A Community Roadmap for Scientific Workflows Research and Development

By |December 28, 2021|Categories: Publications|Tags: , |

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together.

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DeepRank: A deep learning framework for data mining 3D protein-protein interfaces

By |December 3, 2021|Categories: Publications|Tags: , , , |

Three-dimensional (3D) structures of protein complexes provide fundamental information to [...]

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GA4GH: International policies and standards for data sharing across genomic research and healthcare

By |November 10, 2021|Categories: Publications|Tags: , |

In this perspective, we present the GA4GH strategies for addressing the major challenges of the data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders

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The Impact of the HydroxyMethylCytosine epigenetic signature on DNA structure and function

By |November 8, 2021|Categories: Publications|Tags: |

In Eukaryotic cells, DNA epigenetic modifications play an important role in gene expression and regulation, and protein recognition. In this work we investigate the physical implications of cytosine 5-hydroxymethylation on DNA, its structural and flexibility differences with methylated and unmodified cytosine using molecular dynamics, biophysical experiments and NMR spectroscopy.

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Markov state models of proton- and pore-dependent activation in a pentameric ligand-gated ion channel

By |October 15, 2021|Categories: Publications|

Here, we used enhanced sampling to simulate the pH-gated channel GLIC, and construct Markov state models (MSMs) of gating. Consistent with new functional recordings, we report in oocytes, our analysis revealed differential effects of protonation and mutation on free-energy wells.

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MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

By |October 9, 2021|Categories: Publications|Tags: , , , |

We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs).

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