Postdoctoral Fellow Maria A. Castellanos wins poster prize at Computational Medicinal Chemistry School for AlphaFold-based prediction of antiviral spectrum

Chodera lab Postdoctoral Fellow Maria A. Castellanos was awarded a poster prize at the Computational Medicinal Chemistry School held Oct 28-30, 2024 at the Novartis Institutes for Biomedical Research in Cambridge, MA.

Working with ASAP Antiviral Discovery Consortium computational chemistry lead Jenke Scheen and senior graduate student Alexander Payne, Maria has developed a pipeline aimed at predicting the breadth of antiviral activity for direct-acting small molecule antivirals, with the goal of prioritizing molecules for synthesis that maximize breadth of activity within a viral family. This pipeline leverages AlphaFold-like methods and high-throughput crystallographic data generated by the Diamond Light Source along with machine-learned affinity prediction to predict and score potential binding modes to the viral target across the viral family. The resulting pipeline will aid the $68M NIH-funded ASAP Consortium in developing broad-spectrum direct-acting antivirals to prevent future pandemics.

You can download the poster here: [PDF]
All code is open source: https://github.com/asapdiscovery/asapdiscovery

To learn more about Maria’s work, check out her LinkedIn.

Congratulations to TPCB graduate student Chaya Stern for being selected for a 2018 MolSSI Phase I Fellowship!

Congratulations to Tri-Institutional PhD Program in Chemical Biology (TPCB) graduate student and NSF GRFP recipient Chaya Stern on receiving a 2018 MolSSI Phase I Fellowship to support her work in developing new algorithms and open source software for Bayesian inference of force field parameters from experimental and quantum chemical data! You can learn more about Chaya's work in this area by listening to her PyData NYC 2017 talk or reading her MolSSI Fellowship Proposal, and hear more about what Chaya is up to by following her twitter feed.

Congratulations to Rafal Wiewiora on receiving a Cancer Research Horizon Award!

Rafal Wiewiora, a graduate student in the Tri-Institutional Program in Chemical Biology, has been awarded a DoD Peer Reviewed Cancer Research Horizon Award.  This two-year grant supports exceptional junior-level scientists pursuing research in cancer with the guidance of a faculty mentor. Rafal’s project investigates the conformational heterogeneity of histone methyltransferases using molecular dynamics simulations on the Folding@home platform, a worldwide distributed computing project where hundreds of thousands of people around the world contribute their computing power toward the understanding of cancer targets. These models of conformational dynamics will be vital in aiding the in silico design of selective chemical probes to understand the roles that these methyltransferases play in cancer, as well as to develop new strategies to inhibit them. A native of Poland, Rafal received his MChem degree from the University of Oxford. 

Combined computational and experimental approach to discovering selective inhibitors of EZH2 and SETDB1 that exploit differences in conformational reorganization energies to achieve selectivity.

Congratulations to Chaya Stern for receiving a Diversity Scholarship to attend SciPy 2017!

Congratulations to TPCB graduate student Chaya Stern for receiving a Diversity Scholarship to attend SciPy 2017 in Austin, TX July 10-16. SciPy is a conference focusing on scientific computing with Python, and brings together a community of open source software developers and users from industry, academia, and government to show off their projects, learn from each other, and collaborate to develop better code.

We're grateful to JumpTrading and NumFocus for providing the funds for this fellowship.

Postdoc Gregory Ross awarded Postdoctoral MolSSI Fellowship

Congratulations to postdoc Gregory Ross for being awarded an inaugural MolSSI Postdoctoral Software Science Fellowship! Greg's project focuses on building a toolbox of self-tuning Monte Carlo methods for use alongside molecular dynamics based Markov chain Monte Carlo (MCMC) sampling to greatly enhance the efficiency of molecular simulations and facilitate the construction of new sampling algorithms. Read on for a more detailed description of his project.

An automatic sampling toolbox for molecular simulation

Gregory Ross, DPhil
In the biomolecular simulation community, molecular dynamics (MD) is the most popular method for sampling molecular configurations. MD is well suited to capturing global, concerted movements, but it struggles to sample configurations that are separated by large energetic barriers. As a result, configurations taken from MD simulations tend to be from the same local region. An equivalent problem is encountered in Bayesian inference when drawing samples from a high-dimensional, multimodal posterior distribution. MD-related techniques are a small subset of a large class of Markov chain Monte Carlo (MCMC) techniques, and practitioners in both the biomolecular simulation and Bayesian inference communities have, in principle, a great deal of choice on which sampling method to use. However, it is difficult to know which MCMC method will the most efficient at generating uncorrelated samples for a particular system. In my project, I will develop a toolbox for MCMC that is agnostic with regard to what is being sampled, whether a protein conformation or posterior distribution. I will focus on using adaptive MCMC methods to help select the sampling technique that works best for a given problem. The general idea is to write a library of MCMC tools and a software interface that allows for the construction of MCMC “blocks” which can be mixed together and easily applied. The goal is to simplify sampling from difficult distributions and, ultimately, to widen the range of phenomena currently accessible by molecular simulation.

Congratulations to TPCB graduate student Mehtap Isik on being awarded the 2017-2018 Doris J. Hutchison Fellowship

Congratulations to TPCB graduate student Mehtap Isik, who is the 2017-2018 recipient of the Doris J. Hutchison Fellowship from the Sloan Kettering Division of the Weill Cornell Graduate School of Medical Sciences. Her project focuses on the development of model protein:ligand systems for advancing the field of predictive quantitative computational modeling for drug discovery using robotic wetlab experiments and advanced GPU computing, and is described in this proposal she submitted to the fellowship competition.