Abstract
BackgroundThe identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites.ResultsThe result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs.ConclusionsQCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF’s algorithm, we leveraged Compute Unified Device Architecture (CUDA).The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/.
Highlights
IntroductionIntroduction to Quantum InformationScience (Oxford Graduate Texts). New York: Oxford University Press Inc.; 2006.58
Introduction to Quantum InformationScience (Oxford Graduate Texts)
The results we present in this study show that the vast majority of Quantum Coupled Mutation Finder (QCMF)-significant residue sites are closely related to functionality and structural stability of both human epidermal growth factor receptor (EGFR) and GCK proteins. 10 significant residue sites in EGFR and 19 significant sites in GCK are established as functionally important since they are directly located at or close to catalytic sites, allosteric sites and binding sites which are crucial for maintaining protein functions and for understanding the underlying molecular mechanism
Summary
Introduction to Quantum InformationScience (Oxford Graduate Texts). New York: Oxford University Press Inc.; 2006.58. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures These measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. The first one consists of detectable highly conserved residue sites that are obviously important for the structure and/or the function of the protein; while the second one corresponds to compensatory (coupled) mutations between two or more residue sites that contain crucial information on the structural and functional basis of proteins [1]. The challenging problems in bioinformatics for the detection of significant compensatory mutation signals are: i) the minimization of the influence of phylogenetic relationships of protein sequences by incorporating physical or biochemical properties of amino acids in the calculation; ii) the separation of significant signals from the background noise or unrelated pair signals
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