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In new paper, grad student Rob Raddi uses Deep Gaussian Processes to improve pKa predictions in the SAMPL7 challenge: https://rdcu.be/ctjkk https://doi.org/10.1007/s10822-021-00411-8… Nice work Rob!

Over the last few years, our lab has been working on a Bayesian inference method we call BICePs to reconcile simulations of conformational ensembles with experimental measurements. BICePs has several key advantages over other existing algorithms, and we have applied it in lots of different ways to model the conformational properties of peptides, macrolides and peptidomimetics (like peptoids). In this new review paper, we discuss the theory behind BICePs, give an overview of the many applications so far, and discuss future improvements soon to come!

Read all about BICePs in our new review paper here: “Reconciling simulations and experiments with BICePs: a review.” Vincent A. Voelz, Yunhui Ge and Robert M. Raddi. (2021) Frontiers in Molecular Biosciences: Biological Modeling and Simulation. https://doi.org/10.3389/fmolb.2021.661520

If you want to try it out for yourself, see our GitHub repo: https://github.com/vvoelz/biceps

In new work published in Journal of Chemical Information and Modeling, Ge et al. use molecular simulation study the binding mechanism of four designed cyclic β-hairpin ligands of the MDM2 receptor. These peptides, first characterized by the Mate Erdelyi group at the University of Uppsala, show different amounts of conformational flexibility in solution, and a range of binding affinities. By constructing Markov State Models (MSMs) of these peptides in solution, and binding to MDM2, we strikingly find that the difference in binding affinity can be entirely explained by the extent of preorganization (i.e. folding propensity) of these peptides in solution. This work has big implications for the possibility of designing solution-state folding properties to enhance the binding affinity of peptidomimetics.

We also show improved estimates of slow dissociation rates by using biased simulations (umbrella sampling) along with Multi-ensemble Markov models (MEMMs).

Read more in our paper “Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2″ here: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00029

Congrats to Yunhui on our new book chapter “Markov State Models to Elucidate Ligand Binding Mechanism”, now available in MIMB: Protein-Ligand Interactions and Drug Design, vol 2266 (Humana Press) https://doi.org/10.1007/978-1-0716-1209-5_14

Congrats to Matt Hurley and the multi-disciplinary team of Temple researchers from the Schafmeister, Zdilla, and Baxter groups on this new manuscript describing Metal-Binding Q-Proline Macrocycles. As Matt shows in a companion theory paper (still in review, stay tuned), these highly-functionalizable molecules slowly pre-organize in the presence of cations to an all-trans amide conformation. Interestingly, a solid-state racemic crystal structure obtained by first author JD Northrup and team does NOT show all all-trans backbone, underscoring the need to model solution-state conformational properties.

Metal-Binding Q-Proline Macrocycles
Justin D. Northrup, Jesse A. Wiener, Matthew F. D. Hurley, Chun-Feng David Hou, Taylor M. Keller, Richard H. G. Baxter, Michael J. Zdilla, Vincent A. Voelz, and Christian E. Schafmeister
J. Org. Chem. 2021, 86, 6, 4867–4876
doi:10.1021/acs.joc.1c00116

Folding@home has teamed up with Amazon Web Services’ (AWS) Disaster Response Program to publicly release all of our COVID-19 protein simulation data as an AWS Open Data Set. These data, which include COVID Moonshot absolute free energy calculations performed by our lab, represent one of the largest databases of trajectory data ever simulated. We hope access to this these results will continue to help researchers’ understanding of the SARS-CoV-2 viral proteome, and the search for new therapies.

You can access the data here: https://registry.opendata.aws/foldingathome-covid19

Huge kudos to graduate student Matt Hurley in our lab for collecting and releasing this huge amount of data!

Our lab has just been granted 192,500 node hours on the TACC Stampede2 (Dell/Intel Knights Landing, Skylake) supercomputer. This 6-month allocation (TG-MCB200214) will be a huge boon to our research efforts in Markov Model methods, modeling PAH activation, and much more. Thanks to everyone who helped with benchmarking, especially Tim and Jason!

Congrats to Matt Hurley, who presented an initial analysis of our COVID-19 related work, “Massively parallel absolute free energy perturbation calculations on Folding@home to screen potential SARS-CoV-2 protease inhibitors”, at the Fall 2020 (Virtual) Meeting of the American Chemical Society.

Although our analysis is still ongoing, Matt’s work takes an initial look at the ability of large-scale FEP simulations to screen possible inhibitors of the SARS-CoV-2 main protease (Mpro), many of which were designed as part of the open-science COVID Moonshot project. Matt found that simulations can indeed enrich for good binders, and correctly predict the binding poses seen in co-crystal structures that were later solved for inhibitors that were later experimentally tested.

Stay tuned for more updates on this work, which will be coordinated with a public release of data we have generated on Folding@home!

Congrats to Yunhui Ge on getting his newly minted PhD! On April 13, he successfully defended of his thesis, “Using molecular dynamic simulations and statistical models to understand biomolecular conformational dynamics.” Dr. Yunhui Ge will soon be off to sunnier vistas in California to pursue postdoctoral work.

On April 13-14, Shahlo Solieva presented her work, “Using Molecular Simulation to Understand the Role of Conserved Residues in an Extremophilic β-galactosidase,” at the Symposium for Undergraduate Research and Creativity. It is a distinct honor to be selected for this University-wide showcase of top undergraduate work.

Due to social distancing, the symposium was held virtually. The upshot: you can see Shahlo’s talk online at https://voicethread.com/share/14064486/