Structure and Dynamics of Biomembranes containing Cholesterol and other Biologically-Important Sterols : a computational perspective

In this thesis, the differential effects of three closely-related sterols: ergosterol, cholesterol and lanosterol on the structural and dynamical properties of a model dipalmitoyl phosphatidylcholine (DPPC) membrane were examined using Molecular Dynamics (MD) simulations and Neutron Scattering (NS) calculations. As a necessary step towards realistic sterol:biomembrane simulations, molecular mechanics force field parameters for cholesterol, ergosterol and lanosterol, for the program package CHARMM are derived. Subsequently, MD simulations of hydrated sterol:DPPC lipid systems are performed at a biologically-relevant concentration (40\% mol.) at 309K and 323K. The simulations are compared with control simulations of the gel and liquid DPPC phases. All three sterols are found to order and condense the lipids relative to the liquid phase, but to markedly different degrees. Ergosterol is enhancing the packing of the lipids with each other and has a higher condensing effect on the membrane than the other two sterols. Moreover, ergosterol induces a higher proportion of trans lipid conformers, a thicker membrane and higher lipid order parameters, and is aligned more closely with the membrane normal. Ergosterol also positions itself closer to the bilayer:water interface. In contrast, lanosterol orders, straightens and packs the lipids less well, and is less closely aligned with the membrane normal. Furthermore, lanosterol lies closer to the relatively-disordered membrane center than do the other sterols. The behaviour of cholesterol in all the above respects is intermediate between that of lanosterol and ergosterol. The origins of the different membrane behavior upon addition of each sterol are discussed with respect to the sterol chemical differences. Ergosterol was also found to diffuse the slowest and cholesterol the fastest both in the xy-plane and the z-axis of the membrane among the three sterols studied. The findings here may explain why ergosterol is the most efficient of the three sterols at promoting the liquid-ordered phase and lipid domain formation, and may also furnish part of the explanation as to why cholesterol is evolutionarily preferred over lanosterol in higher-vertebrate plasma membranes

\u3cem\u3eIn Vitro\u3c/em\u3e Determination of Potency of Small Molecule Inhibitors of Arp2/3 Complex

Actin is a key protein building block of actin microfilaments, which are constructed and deconstructed in response to cellular signaling pathways to regulate cellular processes such as motility, division, and endocytosis. Arp2/3 Complex is a 7-subunit protein complex that is in involved in cellular construction of branched actin networks, functioning by attaching to the side of a pre-existing actin filament and nucleating a daughter branch. Overexpression of Arp2/3 complex has been linked to the ability of certain metastatic cancers to proliferate. This work describes the synthesis and in vitro biochemical testing of several molecules predicted by computational docking to be inhibitors of Arp2/3 Complex, and therefore of potential interest in clinical applications. A bulk actin polymerization assay is used as the key method to determine the potency of inhibitor candidates. Structure-activity relationships derived from these results are also discussed

Fostering global data sharing: Highlighting the recommendations of the Research Data Alliance COVID-19 working group

© 2020 Austin CC et al. The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community

Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM

Machine learning approaches in predicting allosteric sites

Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of Machine Learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of Artificial Intelligence such as protein Language Models

Unravelling the effect of the E545K mutation on PI3Kα kinase

PI3Ka controls several cellular processes and its aberrant signalling is implicated in tumorigenesis. One of its hotspot mutations, E545K, increases PI3Ka lipid kinase activity, but its mode of action is only partially understood. Here, we perform biased and unbiased molecular dynamics simulations of PI3Ka and uncover, for the first time, the free energy landscape of the E545K PI3Ka mutant. We reveal the mechanism by which E545K leads to PI3Ka activation in atomic-level detail, which is considerably more complex than previously thought

NanoCrystal: A Web-Based Crystallographic Tool for the Construction of Nanoparticles Based on Their Crystal Habit

Modeling nanoparticles is an essential first step to assess their capacity in different uses such as in energy storage or drug delivery. However, creating an initial starting conformation for modeling and simulation is tedious because every crystalline material grows with a different crystal habit. In this application note, we describe Nano-Crystal, a novel web-based crystallographic tool, which creates nanoparticle models from any crystal structure guided by their preferred equilibrium shape under standard conditions according to the Wulff morphology (crystal habit). Users can upload a cif file, define the Miller indices and their corresponding minimum surface energies according to the Wulff construction of a particular crystal, and specify the size of the nanocrystal. As a result, the nanoparticle is constructed and visualized, and the coordinates of the atoms are output to the user. Nano-Crystal can be accessed and used at http://nanocrystal.vi-seem.eu/.</p