Author: Vincent Voelz

Our new preprint just dropped on ChemRxiv:

Model selection using replica averaging with Bayesian inference of conformational populations.
Raddi RM, Marshall T, Ge Y, Voelz V. https://doi.org/10.26434/chemrxiv-2023-396mm

In this work, we describe and demonstrate with examples how equipping our BICePs algorithm with a replica-averaged forward model makes it a Maximum-Entropy reweighting method with some key advantages:

• It is a post-processing method that does not require restraints implemented inside simulations
• Heuristic estimates of uncertainty are not needed; posterior sampling enables learning uncertainties from the data
• BICePs has improved likelihood models to account for outliers
• The BICePs score can be used for objective model selection
• Automated force field and forward model parameterization can be performed using the BICePs score as an objective function.

I recently spoke about this work at the ACS Fall 2023 symposium “Emerging Techniques to Quantify Biomolecular Conformational Ensembles” symposium. 

Our new paper has been published in the Journal of Chemical Information and Modeling!

Raddi, Robert M., Yunhui Ge, and Vincent A. Voelz. “BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.” Journal of Chemical Information and Modeling 63, no. 8 (April 24, 2023): 2370–81. https://doi.org/10.1021/acs.jcim.2c01296.

Congrats to Rob Raddi on this paper and for coding this more user-friendly and extensible Python package: https://github.com/vvoelz/biceps

With five students presenting at BPS, the Voelz Lab made a strong showing at BPS 2023 in San Diego, CA, Feb 18-22! Dylan Novack gave an early-morning (yet still riveting 🙂 Platform Session talk on work simulating FOXO1 and the effects of mutations. Here are the presentations:

An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models
Tim Marshall, Robert Raddi, Vincent A. Voelz

Model selection using Bayesian inference of conformational populations with replica averaging
Robert M. Raddi, Tim Marshall, Yunhui Ge, and Vincent A. Voelz

ONCOGENIC MUTATIONS IN THE DNA-BINDING DOMAIN OF FOXO1 THAT DISRUPT FOLDING: QUANTITATIVE INSIGHTS FROM EXPERIMENTS AND MOLECULAR SIMULATION.
Dylan Novack
Platform Session Title: Platform: Protein Stability, Folding, and Chaperones

De novo design of beta hairpin peptides with custom installed halogen-bonding groups
Thi Dung Nguyen, Vincent Voelz

Computational fragment screening of dimeric SARS-CoV-2 Main Protease
Rashad Reid Jr., Vincent A. Voelz

Congratulations to Ph.D student Rob Raddi, a 2022 winner of the College of Science and Technology Outstanding Teaching Assistant (TA) at Temple University!    This prestigious college-level award attests to Rob’s excellence in teaching.  Great job and well deserved!

Our new paper “Multi-ensemble Markov Models to estimate affinities and rates of ligand binding is now published in the Journal of Chemical Physics.  Congrats to Yunhui Ge for getting this across the finish line!

This work examines a toy receptor made from an icosahedron of 11 Lennard-Jones particles. The ligand is a LJ particle, and all is solvated in explicit water. This system has number of encounter-complex states and a slow(ish) residence time of 30.3 ns. 

While conventional MSMs accurately estimate binding rates from swarms of short trajectories, they estimate affinities poorly.  Why? Because MSM estimators typically enforce detailed balance, assuming the data is sampled at equilibrium. 

So even with lots of parallel simulation, it still takes a long time for bound and unbound populations to equilibrate, and get a good model.  (Our related work explored this problem with adaptive seeding simulations.) 

Instead, what if we could quickly collect many binding/unbinding transitions in a biased ensemble, and use this information to infer populations and rates in the unbiased ensemble.   This is a job for multiensemble Markov models, or MEMMs!

To accelerate transitions, we scaled the ligand nonbonded interactions, as is typical in FEP calculations for drug discovery.   We tried two different MEMM estimators: (1) TRAM, from the Frank Noé group in Berlin, and (2) our Maximum-Caliber approach. 

We find that both work pretty well (!), but TRAM works slightly better.

These results have encouraged us to explore how this method works in more realistic ligand binding systems, as a potential tool for MD-based virtual screening. Expect more from our lab on this soon 🙂   

You can read more about this work here:

Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling. Ge, Yunhui, and Vincent A. Voelz.  The Journal of Chemical Physics 156, no. 13 (April 7, 2022): 134115. https://doi.org/10.1063/5.0088024

On April 5, Si Zhang successfully defended her PhD thesis “Computational Approaches for Protein Folding and Ligand Binding: from Thermodynamics to Kinetics”. Congratulations Si!

Congrats to undergrad researcher and MARC scholar Rashad Reid, who presented his poster “Structural and dynamical analysis of monomeric SARS-CoV-2 Mpro active site integrity” at the Spring 2022 Meeting of the American Chemical Society in San Diego this week.

Rashad was selected for both the ACS COMP session and the SCI-MIX. Great work, Rashad!

Congrats to Voelz Lab student Si Zhang for her modeling and simulation contributions to this exciting new methodology developed by the Ross Wang Lab at Temple. These new staples use a fluorine thiol-displacement reaction for cyclization, resulting in peptides that not only are more stable their folded conformations, but are also taken up by multiple kinds of cancer cells. From large-scale folding simulations of various staple designs, Si achieved good agreement with experimental and simulated peptide helicity, suggesting that folding simulations can be a predictive tool for peptide staple design.

You can read all about it in the latest issue of Nature Communications:

Unprotected Peptide Macrocyclization and Stapling via A Fluorine-Thiol Displacement Reaction. Md Shafiqul Islam, Samuel Junod, Si Zhang, Zakey Buuh, Yifu Guan, Mi Zhao, Kishan Kaneria, Parmila Kafley, Carson Cohen, Robert Maloney, Zhigang Lyu, Vincent A. Voelz, Weidong Yang, and Rongsheng Wang. Nature Communications 13, 350 (2022). https://doi.org/10.1038/s41467-022-27995-5