Biology subjects

Wehrspan, Z. J.

Publications and source records attributed to Wehrspan, Z. J..

3 recordsLinked to original sources

bioRxiv2023

Multi-scale Modeling and Experimental Data Enable Structural Models of the Escherichia Coli Peptidoglycan to be Constructed on the Whole-Cell Scale

The peptidoglycan (PG) layer of Escherichia coli is a single, interconnected gigaDalton molecule that is the largest in the cell. Experimental studies have established a number of the PGs properties, and previous computational studies have simulated aspects of its behavior on sub-cellular scales, but none have fully modeled the PGs compositional heterogeneity and no models have yet been constructed on the whole-cell scale. Here we use a combination of computational modeling approaches to construct whole-cell PG models at a resolution of one coarse-grained (CG) bead per glycan that are consistent with a wide variety of available experimental data. In particular, we derive plausible glycan strand length distributions for the polar and cylindrical regions of the cell that cover the full range of possible strand lengths and that are consistent with all available experimental data. In addition, we develop stochastic simulation code that explicitly models a cross-linking experiment from the literature that has a direct bearing on the extent to which Brauns lipoprotein (Lpp) is partitioned between periplasmic and surface-exposed locations. We then use all of these data as inputs to a new computer code, PG_maker, which builds CG models of the PG on a whole-cell scale in under an hour. Finally, we use the resulting 3D models as a basis for: (a) estimating pore size distributions - which, despite the idealized nature of the models, are shown to be in surprisingly good agreement with experimental estimates - and (b) calculating the effects of the large numbers of periplasmic Lpps on the ability of freely diffusing proteins to access the compartment that lies between the PG and the outer membrane. The ability to combine a wide range of experimental data into structural models that are physically realizable in 3D helps to set the stage for performing simulations of the PG on the whole-cell scale in the near future.

microbiology↗

bioRxiv2023

Atomic Models of All Major Trans-Envelope Complexes Involved in Lipid Trafficking in Escherichia Coli Constructed Using a Combination of AlphaFold2, AF2Complex, and Membrane Morphing Simulations

In Gram-negative bacteria, several trans-envelope complexes (TECs) have been identified that span the periplasmic space in order to facilitate lipid transport between the inner- and outer- membranes. While partial or near-complete structures of some of these TECs have been solved by conventional experimental techniques, most remain incomplete. Here we describe how a combination of computational approaches, constrained by experimental data, can be used to build complete atomic models for four TECs implicated in lipid transport in Escherichia coli. We use DeepMinds protein structure prediction algorithm, AlphaFold2, and a variant of it designed to predict protein complexes, AF2Complex, to predict the oligomeric states of key components of TECs and their likely interfaces with other components. After obtaining initial models of the complete TECs by superimposing predicted structures of subcomplexes, we use the membrane orientation prediction algorithm OPM to predict the likely orientations of the inner- and outer- membrane components in each TEC. Since, in all cases, the predicted membrane orientations in these initial models are tilted relative to each other, we devise a novel molecular mechanics-based strategy that we call "membrane morphing" that adjusts each TEC model until the two membranes are properly aligned with each other and separated by a distance consistent with estimates of the periplasmic width in E. coli. The study highlights the potential power of combining computational methods, operating within limits set by both experimental data and by cell physiology, for producing useable atomic structures of very large protein complexes.

biochemistry↗

bioRxiv2021

Identification of Iron-Sulfur (Fe-S) and Zn-binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome

DeepMinds AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn-binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of Fe-S cluster or Zn-binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.

bioinformatics↗