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 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. 2 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 proteins 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 to protein separation. An important outcome was discovery the role of dipeptides, in particular Ile-Ile, in cancer progression. In addition, Generalized Rule Induction (GRI) found association rules found in the data showing 100 most important rules classifying benign, malignant, and common 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 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 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 Our findings have the potential to be efficiently used in the following area: filling the gap between laboratory engineering of proteins an...

Unravelling and Quantifying the Biophysical– Biochemical Descriptors Governing Protein Thermostability by Machine Learning

“…Our results of higher propensities of polar amino acids of Gln and Ser are consistent with the studies reported in literature. [79] Several other studies [80][81][82] have also shown that amino acid compositions, especially polar amino acids (Gln, Ser) play an important role in enhancing molecular hydrophilicity and impart recognizable effects on thermostability of proteins.…”