Prediction of Protein Functional Specificity without an Alignment

Abstract

We propose a new approach to predict functional specificity of proteins from their amino acid sequences. Our approach is based on two things: structural Multilevel Neighborhoods of Atom (MNA) descriptors and an original Bayesian algorithm. Usually, a protein sequence is presented as a string of amino acid symbols. Here we introduce a new description of an amino acid sequence: a set of structural MNA descriptors. The MNA descriptor is a string describing an atom and its neighbor atoms according to the selected level. In this work, we also use description of a protein sequence as a set of peptides (strings of amino acid symbols). We performed a case study on two subsubclasses of enzyme nomenclature (EC). It is shown that B-statistics give a sufficient predictive power of enzyme specificity prediction for both MNA descriptors and peptides. We also showed that MNA descriptors give higher accuracy values in comparison with peptides and also provide a choice of MNA descriptor levels for best accuracy prediction. The highest average accuracy prediction that was achieved was 0.98.

Bayesian methods for proteomics

“…As described by Fomenko et al, MNA descriptors are strings depicting an atom and its neighbors. They are a new way of symbolizing a protein, containing information on protein structural features and thus are more informative than traditional amino acid sequences [35]. An MNA descriptor at level "0" is a single atom while one at level "1" is the atom and all atoms bonded to it by a single covalent bond.…”

“…An MNA descriptor at level "0" is a single atom while one at level "1" is the atom and all atoms bonded to it by a single covalent bond. Fomenko et al [35] showed that by using MNA descriptors and Bayesian methods together, accurate enzyme specificity predictions result.…”