Amino acid features: a missing compartment of prediction of protein function

Abstract

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 modeling 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, has 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 attributes 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 EMC can vary from 0.0% to 100%, depending upon which attribute weighting algorithm had summarized the attributes of the dataset prior to running the clustering algorithm.In another recent study on halostability, the results showed that amino acid composition can be used to efficiently discriminate halostable protein groups with up to 98% accuracy implying the possibility of precise prediction of halostability when an appropriate machine learning algorithm mines a large number of structural amino acid attributes of primary protein structure.Using our approach, simple amino acid features, without the need of advanced features of protein structure, could explain the difference between P1B-ATPases in hyperaccumulator and nonhyperaccumulator plants. More importantly, a precise model was built to discriminate P1B-ATPases in different organisms based on their structural amino acid features. In addition, for the first time, reliable models for prediction of the hyperaccumulating activity of unknown P1B-ATPase pumps were developed.We employed our method in monitoring and prediction of breast cancer. The results confirmed that amino acid composition can be used to discriminate between protein groups expressed in two forms of breast cancer: malignant and benign. This study was strong evidence that malignancy can be predicted out from amino acid, and malignant proteins can be distinguished based on the amino acid composition of their proteomes without further need for protein separation. An important outcome was the discovery of the role of dipeptides, in particular Ile-Ile, in cancer progression. In addition, Generalized Rule Induction (GRI) found association rules in the data showing the 100 most important rules classifying benign, malignant, and commonly expressed proteins expressed in breast cancers.In another investigation, we found that EST-SSRs in normal lung tissues are different than in unhealthy tissues, and tagged ESTs with SSRs cause remarkable differences in amino acid and protein expression patterns in cancerous tissue. This can be supposed as a glimpse of invention of a new sort of biomarkers based on frequency of amino acids.Up to now, phylogenic trees, drawn by nucleic acid or amino acid sequence alignments, have been employed as the base of evolutionary studies. However, this method does not take into account the structural and functional features of sequences during evolution. On the contrary, the presented classification here, based on the decision tree, anomaly detection model and feature weighting, provides an evolutionary separation of organisms based on their structural reasons of this diversity.Our findings have the potential to be efficiently used in the following area: filling the gap between laboratory engineering of proteins and computational biology, developing amino acid feature based-biomarkers, increasing the accuracy of prediction of 3D protein structure based on important amino acid features, and developing websites/software for prediction of the results of mutation. In addition, important discovered amino acid features can be employed as clues for discovering important DNA mutations and increasing prediction accuracy of 3D structure from DNA sequence. Furthermore, this study offers new for protein function, irrespective of similarity searches.