Can field based chemistry help us to predict protein-DNA binding sites?
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This document discusses using computational methods to predict protein-DNA binding sites by analyzing DNA flexibility and electrostatics. It describes direct and indirect readout of DNA sequences by proteins, and how sequence-dependent flexibility can be recognized through an electric field-based approach. The document proposes using this approach to design small molecule drugs that bind DNA by targeting flexible regions. It outlines analyzing DNA sequences with software, aligning drug candidates to a target peptide, and growing fragments to expand candidates. The goal is to design larger molecules accounting for DNA bending to sufficiently hit multiple binding sites.
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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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Can field based chemistry help us to predict protein-DNA binding sites?
- 1. Can Field-Based Chemistry Help Us To Predict Protein-DNA Binding Sites? Daniel Barr, PhD Assistant Professor of Chemistry Utica College dabarr@utica.edu
- 2. Introduction and Motivation • The ABC’s of DNA – Direct vs. Indirect Readout – Field-based approach to understanding DNA – Electrostatics as a “shortcut” to dynamics? • Implications for Pharmaceuticals – Design small(-ish?) molecules to bind DNA – Utilize a variety of peptidomimetic backbones – Grow fragments to hit flexible/dynamic sites
- 3. Sequence-Specific Protein-DNA Binding • Direct readout recognizes the patterns of hydrogen bond donors/acceptors unique to each DNA sequence • Indirect readout recognizes the shape and/or dynamical flexibility of the DNA sequence – Can be sequence-dependent Rohs et. al. Ann.Rev.Biochem. 2010
- 4. Direct Readout Suzuki, Structure 1994
- 5. Sequence-Dependent Flexibility • In general YR tends to be most flexible • AT basepairs less context-dependent than CG Packer, Dauncey, Hunter JMB 2000 Lavery et al (ABC Consortium) Nucl Acid Res 2009 YR RY RRSpiriti et al (in preparation)
- 6. Protein-DNA Contacts • Q18, R22 (blue) are bidentate ligands: direct readout • Y7, Y17 (red) are monodentate: indirect readout Barr and van der Vaart, PCCP 2012
- 7. Analysis of DNA with Forge
- 10. Drug Design Strategy • DNA-binding drugs may need to be large-ish – It might not be sufficient to hit one or two key sites – Might need bigger molecules with specific geometries to account for DNA bending • Test different scaffolds as templates for peptidomimetic backbones Davis, Tsou, and Hamilton, Chem. Soc. Rev. 2007 Yap, et. al., Organic & Biomolecular Chem. 2012
- 11. Drug Design Step 1 • Use Forge to align models against the target peptide
- 12. Drug Design Step 1 • Use Forge to align models against the target peptide
- 13. Drug Design Step 1 • Use Forge to align models against the target peptide
- 14. Drug Design Step 2 • Use Spark to grow candidates to the target peptide
- 15. Ongoing/Future Work • Extend the DNA sequence analysis to all 39 nearest-neighbor combinations • Keep working on fragment growing for drug candidates • Docking studies?
- 16. Acknowledgements • Heather McManus and Daniel Bollen (DNA sequence comparisons) • Michael Convertino and Jade Bonsel (drug scaffold analyses) • Gabrielle Abbot (drug design and fragment growing) – Cresset Bio-Medical Discovery – National Science Foundation – Utica College