Showing papers by "Michael Feig published in 2023"

Varying molecular interactions explain aspects of crowder-dependent enzyme function of a viral protease

Abstract: Biochemical processes in cells, including enzyme-catalyzed reactions, occur in crowded conditions with various background macromolecules occupying up to 40% of cytoplasm’s volume. Viral enzymes in the host cell also encounter such crowded conditions as they often function at the endoplasmic reticulum membranes. We focus on an enzyme encoded by the hepatitis C virus, the NS3/4A protease, which is crucial for viral replication. We have previously found experimentally that synthetic crowders, polyethylene glycol (PEG) and branched polysucrose (Ficoll), differently affect the kinetic parameters of peptide hydrolysis catalyzed by NS3/4A. To gain understanding of the reasons for such behavior, we perform atomistic molecular dynamics simulations of NS3/4A in the presence of either PEG or Ficoll crowders and with and without the peptide substrates. We find that both crowder types make nanosecond long contacts with the protease and slow down its diffusion. However, they also affect the enzyme structural dynamics; crowders induce functionally relevant helical structures in the disordered parts of the protease cofactor, NS4A, with the PEG effect being more pronounced. Overall, PEG interactions with NS3/4A are slightly stronger but Ficoll forms more hydrogen bonds with NS3. The crowders also interact with substrates; we find that the substrate diffusion is reduced much more in the presence of PEG than Ficoll. However, contrary to NS3, the substrate interacts more strongly with Ficoll than with PEG crowders, with the substrate diffusion being similar to crowder diffusion. Importantly, crowders also affect the substrate-enzyme interactions. We observe that both PEG and Ficoll enhance the presence of substrates near the active site, especially near catalytic H57 but Ficoll crowders increase substrate binding more than PEG molecules.

One particle per residue is sufficient to describe all-atom protein structures

Abstract: Atomistic resolution is considered the standard for high-resolution biomolecular structures, but coarse-grained models are often necessary to reflect limited experimental resolution or to achieve feasibility in computational studies. It is generally assumed that reduced representations involve a loss of detail, accuracy, and transferability. This study explores the use of advanced machine-learning networks to learn from known structures of proteins how to reconstruct atomistic models from reduced representations to assess how much information is lost when the vast knowledge about protein structures is taken into account. The main finding is that highly accurate and stereochemically realistic all-atom structures can be recovered with minimal loss of information from just a single bead per amino acid residue, especially when placed at the side chain center of mass. High-accuracy reconstructions with better than 1 Å heavy atom root-mean square deviations are still possible when only Cα coordinates are used as input. This suggests that lower-resolution representations are essentially sufficient to represent protein structures when combined with a machine-learning framework that encodes knowledge from known structures. Practical applications of this high-accuracy reconstruction scheme are illustrated for adding atomistic detail to low-resolution structures from experiment or coarse-grained models generated from computational modeling. Moreover, a rapid, deterministic all-atom reconstruction scheme allows the implementation of an efficient multi-scale framework. As a demonstration, the rapid refinement of accurate models against cryoEM densities is shown where sampling at the coarse-grained level is guided by map correlation functions applied at the atomistic level. With this approach, the accuracy of standard all-atom simulation based refinement schemes can be matched at a fraction of the computational cost. STATEMENT OF SIGNIFICANCE The fundamental insight of this work is that atomistic detail of proteins can be recovered with minimal loss of information from highly reduced representations with just a single bead per amino acid residue. This is possible by encoding the existing knowledge about protein structures in a machine-learning model. This suggests that it is not strictly necessary to resolve structures in atomistic detail in experiments, computational modeling, or the generation of protein conformations via neural networks since atomistic details can inferred quickly via the neural network. This increases the relevance of experimental structures obtained at lower resolutions and broadens the impact of coarse-grained modeling.

The effect of polymer length in liquid-liquid phase separation

Abstract: Understanding the thermodynamics that drive liquid-liquid phase separation (LLPS) is quite important given the number of diverse biomolecular systems undergoing this phenomenon. Many studies have focused on condensates of long polymers, but very few systems of short-polymer condensates have been observed and studied. Here, we study a short-polymer system of various lengths of poly-adenine RNA and peptides formed by the RGRGG sequence repeats to understand the underlying thermodynamics of LLPS. Using the recently developed COCOMO coarse-grained (CG) model, we predicted condensates for lengths as short as 5-10 residues, which was then confirmed by experiment, making this one of the smallest LLPS systems yet observed. A free-energy model reveals that the length dependence of condensation is driven primarily by entropy of confinement. The simplicity of this system will provide the basis for understanding more biologically realistic systems.