Biology subjects

Elcock, A. H.

Publications and source records attributed to Elcock, A. H..

7 recordsLinked to original sources

bioRxiv2023

AutoRNC: an automated modeling program for building atomic models of ribosome-nascent chain complexes

The interpretation of experimental studies of co-translational protein folding often benefits from the use of computational methods that seek to model the nascent chain and its interactions with the ribosome. Ribosome-nascent chain (RNC) constructs studied experimentally can vary significantly in size and the extent to which they contain secondary and tertiary structure, and building realistic 3D models of them therefore often requires expert knowledge. To circumvent this issue, we describe here AutoRNC, an automated modeling program capable of constructing large numbers of plausible atomic models of RNCs within minutes. AutoRNC takes input from the user specifying any regions of the nascent chain that contain secondary or tertiary structure and attempts to build conformations compatible with those specifications - and with the constraints imposed by the ribosome - by sampling and progressively piecing together dipeptide conformations extracted from the RCSB. We first show that conformations of completely unfolded proteins built by AutoRNC in the absence of the ribosome have radii of gyration that match well with the corresponding experimental data. We then show that AutoRNC can build plausible conformations for a wide range of RNC constructs for which experimental data have already been reported. Since AutoRNC requires only modest computational resources, we anticipate that it will prove to be a useful hypothesis generator for experimental studies, for example, in providing indications of whether designed constructs are likely to be capable of folding, as well as providing useful starting points for downstream atomic or coarse-grained simulations of the conformational dynamics of RNCs.

biophysics↗

bioRxiv2023

Modeling the 3D structure and conformational dynamics of very large RNAs using coarse-grained molecular simulations

We describe a computational approach to building and simulating realistic 3D models of very large RNA molecules (>1000 nucleotides) at a resolution of one "bead" per nucleotide. The method starts with a predicted secondary structure and uses several stages of energy minimization and Brownian dynamics (BD) simulation to build 3D models. A key step in the protocol is the temporary addition of a 4th spatial dimension that allows all predicted helical elements to become disentangled from each other in an effectively automated way. We then use the resulting 3D models as input to Brownian dynamics simulations that include hydrodynamic interactions (HIs) that allow the diffusive properties of the RNA to be modelled as well as enabling its conformational dynamics to be simulated. To validate the dynamics part of the method, we first show that when applied to small RNAs with known 3D structures the BD-HI simulation models accurately reproduce their experimental hydrodynamic radii (Rh). We then apply the modelling and simulation protocol to a variety of RNAs for which experimental Rh values have been reported ranging in size from 85 to 3569 nucleotides. We show that the 3D models, when used in BD-HI simulations, produce hydrodynamic radii that are usually in good agreement with experimental estimates for RNAs that do not contain tertiary contacts that persist even under very low salt conditions. Finally, we show that sampling of the conformational dynamics of large RNAs on timescales of 100 {micro}s is computationally feasible with BD-HI simulations.

biophysics↗

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↗

bioRxiv2023

Easy Removal of Steric Clashes and Entanglements in Macromolecular Systems by Temporary Addition of a Fourth Spatial Dimension

When models of complicated macromolecular systems are constructed, it is common to inadvertently include either gross steric clashes or entanglements of extended loop regions. Removing these problems with conventional energy minimization or dynamics algorithms can often be difficult. Here I show that one easy alternative is to temporarily add an extra spatial dimension and to displace atoms or molecules along this fourth dimension such that the distances between atoms, when measured in 4D, are no longer considered clashing. Adding in half-harmonic potential functions to mimic walls in this 4th dimension, and then moving these walls toward each other, has the effect of decreasing the space available in the 4th dimension and drives atoms to avoid each other in 3D. I illustrate the method with three examples: two showing how interlocked ring polymers can be easily disentangled from each other in both 2D and 3D, and one showing how ten identical coarse-grained protein models, all placed at the same point in 3D space, can be separated from each other, without distorting their structures, during the course of a single energy minimization. A sample program implementing the method is available that can be easily adapted to other situations.

biophysics↗

bioRxiv2023

An Orientationally Averaged Version of the Rotne-Prager-Yamakawa Tensor Provides A Fast But Still Accurate Treatment Of Hydrodynamic Interactions In Brownian Dynamics Simulations Of Biological Macromolecules

The Brownian dynamics (BD) simulation technique is widely used to model the diffusive and conformational dynamics of complex systems comprising biological macromolecules. For the diffusive properties of macromolecules to be described correctly by BD simulations, it is necessary to include hydrodynamic interactions (HI). When modeled at the Rotne-Prager-Yamakawa (RPY) level of theory, for example, the translational and rotational diffusion coefficients of isolated macromolecules can be accurately reproduced; when HIs are neglected, however, diffusion coefficients can be underestimated by an order of magnitude or more. The principal drawback to the inclusion of HIs in BD simulations is their computational expense, and several previous studies have sought to accelerate their modeling by developing fast approximations for the calculation of the correlated random displacements. Here we explore the use of an alternative way to accelerate calculation of HIs, i.e., by replacing the full RPY tensor with an orientationally averaged (OA) version which retains the distance dependence of the HIs but averages out their orientational dependence. We seek here to determine whether such an approximation can be justified in application to the modeling of typical proteins and RNAs. We show that the use of an OA RPY tensor allows translational diffusion of macromolecules to be modeled with very high accuracy at the cost of rotational diffusion being underestimated by [~]25%. We show that this finding is independent of the type of macromolecule simulated and the level of structural resolution employed in the models. We also show, however, that these results are critically dependent on the inclusion of a non-zero term that describes the divergence of the diffusion tensor: when this term is omitted from simulations that use the OA RPY model, unfolded macromolecules undergo rapid collapse. Our results indicate that the orientationally averaged RPY tensor is likely to be a useful, fast approximate way of including HIs in BD simulations of intermediate-scale systems.

biophysics↗

bioRxiv2023

spotter: A single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes

Stochastic simulation models have played an important role in efforts to understand the mechanistic basis of prokaryotic transcription and translation. Despite the fundamental linkage of these processes in bacterial cells, however, most simulation models have been limited to representations of either transcription or translation. In addition, the available simulation models typically either attempt to recapitulate data from single-molecule experiments without considering cellular-scale high-throughput sequencing data or, conversely, seek to reproduce cellular-scale data without paying close attention to many of the mechanistic details. To address these limitations, we here present spotter (Simulation of Prokaryotic Operon Transcription & Translation Elongation Reactions), a flexible, user-friendly simulation model that offers highly-detailed combined representations of prokaryotic transcription, translation, and DNA supercoiling. In incorporating nascent transcript and ribosomal profiling sequencing data, spotter provides a critical bridge between data collected in single-molecule experiments and data collected at the cellular scale. Importantly, in addition to rapidly generating output that can be aggregated for comparison with next-generation sequencing and proteomics data, spotter produces residue-level positional information that can be used to visualize individual simulation trajectories in detail. We anticipate that spotter will be a useful tool in exploring the interplay of processes that are crucially linked in prokaryotes.

molecular biology↗