Improved prediction of site-rates from structure with averaging across homologs.

Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site-specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site-specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site-specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site-specific rates across many extant protein structures can lead to correct recovery of site-rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site-specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site-specific rates in protein families.

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking.

AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic - tetrahedral, octahedral and icosahedral - symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.

Facile Method for High-throughput Identification of Stabilizing Mutations.

Characterizing the effects of mutations on stability is critical for understanding the function and evolution of proteins and improving their biophysical properties. High throughput folding and abundance assays have been successfully used to characterize missense mutations associated with reduced stability. However, screening for increased thermodynamic stability is more challenging since such mutations are rarer and their impact on assay readout is more subtle. Here, a multiplex assay for high throughput screening of protein folding was developed by combining deep mutational scanning, fluorescence-activated cell sorting, and deep sequencing. By analyzing a library of 2000 variants of Adenylate kinase we demonstrate that the readout of the method correlates with stability and that mutants with up to 13 °C increase in thermal melting temperature could be identified with low false positive rate. The discovery of many stabilizing mutations also enabled the analysis of general substitution patterns associated with increased stability in Adenylate kinase. This high throughput method to identify stabilizing mutations can be combined with functional screens to identify mutations that improve both stability and activity.

Atomistic simulation of protein evolution reveals sequence covariation and time-dependent fluctuations of site-specific substitution rates.

Thermodynamic stability is a crucial fitness constraint in protein evolution and is a central factor in shaping the sequence landscapes of proteins. The correlation between stability and molecular fitness depends on the mechanism that relates the biophysical property with biological function. In the simplest case, stability and fitness are related by the amount of folded protein. However, when proteins are toxic in the unfolded state, the fitness function shifts, resulting in higher stability under mutation-selection balance. Likewise, a higher population size results in a similar change in protein stability, as it magnifies the effect of the selection pressure in evolutionary dynamics. This study investigates how such factors affect the evolution of protein stability, site-specific mutation rates, and residue-residue covariation. To simulate evolutionary trajectories with realistic modeling of protein energetics, we develop an all-atom simulator of protein evolution, RosettaEvolve. By evolving proteins under different fitness functions, we can study how the fitness function affects the distribution of proposed and accepted mutations, site-specific rates, and the prevalence of correlated amino acid substitutions. We demonstrate that fitness pressure affects the proposal distribution of mutational effects, that changes in stability can largely explain variations in site-specific substitution rates in evolutionary trajectories, and that increased fitness pressure results in a stronger covariation signal. Our results give mechanistic insight into the evolutionary consequences of variation in protein stability and provide a basis to rationalize the strong covariation signal observed in natural sequence alignments.

A memetic algorithm enables efficient local and global all-atom protein-protein docking with backbone and side-chain flexibility.

Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires use of simplified energy models and limited conformational flexibility. In particular, use of all-atom energy functions and backbone and side-chain flexibility results in rugged energy landscapes that are difficult to explore. In this study, we develop a protein-protein docking algorithm, EvoDOCK, that combines the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method to refine detailed atomic interactions. EvoDOCK enabled accurate and fast local and global protein-protein docking using an all-atom energy function with side-chain flexibility. Comparison with a standard method built on Monte Carlo optimization demonstrated improved accuracy and increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.

DnaK response to expression of protein mutants is dependent on translation rate and stability.

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the λ-repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

Amyloid ß 42 fibril structure based on small-angle scattering.

Amyloid fibrils are associated with a number of neurodegenerative diseases, including fibrils of amyloid ß42 peptide (Aß42) in Alzheimer's disease. These fibrils are a source of toxicity to neuronal cells through surface-catalyzed generation of toxic oligomers. Detailed knowledge of the fibril structure may thus facilitate therapeutic development. We use small-angle scattering to provide information on the fibril cross-section dimension and shape for Aß42 fibrils prepared in aqueous phosphate buffer at pH = 7.4 and pH 8.0 under quiescent conditions at 37 °C from pure recombinant Aß42 peptide. Fitting the data using a continuum model reveals an elliptical cross-section and a peptide mass-per-unit length compatible with two filaments of two monomers, four monomers per plane. To provide a more detailed atomistic model, the data were fitted using as a starting state a high-resolution structure of the two-monomer arrangement in filaments from solid-state NMR (Protein Data Bank ID 5kk3). First, a twofold symmetric model including residues 11 to 42 of two monomers in the filament was optimized in terms of twist angle and local packing using Rosetta. A two-filament model was then built and optimized through fitting to the scattering data allowing the two N-termini in each filament to take different conformations, with the same conformation in each of the two filaments. This provides an atomistic model of the fibril with twofold rotation symmetry around the fibril axis. Intriguingly, no polydispersity as regards the number of filaments was observed in our system over separate samples, suggesting that the two-filament arrangement represents a free energy minimum for the Aß42 fibril.

A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices.

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.

An ultra-high affinity protein-protein interface displaying sequence-robustness.

Protein-protein interactions are crucial in biology and play roles in for example, the immune system, signaling pathways, and enzyme regulation. Ultra-high affinity interactions (Kd <0.1 nM) occur in these systems, however, structures and energetics behind stability of ultra-high affinity protein-protein complexes are not well understood. Regulation of the starch debranching barley limit dextrinase (LD) and its endogenous cereal type inhibitor (LDI) exemplifies an ultra-high affinity complex (Kd of 42 pM). In this study the LD-LDI complex is investigated to unveil how robust the ultra-high affinity is to LDI sequence variation at the protein-protein interface and whether alternative sequences can retain the ultra-high binding affinity. The interface of LD-LDI was engineered using computational protein redesign aiming at identifying LDI variants predicted to retain ultra-high binding affinity. These variants present a very diverse set of mutations going beyond conservative and alanine substitutions typically used to probe interfaces. Surface plasmon resonance analysis of the LDI variants revealed that high affinity of LD-LDI requires interactions of several residues at the rim of the protein interface, unlike the classical hotspot arrangement where key residues are found at the center of the interface. Notably, substitution of interface residues in LDI, including amino acids with functional groups different from the wild-type, could occur without loss of affinity. This demonstrates that ultra-high binding affinity can be conferred without hotspot residues, thus making complexes more robust to mutational drift in evolution. The present mutational analysis also demonstrates how energetic coupling can emerge between residues at large distances at the interface.

ZEAL: protein structure alignment based on shape similarity.

MOTIVATION: Most protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites. RESULTS: We present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL were of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins. AVAILABILITY AND IMPLEMENTATION: ZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Orientational Ambiguity in Septin Coiled Coils and its Structural Basis.

Septins are an example of subtle molecular recognition whereby different paralogues must correctly assemble into functional filaments important for essential cellular events such as cytokinesis. Most possess C-terminal domains capable of forming coiled coils which are believed to be involved in filament formation and bundling. Here, we report an integrated structural approach which aims to unravel their architectural diversity and in so doing provide direct structural information for the coiled-coil regions of five human septins. Unexpectedly, we encounter dimeric structures presenting both parallel and antiparallel arrangements which are in consonance with molecular modelling suggesting that both are energetically accessible. These sequences therefore code for two metastable states of different orientations which employ different but overlapping interfaces. The antiparallel structures present a mixed coiled-coil interface, one side of which is dominated by a continuous chain of core hydrophilic residues. This unusual type of coiled coil could be used to expand the toolkit currently available to the protein engineer for the design of previously unforeseen coiled-coil based assemblies. Within a physiological context, our data provide the first atomic details related to the assumption that the parallel orientation is likely formed between septin monomers from the same filament whilst antiparallelism may participate in the widely described interfilament cross bridges necessary for higher order structures and thereby septin function.

Crystal Structure of Non-Structural Protein 10 from Severe Acute Respiratory Syndrome Coronavirus-2.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), causing Coronavirus Disease 19 (COVID-19), emerged at the end of 2019 and quickly spread to cause a global pandemic with severe socio-economic consequences. The early sequencing of its RNA genome revealed its high similarity to SARS, likely to have originated from bats. The SARS-CoV-2 non-structural protein 10 (nsp10) displays high sequence similarity with its SARS homologue, which binds to and stimulates the 3'-to-5' exoribonuclease and the 2'-O-methlytransferase activities of nsps 14 and 16, respectively. Here, we report the biophysical characterization and 1.6 Å resolution structure of the unbound form of nsp10 from SARS-CoV-2 and compare it to the structures of its SARS homologue and the complex-bound form with nsp16 from SARS-CoV-2. The crystal structure and solution behaviour of nsp10 will not only form the basis for understanding the role of SARS-CoV-2 nsp10 as a central player of the viral RNA capping apparatus, but will also serve as a basis for the development of inhibitors of nsp10, interfering with crucial functions of the replication-transcription complex and virus replication.

Assembly of Capsids from Hepatitis B Virus Core Protein Progresses through Highly Populated Intermediates in the Presence and Absence of RNA.

The genetic material of viruses is protected by protein shells that are assembled from a large number of subunits in a process that is efficient and robust. Many of the mechanistic details underpinning efficient assembly of virus capsids are still unknown. The assembly mechanism of hepatitis B capsids has been intensively researched using a truncated core protein lacking the C-terminal domain responsible for binding genomic RNA. To resolve the assembly intermediates of hepatitis B virus (HBV), we studied the formation of nucleocapsids and empty capsids from full-length hepatitis B core proteins, using time-resolved small-angle X-ray scattering. We developed a detailed structural model of the HBV capsid assembly process using a combination of analysis with multivariate curve resolution, structural modeling, and Bayesian ensemble inference. The detailed structural analysis supports an assembly pathway that proceeds through the formation of two highly populated intermediates, a trimer of dimers and a partially closed shell consisting of around 40 dimers. These intermediates are on-path, transient and efficiently convert into fully formed capsids. In the presence of an RNA oligo that binds specifically to the C-terminal domain the assembly proceeds via a similar mechanism to that in the absence of nucleic acids. Comparisons between truncated and full-length HBV capsid proteins reveal that the unstructured C-terminal domain has a significant impact on the assembly process and is required to obtain a more complete mechanistic understanding of HBV capsid formation. These results also illustrate how combining scattering information from different time-points during time-resolved experiments can be utilized to derive a structural model of protein self-assembly pathways.

Wnt5a is a TLR2/4-ligand that induces tolerance in human myeloid cells.

Innate immune responses are rapid, dynamic and highly regulated to avoid overt reactions. This regulation is executed by innate immune tolerance mechanisms that remain obscure. Wnt5a is a signalling protein mainly involved in developmental processes and cancer. The effect of Wnt5a on inflammatory myeloid cells is controversial. Here, we combine primary cell cultures, in vitro binding studies, mass spectrometry and Drosophila protein modelling to show that Wnt5a is a direct ligand of toll-like receptor (TLR) 2 and 4. The binding promotes a MyD88-non-canonical nuclear factor of kappa B (NFκB) and AP-1 signalling cascade, with contradictory profiles in mouse (pro-inflammatory) and human (anti-inflammatory) myeloid immune cells. These data reveal that the true nature of Wnt5a in inflammatory cells, is to regulate TLR signals, and in human myeloid cells it acts as an endogenous, tolerance-associated molecular pattern (TAMP), inducing IL-10 and innate immune tolerance.

Bayesian inference of protein conformational ensembles from limited structural data.

Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble. This is followed by a complete Bayesian inference of population weights in the selected ensemble. Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise. Using experimental data, we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance (NMR) and structural energies of conformers extracted from the all-atom energy functions. We show that the data from SAXS, NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble.

A Protein-Based Encapsulation System with Calcium-Controlled Cargo Loading and Detachment.

Protein-based encapsulation systems have a wide spectrum of applications in targeted delivery of cargo molecules and for chemical transformations in confined spaces. By engineering affinity between cargo and container proteins it has been possible to enable the efficient and specific encapsulation of target molecules. Missing in current approaches is the ability to turn off the interaction after encapsulation to enable the cargo to freely diffuse in the lumen of the container. Separation between cargo and container is desirable in drug delivery applications and in the use of capsids as catalytic nanoparticles. We describe an encapsulation system based on the hepatitis B virus capsid in which an engineered high-affinity interaction between cargo and capsid proteins can be modulated by Ca2+ . Cargo proteins are loaded into capsids in the presence of Ca2+ , while ligand removal triggers unbinding inside the container. We observe that confinement leads to hindered rotation of cargo inside the capsid. Application of the designed container for catalysis was also demonstrated by encapsulation of an enzyme with ß-glucosidase activity.

Computational assessment of folding energy landscapes in heterodimeric coiled coils.

The coiled coil structural motif consists of alpha helices supercoiling around each other to form staggered knobs-into-holes packing. Such structures are deceptively simple, especially as they often can be described with parametric equations, but are known to exist in various conformations. Even the simplest systems, consisting of 2 monomers, can assemble into a wide range of states. They can form canonical as well as noncanonical coiled coils, be parallel or antiparallel, where helices associate with different degrees of shift, tilt, and rotation. Here, we investigate the energy landscape of heterodimeric coiled coils by carrying out de novo folding simulations starting from amino acid sequence. We folded a diverse set of 22 heterodimers and demonstrate that the approach is capable of identifying the atomic details in the experimental structure in the majority of cases. Our methodology also enables exploration of alternative states that can be accessible in solution beyond the experimentally determined structure. For many systems, we observe folding energy landscapes with multiple energy minima and several isoenergetic states. By comparing coiled coils from single domains and those extracted from larger proteins, we find that standalone coiled coils have deeper energy wells at the experimentally determined conformation. By folding the competing homodimeric states in addition to the heterodimers, we observe that the structural specificity towards the heteromeric state is often small. Taken together, our results demonstrate that de novo folding simulations can be a powerful tool to characterize structural specificity of coiled coils when coupled to assessment of energy landscapes.

Modeling the Structure of Helical Assemblies with Experimental Constraints in Rosetta.

Determining high-resolution structures of proteins with helical symmetry can be challenging due to limitations in experimental data. In such instances, structure-based protein simulations driven by experimental data can provide a valuable approach for building models of helical assemblies. This chapter describes how the Rosetta macromolecular package can be used to model homomeric protein assemblies with helical symmetry in a range of modeling scenarios including energy refinement, symmetrical docking, comparative modeling, and de novo structure prediction. Data-guided structure modeling of helical assemblies with experimental information from electron density, X-ray fiber diffraction, solid-state NMR, and chemical cross-linking mass spectrometry is also described.

Correction: Thermal stability and structural changes in bacterial toxins responsible for food poisoning.

[This corrects the article DOI: 10.1371/journal.pone.0172445.].

Thermal stability and structural changes in bacterial toxins responsible for food poisoning.

The staphylococcal enterotoxins (SEs) are secreted by the bacteria Staphylococcus aureus and are the most common causative agent in staphylococcal food poisoning. The staphylococcal enterotoxin A (SEA) has been associated with large staphylococcal food poisoning outbreaks, but newer identified SEs, like staphylococcal enterotoxin H (SEH) has recently been shown to be present at similar levels as SEA in food poisoning outbreaks. Thus, we set out to investigate the thermo-stability of the three-dimensional structures of SEA, SEH and staphylococcal enterotoxin E (SEE), since heat inactivation is a common method to inactivate toxins during food processing. Interestingly, the investigated toxins behaved distinctly different upon heating. SEA and SEE were more stable at slightly acidic pH values, while SEH adopted an extremely stable structure at neutral pH, with almost no effects on secondary structural elements upon heating to 95°C, and with reversible formation of tertiary structure upon subsequent cooling to room temperature. Taken together, the data suggests that the family of staphylococcal enterotoxins have different ability to withstand heat, and thus the exact profile of heat inactivation for all SEs causing food poisoning needs to be considered to improve food safety.