Protein dynamics via computational microscope

Molecular dynamics simulations and analysis for bioinformatics undergraduate students

A computational biochemistry laboratory, fitted for bioinformatics students, is presented. The molecular dynamics package GROMACS is used to prepare and simulate a solvated protein. Students analyze the trajectory with different available tools (GROMACS and VMD) to probe the structural stability of the protein during the simulation. Students are also required to make use of Python libraries and write their own code to probe non-covalent interactions between the amino acid side chains. Based on these results, students characterize the system in a qualitatively approach but also assess the importance of each specific interaction through time. This work mobilizes biochemical concepts and programming skills, fostering critical thinking and group work and developing presenting skills.

Ok Google, how could I design therapeutics against prion diseases?

A number of previous successful attempts in the search for therapeutics for a variety of human pathologies highlight the importance of computational technologies in the drug discovery pipeline. This approach, often referred to as computer-aided drug design, is unfortunately inapplicable when the precise information regarding the three-dimensional structure of disease-associated proteins or the mechanism by which they are altered to generate misfolded isoforms are missing. A typical example is represented by prion diseases, fatal pathologies of the nervous system characterized by the conformational conversion of a physiological protein called PrP^C into a misfolded and infectious isoform referred to as PrP^Sc. Missing information regarding the atomic structure of PrP^Sc as well as the mechanism of templated conversion of PrP^C has severely halted the discovery of effective therapies for prion diseases. In this manuscript, we review emerging opportunities to apply computer-aided techniques to target PrP^C, PrP^Sc or to design inhibitors of prion replication, and discuss how these fast-evolving technologies could lay the groundwork for the application of entirely novel rational drug design schemes for these devastating pathologies.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.