the information structure (ANIS method). The method uses a new approach to describe amino acid sequences and identify hierarchically organized elements in the information structure of protein sequences. It was shown that the top-level information structure elements correspond to topologically stable elements of the three-dimensional structure (structural domains). A new approach for the identification of functionally important protein fragments was proposed based on the ANIS method. The approach was tested in the protein engineering experimental studies. Functionally important fragments of heat shock protein (hHSP70), human tumor necrosis factor (hTNF) and protein gp181 from phage φKZ were obtained. The proposed approach can be used for de novo protein design.
Institute of Physics Belgrade, 2014, SERBIA ISBN: 978-86-82441-40-3

Methods for protein modeling and design advanced rapidly in recent years. At the heart of these computational methods is an energy function that calculates the free energy of the system. Many of these functions were also developed to estimate the consequence of mutation on protein stability or binding affinity. In the current study, we chose six different methods that were previously reported as being able to predict the change in protein stability (ddG) upon mutation: CC/PBSA, EGAD, FoldX, I-Mutant2.0, Rosetta and Hunter. We evaluated their performance on a large set of 2156 single mutations, avoiding for each program the mutations used for training. The correlation coefficients between experimental and predicted DeltaDeltaG values were in the range of 0.59 for the best and 0.26 for the worst performing method. All the tested computational methods showed a correct trend in their predictions, but failed in providing the precise values. This is not due to lack in precision of the experimental data, which showed a correlation coefficient of 0.86 between different measurements. Combining the methods did not significantly improve prediction accuracy compared to a single method. These results suggest that there is still room for improvement, which is crucial if we want forcefields to perform better in their various tasks.