Nature Precedings Title Amino acid features: a missing compartment of prediction of protein function

Enormous computational efforts have been carried out to predict structure and function of protein. However, nearly all of these efforts have been focused on prediction of function based on primary nucleic acid sequence or modelling 3D structure of protein from its nucleic acid sequence. In fact, it seems that amino acid attributes, which is an intermediate phase between DNA/RNA and advanced protein structure, have been missed. From 2010, we examined the possibility of precise prediction of structural protein function based on amino acid features by improving the following three aspects of amino acid research: (1) Increasing the number of computationally calculated amino acid features, (2) Testing different feature selection (attribute weighting) algorithms and selection of the most important amino acid attribute based on the overall conclusion of algorithms, (3) Examining different supervised and unsupervised data mining (machine learning) algorithms, and (4) Joining attribute weighting with different data mining algorithms. We applied the discovered procedure in different biological examples including: protein thermostability, halostability, prediction of function of heavy metal transporters, cancer diagnosis and prediction, and pursuing the EST-SSRs in amino acid level. In thermostability study, we successfully established an accurate expert system to predict the thermostability of any input sequence trough mining of its calculated amino acid features. Interestingly, performance of a clustering algorithm such as N at ur e P re ce di ng s : d oi :1 0. 10 38 /n pr e. 20 11 .6 69 3. 1 : P os te d 13 D ec 2 01 1