Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

Paluszewski, Martin; Hamelryck, Thomas

doi:10.1186/1471-2105-11-126

Cited by 19 publications

(14 citation statements)

References 23 publications

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“…S4), for which ϕ, ψ angles, amino acid labels, secondary structure, peptide bond cis/trans information, and CA, CB, C, N, HA, and H chemical shift information was extracted. The model was trained using the stochastic EM algorithm in the Mocapy++ software package (33). The hidden node size was estimated by training models with varying number of hidden node components, and using the Bayesian information criterion for model selection (SI Appendix, Fig.…”

Section: Methodsmentioning

confidence: 99%

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Boomsma

Tian

Frellsen

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Methods of protein structure determination based on NMR chemical shifts are becoming increasingly common. The most widely used approaches adopt the molecular fragment replacement strategy, in which structural fragments are repeatedly reassembled into different complete conformations in molecular simulations. Although these approaches are effective in generating individual structures consistent with the chemical shift data, they do not enable the sampling of the conformational space of proteins with correct statistical weights. Here, we present a method of molecular fragment replacement that makes it possible to perform equilibrium simulations of proteins, and hence to determine their free energy landscapes. This strategy is based on the encoding of the chemical shift information in a probabilistic model in Markov chain Monte Carlo simulations. First, we demonstrate that with this approach it is possible to fold proteins to their native states starting from extended structures. Second, we show that the method satisfies the detailed balance condition and hence it can be used to carry out an equilibrium sampling from the Boltzmann distribution corresponding to the force field used in the simulations. Third, by comparing the results of simulations carried out with and without chemical shift restraints we describe quantitatively the effects that these restraints have on the free energy landscapes of proteins. Taken together, these results demonstrate that the molecular fragment replacement strategy can be used in combination with chemical shift information to characterize not only the native structures of proteins but also their conformational fluctuations.

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Section: Methodsmentioning

confidence: 99%

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Boomsma

Tian

Frellsen

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…These include both commercial and free general-purpose BN modeling software and BN-based classifiers. A more selective list of free packages, compared in the bioinformatics application context, is compiled in Paluszewski and Hamelryck (2010), with a special emphasis on the dynamic BNs (DBNs). pwOmics (Watcher and Beisbarth, 2015) is the most recent implementation of DBN modeling in omics data context.…”

Section: Existing Algorithms and Software Packagesmentioning

confidence: 99%

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

Gogoshin

Boerwinkle

Rodin

2017

Journal of Computational Biology

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Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology—type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types—single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.

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“…TorusDBN was implemented in the dynamic Bayesian network toolkit Mocapy (Paluszewski and Hamelryck, 2010), using a data set of more than 1400 protein structures taken from the sequence alignment benchmark mark database 2005 (van Walle et al. , 2005).…”

Section: Protein Structure Predictionmentioning

confidence: 99%

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

Mardia

2013

Journal of the Royal Statistical Society Series C: Applied Statistics

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Proteins are the workhorses of all living systems, and protein bioinformatics deals with analysis of protein sequences (one dimensional) and structures (three dimensional). The paper reviews statistical advances in three major active areas of protein structural bioinformatics: structure comparison, Ramachandran plots and structure prediction.These topics play a key role in understanding one of the greatest unsolved problems in biology, how proteins fold from one dimension to three dimensions, and have relevance to protein functionality, drug discovery and evolutionary biology. For each area, we give the biological background and review one of the main bioinformatics solutions to a specific problem in that area. We then present statistical tools recently developed to investigate these problems, consisting of Bayesian alignment, directional distributions and hidden Markov models. We illustrate each problem with a new case-study and describe what statistics can offer to these problems. We highlight challenges facing these areas and conclude with an overall discussion.

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Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

Cited by 19 publications

References 23 publications

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

Statistical Approaches to Three Key Challenges in Protein Structural Bioinformatics

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