Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming

Gültaş, Mehmet; Düzgün, Güncel; Herzog, Sebastian; Jäger, Sven; Meckbach, Cornelia; Wingender, Edgar; Waack, Stephan

doi:10.1186/1471-2105-15-96

Cited by 10 publications

(16 citation statements)

References 69 publications

(110 reference statements)

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“…Only the non-binding sites of our training data are used to compute p nd . As we have seen in [32] and [36], transforming empirical amino acid distributions of MSA columns by a carefully designed doubly stochastic matrix is an effective way to integrate the binding site signals. To this end, we first set up a counting matrix C bind in a way similar to that of calculating the matrix C nd .…”

Section: Methodsmentioning

confidence: 99%

“…They were the first using JSD in this context and stated its superiority. Gültas et al [32] showed that the Jensen-Shannon divergence in the context of quantum information theory is of remarkable power. These three articles encouraged us to use JSD in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Compared with [32] and [36], we enhance the effect of transforming M's empirical column distributions by means of the doubly stochastic matrix D just defined. Let k be a column index of M. First, we compute the matrix product C (t) M k := C M k · D. Second, we add up all of C (t) M k 's rows.…”

Section: Methodsmentioning

confidence: 99%

“…For this aim, we introduce and evaluate a new information theory-based method for the prediction of these residues using Jensen-Shannon divergence (JSD). As a divergence measure based on the Shannon entropy, JSD is a symmetrized and smoothed version of the Kullback-Leibler divergence and is often used for different problems in the field of bioinformatics [31][32][33][34][35]. In this study, following the line of Capra et al [34] we first quantify the divergence between the observed amino acid distribution of a site in a protein and the background distribution of non-binding sites by using JSD.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, following the line of Capra et al [34] we first quantify the divergence between the observed amino acid distribution of a site in a protein and the background distribution of non-binding sites by using JSD. After that, in analogy to our previous studies QCMF [32] and CMF [36], we incorporate biochemical signals of binding residues in the calculation of JSD that results in the intensification of the DNA-binding residue signals from the non-binding signals.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Dang

Meckbach

Tacke

et al. 2016

Entropy

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Abstract:The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure-or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen-Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Dang

Meckbach

Tacke

et al. 2016

Entropy

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Insights from 20 years of bacterial genome sequencing

Land

Hauser

Jun

et al. 2015

Funct Integr Genomics

596

427

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Since the first two complete bacterial genome sequences were published in 1995, the science of bacteria has dramatically changed. Using third-generation DNA sequencing, it is possible to completely sequence a bacterial genome in a few hours and identify some types of methylation sites along the genome as well. Sequencing of bacterial genome sequences is now a standard procedure, and the information from tens of thousands of bacterial genomes has had a major impact on our views of the bacterial world. In this review, we explore a series of questions to highlight some insights that comparative genomics has produced. To date, there are genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. However, the distribution is quite skewed towards a few phyla that contain model organisms. But the breadth is continuing to improve, with projects dedicated to filling in less characterized taxonomic groups. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system provides bacteria with immunity against viruses, which outnumber bacteria by tenfold. How fast can we go? Second-generation sequencing has produced a large number of draft genomes (close to 90 % of bacterial genomes in GenBank are currently not complete); third-generation sequencing can potentially produce a finished genome in a few hours, and at the same time provide methlylation sites along the entire chromosome. The diversity of bacterial communities is extensive as is evident from the genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. Genome sequencing can help in classifying an organism, and in the case where multiple genomes of the same species are available, it is possible to calculate the pan- and core genomes; comparison of more than 2000 Escherichia coli genomes finds an E. coli core genome of about 3100 gene families and a total of about 89,000 different gene families. Why do we care about bacterial genome sequencing? There are many practical applications, such as genome-scale metabolic modeling, biosurveillance, bioforensics, and infectious disease epidemiology. In the near future, high-throughput sequencing of patient metagenomic samples could revolutionize medicine in terms of speed and accuracy of finding pathogens and knowing how to treat them.

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A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

Swaminathan,

Quaife,

Allan

2024

J Adv Model Earth Syst

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Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).

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Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming

Cited by 10 publications

References 69 publications

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Insights from 20 years of bacterial genome sequencing

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

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