From Inhibition to Degradation: Targeting the Antiapoptotic Protein Myeloid Cell Leukemia 1 (MCL1).

Protein-protein interactions (PPIs) have emerged as significant targets for therapeutic development, owing to their critical nature in diverse biological processes. An ideal PPI-based target is the protein myeloid cell leukemia 1 (MCL1), a critical prosurvival factor in cancers such as multiple myeloma where MCL1 levels directly correlate to disease progression. Current strategies for halting the antiapoptotic properties of MCL1 revolve around inhibiting its sequestration of proapoptotic factors. Existing inhibitors disrupt endogenous regulatory proteins; however, this strategy actually leads to an increase of MCL1 protein levels. Here, we show the development of hetero-bifunctional small molecules capable of selectively targeting MCL1 using a proteolysis targeting chimera (PROTAC) methodology leading to successful degradation. We have confirmed the involvement of the E3 ligase CUL4A-DDB1 cereblon ubiquitination pathway, making these PROTACs a first step toward a new class of antiapoptotic B-cell lymphoma 2 family protein degraders.

The emerging role of physical modeling in the future of structure determination.

Biomolecular structure determination has long relied on heuristics based on physical insight; however, recent efforts to model conformational ensembles and to make sense of sparse, ambiguous, and noisy data have revealed the value of detailed, quantitative physical models in structure determination. We review these two key challenges, describe different approaches to physical modeling in structure determination, and illustrate several successes and emerging technologies enabled by physical modeling.

Online Optimization of Total Acceptance in Hamiltonian Replica Exchange Simulations.

Replica exchange is a widely used sampling strategy in molecular simulation. While a variety of methods exist to optimize parameters for temperature replica exchange, less is known about how to optimize parameters for more general Hamiltonian replica exchange simulations. We present an algorithm for the online optimization of total acceptance for both temperature and Hamiltonian replica exchange simulations using stochastic gradient descent. We optimize the total acceptance, a heuristic objective function capturing the efficiency of replica exchange. Our approach is general and has several desirable properties, including: (1) it makes few assumptions about the system of interest, (2) optimization occurs online without the requirement of presimulation, and (3) most importantly, it readily generalizes to systems where there are multiple control parameters (e.g., temperatures, force constants, etc.) that determine the Hamiltonian of each replica. We explore some general properties of the algorithm on a simple harmonic oscillator system, and demonstrate its effectiveness on a more complex data-guided protein folding simulation.