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 or simulate the nascent chain and its interactions with the ribosome. Building realistic 3D models of ribosome-nascent chain (RNC) constructs often requires expert knowledge, so 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 Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB). Despite using only modest computational resources, we show here that AutoRNC can build plausible conformations for a wide range of RNC constructs for which experimental data have already been reported.

Improving Signal and Transit Peptide Predictions Using AlphaFold2-predicted Protein Structures.

Many proteins contain cleavable signal or transit peptides that direct them to their final subcellular locations. Such peptides are usually predicted from sequence alone using methods such as TargetP 2.0 and SignalP 6.0. While these methods are usually very accurate, we show here that an analysis of a protein's AlphaFold2-predicted structure can often be used to identify false positive predictions. We start by showing that when given a protein's full-length sequence, AlphaFold2 builds experimentally annotated signal and transit peptides in orientations that point away from the main body of the protein. This indicates that AlphaFold2 correctly identifies that a signal is not destined to be part of the mature protein's structure and suggests, as a corollary, that predicted signals that AlphaFold2 folds with high confidence into the main body of the protein are likely to be false positives. To explore this idea, we analyzed predicted signal peptides in 48 proteomes made available in DeepMind's AlphaFold2 database (https://alphafold.ebi.ac.uk). Applying TargetP 2.0 and SignalP 6.0 to the 561,562 proteins in the database results in 95,236 being predicted to contain a cleavable signal or transit peptide. In 95.1% of these cases, the AlphaFold2 structure of the full-length protein is fully consistent with the prediction of TargetP 2.0 or SignalP 6.0. In the remaining 4.9% of cases where the AlphaFold2 structure does not appear consistent with the prediction, the signal is often only predicted with low confidence. The potential false positives identified here may be useful for training even more accurate signal prediction methods.

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.

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.

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 (HIs). 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 the 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.

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 4 th 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 µs is computationally feasible with BD-HI simulations.

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.

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 DeepMind's 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.

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 4 th dimension, and then moving these walls toward each other, has the effect of decreasing the space available in the 4 th 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.

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.

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

DeepMind's 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 (Zn) 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 ligand 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.

Design principles that protect the proteasome from self-destruction.

The proteasome is a powerful intracellular protease that can degrade effectively any protein, self or foreign, for regulation, quality control, or immune response. Proteins are targeted for degradation by localizing them to the proteasome, typically by ubiquitin tags. At the same time, the proteasome is built from ~33 subunits, and their assembly into the complex and activity are tuned by post-translational modifications on long disordered regions on the subunits. Molecular modeling and biochemical experiments show that some of the disordered regions of proteasomal subunits can access the substrate recognition sites. All disordered regions tested, independent of location, are constructed from amino acid sequences that escape recognition. Replacing a disordered region with a sequence that is recognized by the proteasome leads to self-degradation and, in the case of an essential subunit, cell death.

Cotranslational protein assembly imposes evolutionary constraints on homomeric proteins.

Cotranslational protein folding can facilitate rapid formation of functional structures. However, it can also cause premature assembly of protein complexes, if two interacting nascent chains are in close proximity. By analyzing known protein structures, we show that homomeric protein contacts are enriched toward the C termini of polypeptide chains across diverse proteomes. We hypothesize that this is the result of evolutionary constraints for folding to occur before assembly. Using high-throughput imaging of protein homomers in Escherichia coli and engineered protein constructs with N- and C-terminal oligomerization domains, we show that, indeed, proteins with C-terminal homomeric interface residues consistently assemble more efficiently than those with N-terminal interface residues. Using in vivo, in vitro and in silico experiments, we identify features that govern successful assembly of homomers, which have implications for protein design and expression optimization.

The C-terminal region of translesion synthesis DNA polymerase η is partially unstructured and has high conformational flexibility.

Eukaryotic DNA polymerase η catalyzes translesion synthesis of thymine dimers and 8-oxoguanines. It is comprised of a polymerase domain and a C-terminal region, both of which are required for its biological function. The C-terminal region mediates interactions with proliferating cell nuclear antigen (PCNA) and other translesion synthesis proteins such as Rev1. This region contains a ubiquitin-binding/zinc-binding (UBZ) motif and a PCNA-interacting protein (PIP) motif. Currently little structural information is available for this region of polymerase η. Using a combination of approaches-including genetic complementation assays, X-ray crystallography, Langevin dynamics simulations, and small-angle X-ray scattering-we show that the C-terminal region is partially unstructured and has high conformational flexibility. This implies that the C-terminal region acts as a flexible tether linking the polymerase domain to PCNA thereby increasing its local concentration. Such tethering would facilitate the sampling of translesion synthesis polymerases to ensure that the most appropriate one is selected to bypass the lesion.

Features of genomic organization in a nucleotide-resolution molecular model of the Escherichia coli chromosome.

We describe structural models of the Escherichia coli chromosome in which the positions of all 4.6 million nucleotides of each DNA strand are resolved. Models consistent with two basic chromosomal orientations, differing in their positioning of the origin of replication, have been constructed. In both types of model, the chromosome is partitioned into plectoneme-abundant and plectoneme-free regions, with plectoneme lengths and branching patterns matching experimental distributions, and with spatial distributions of highly-transcribed chromosomal regions matching recent experimental measurements of the distribution of RNA polymerases. Physical analysis of the models indicates that the effective persistence length of the DNA and relative contributions of twist and writhe to the chromosome's negative supercoiling are in good correspondence with experimental estimates. The models exhibit characteristics similar to those of 'fractal globules,' and even the most genomically-distant parts of the chromosome can be physically connected, through paths combining linear diffusion and inter-segmental transfer, by an average of only ∼10 000 bp. Finally, macrodomain structures and the spatial distributions of co-expressed genes are analyzed: the latter are shown to depend strongly on the overall orientation of the chromosome. We anticipate that the models will prove useful in exploring other static and dynamic features of the bacterial chromosome.

Reparameterization of Solute-Solute Interactions for Amino Acid-Sugar Systems Using Isopiestic Osmotic Pressure Molecular Dynamics Simulations.

AMBER/GLYCAM and CHARMM are popular force fields for simulations of amino acids and sugars. Here we report excessively attractive amino acid-sugar interactions in both force fields, and corrections to nonbonded interactions that match experimental osmotic pressures of mixed aqueous solutions of diglycine and sucrose. The modified parameters also improve the ΔGtrans of diglycine from water to aqueous sucrose and, with AMBERff99SB/GLYCAM06, eliminate a caging effect seen in previous simulations of the protein ubiquitin with glucose.