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Data associated with the article:Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

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.

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.

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.

Protein-protein docking plays a central role in the characterization and discovery of protein interactions in the cell. Complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models, coarse-grained protein descriptions and rigid-body approximations. In this study we present EvoDOCK, which is an evolutionary-based docking algorithm that enables the identification of optimal docking orientations using an atomistic energy function and sidechain flexibility, employing a global search without prior information of the binding site. EvoDOCK is a memetic algorithm 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, built on the Monte Carlo-based RosettaDOCK program, to optimize detailed atomic interactions. This approach resulted in substantial improvements in both sampling efficiency and computation speed compared to calculations using the local optimization method RosettaDOCK alone, with up to 35 times of reduction in computational cost. For all the ten systems investigated in this study, a highly accurate docking prediction could be identified as the lowest energy model with high efficiency. While protein-protein docking with EvoDOCK is still computationally expensive compared to many methods based on Fast Fourier Transforms (FFT), the results demonstrate the tractability of global docking proteins using an atomistic energy function while exploring sidechain flexibility. Comparison with FFT global docking demonstrated the benefits of using an all-atom energy function to identify native-like predictions. The sampling strategy in EvoDOCK can readily be tailored to include backbone flexibility in the search, which is often necessary to tackle more challenging docking challenges.

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.

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., Meliha Mehmeti et al. show that Wnt5a is a direct ligand of toll-like receptor 2 and 4 to act as an endogenous tolerance-associated molecular pattern, triggering innate immune tolerance in human myeloid cells. This study expands the functional repertoire of Wnt5a that is mainly known as a signalling protein involved in development and cancer.

Conformational changes are central to the function of many proteins. Characterization of these changes using molecular simulation requires methods to effectively sample pathways between protein conformational states. In this paper we present an iterative algorithm that samples conformational transitions in protein loops, referred to as the Jacobian-based Loop Transition (JaLT) algorithm. The method uses internal coordinates to minimise the sampling space, while Cartesian coordinates are used to maintain loop closure. Information from the two representations is combined to push sampling towards a desired target conformation. The innovation that enables the simultaneous use of Cartesian coordinates and internal coordinate is the linearisation of the inverse kinematics of a protein backbone. The algorithm uses the Rosetta all-atom energy function to steer sampling through low-energy regions and uses Rosetta’s side-chain energy minimiser to update side-chain conformations along the way. Because the JaLT algorithm combines a detailed energy function with a low-dimensional conformational space, it is positioned in between molecular dynamics (MD) and elastic network model (ENM) methods. As a proof of principle, we apply the JaLT algorithm to study the conformational transition between the open and occluded state in the MET20 loop of the Escherichia coli dihydrofolate reductase enzyme. Our results show that the algorithm generates semi-continuous pathways between the two states with realistic energy profiles. These pathways can be used to identify energy barriers along the transition. The effect of a single point mutation of the MET20 loop was also investigated and the predicted increase in energy barrier is consistent with the experimentally observed reduction in catalytic rate of the enzyme. Additionally, it is demonstrated how the JaLT algorithm can be used to identify dominant degrees of freedom during a transition. This can be valuable input for a more extensive characterization of the free energy pathway along a transition using molecular dynamics, which is often performed with a reduced set of degrees of freedom. This study has thereby provided the first examples of how linearisation of inverse kinematics can be applied to the analysis of proteins.

Protein–protein interactions are crucial in biology and play roles in for example, the immune system, signalling pathways, and enzyme regulation. Ultra‐high affinity interactions (Kd, d 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 centre 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.

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. Averaged over time and across proteins, these evolutionary pressures are sufficiently consistent to produce global substitution patterns that can be used to successfully find homologues, infer phylogenies, and reconstruct ancestral sequences. Although the factors which govern the variation of protein substitution rates has received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid rate 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 pattern of empirical rates 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 global driver behind the amino acid substitution patterns observed in proteins throughout the tree of life.

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.

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.

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., This research was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (grants #2016/04658-9 to DAL, #2013/04433-9 and #2018/19992-7 to IAC, #2015/00062-1 to FAS, #2018/20209-5 to DCM, #2019/22000-9 to HVDR, #2017/07709-6 to PSK, #2014/15546-1 to RCG and APUA and EMU/FAPESP grants #2015/16811-3 and #2015/16812-0 for the acquisition of the Gryphon crystallization robot and the SEC-MALS apparatus, respectively).

Protein self-assembly is extensively used in nature to build functional biomolecules and provides a general approach to design molecular complexes with many intriguing applications. Although computational design of protein-protein interfaces remains difficult, much progress has recently been made in de novo design of protein assemblies with cyclic, helical, cubic, internal and lattice symmetries. Here, we discuss some of the underlying biophysical principles of self-assembly that influence the design problem and highlight methodological advances that have made self-assembly design a fruitful area of protein design.

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&prime, to-5&prime, exoribonuclease and the 2&prime, O-methlytransferase activities of nsps 14 and 16, respectively. Here, we report the biophysical characterization and 1.6 &Aring, 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&ndash, transcription complex and virus replication.

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 β-glucosidase activity. (Less)

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.

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.

Homomeric coiled-coils can self-assemble into a wide range of structural states with different helix topologies and oligomeric states. In this study, we have combined de novo structure modeling with stability calculations to simultaneously predict structure and oligomeric states of homomeric coiled-coils. For dimers an asymmetric modeling protocol was developed. Modeling without symmetry constraints showed that backbone asymmetry is important for the formation of parallel dimeric coiled-coils. Collectively, our results demonstrate that high-resolution structure of coiled-coils, as well as parallel and antiparallel orientations of dimers and tetramers, can be accurately predicted from sequence. De novo modeling was also used to generate models of competing oligomeric states, which were used to compare stabilities and thus predict the native stoichiometry from sequence. In a benchmark set of 33 coiled-coil sequences, forming dimers to pentamers, up to 70% of the oligomeric states could be correctly predicted. The calculations demonstrated that the free energy of helix folding could be an important factor for determining stability and oligomeric state of homomeric coiled-coils. The computational methods developed here should be broadly applicable to studies of sequence-structure relationships in coiled-coils and the design of higher order assemblies with improved oligomerization specificity.

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.

Structure-based protein design offers a possibility of optimizing the overall shape of engineered binding scaffolds to match their targets better. We developed a computational approach for the structure-based design of repeat proteins that allows for adjustment of geometrical features like length, curvature, and helical twist. By combining sequence optimization of existing repeats and de novo design of capping structures, we designed leucine-rich repeats (LRRs) from the ribonuclease inhibitor (RI) family that assemble into structures with a predefined geometry. The repeat proteins were built from self-compatible LRRs that are designed to interact to form highly curved and planar assemblies. We validated the geometrical design approach by engineering a ring structure constructed from 10 self-compatible repeats. Protein design can also be used to increase our structural understanding of repeat proteins. We use our design constructs to demonstrate that buried Cys play a central role for stability and folding cooperativity in RI-type LRR proteins. The computational procedure presented here may be used to develop repeat proteins with various geometrical shapes for applications where greater control of the interface geometry is desired.

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., Author summary Proteins are commonly built up by folded domains connected by regions with higher flexibility. The interdomain orientations encoded by such hinges or linkers can play central roles in controlling the function of multidomain proteins, which makes them important to characterize. Small Angle X-ray Scattering (SAXS) is uniquely suited to study the conformational ensembles adopted by these kinds of proteins. However, because of the limited information provided by SAXS, ensemble models must be built by combination with other information sources and care have to be taken to avoid constructing ensembles that are more complex than data can support. We developed a method based on Bayesian statistics that combine data from molecular simulation with experimental data from SAXS and Nuclear Magnetic Resonance while automatically balancing the complexity of ensemble model with information in the data. We demonstrate that this method is capable of accurate inference of ensembles even in the presence of high levels of experimental noise. The method represents a general approach to combine data and simulation in the modeling of protein ensembles and can be extended to employ additional sources of experimental information.

Calreticulin is a highly conserved multifunctional protein implicated in many different biological systems and has therefore been the subject of intensive research. It is primarily present in the endoplasmatic reticulum where its main functions are to regulate Ca 2 + homeostasis, act as a chaperone and stabilize the MHC class I peptide-loading complex. Although several high-resolution structures of calreticulin exist, these only cover three-quarters of the entire protein leaving the extended structures unsolved. Additionally, the structure of calreticulin is influenced by the presence of Ca 2 +. The conformational changes induced by Ca 2 + have not been determined yet as they are hard to study with traditional approaches. Here, we investigated the Ca 2 +-induced conformational changes with a combination of chemical cross-linking, mass spectrometry, bioinformatics analysis and modelling in Rosetta. Using a bifunctional linker, we found a large Ca 2 +-induced change to the cross-linking pattern in calreticulin. Our results are consistent with a high flexibility in the P-loop, a stabilization of the acidic C-terminal and a relatively close interaction of the P-loop and the acidic C-terminal. Biological significance The function of calreticulin, an endoplasmatic reticulin chaperone, is affected by fluctuations in Ca 2 + concentration, but the structural mechanism is unknown. The present work suggests that Ca 2 +-dependent regulation is caused by different conformations of a long proline-rich loop that changes the accessibility to the peptide/lectin-binding site. Our results indicate that the binding of Ca 2 + to calreticulin may thus not only just be a question of Ca 2 + storage but is likely to have an impact on the chaperone activity.

Understanding design principles of biomolecular recognition is a key question of molecular biology. Yet the enormous complexity and diversity of biological molecules hamper the efforts to gain a predictive ability for the free energy of protein-protein, protein-DNA, and protein-RNA binding. Here, using a variant of the Derrida model, we predict that for a large class of biomolecular interactions, it is possible to accurately estimate the relative free energy of binding based on the fluctuation properties of their energy spectra, even if a finite number of the energy levels is known. We show that the free energy of the system possessing a wider binding energy spectrum is almost surely lower compared with the system possessing a narrower energy spectrum. Our predictions imply that low-affinity binding scores, usually wasted in protein-protein and protein-DNA docking algorithms, can be efficiently utilized to compute the free energy. Using the results of Rosetta docking simulations of protein-protein interactions from Andre et al., Proc. Natl. Acad. Sci. U.S.A. 105, 16148 (2008), we demonstrate the power of our predictions.

Protein conformational switches have many useful applications but are difficult to design rationally. Here we demonstrate how the isoenergetic energy landscape of higher-order coiled coils can enable the formation of an oligomerization switch by insertion of a single destabilizing element into an otherwise stable computationally designed scaffold. We describe a de novo designed peptide that was discovered to switch between a parallel symmetric pentamer at pH 8 and a trimer of antiparallel dimers at pH 6. The transition between pentamer and hexamer is caused by changes in the protonation states of glutamatic acid residues with highly upshifted pKa values in both oligomer forms. The drastic conformational change coupled with the narrow pH range makes the peptide sequence an attractive candidate for introduction of pH sensing into other proteins. The results highlight the remarkable ability of simple-α helices to self-assemble into a vast range of structural states.

What conformations do protein molecules populate in solution? Crystallography provides a high-resolution description of protein structure in the crystal environment, while NMR describes structure in solution but using less data. NMR structures display more variability, but is this because crystal contacts are absent or because of fewer data constraints? Here we report unexpected insight into this issue obtained through analysis of de tailed protein energy landscapes generated by large-scale, native-enhanced sampling of conformational space with Rosetta@home for 111 protein domains. In the absence of tightly associating binding partners or ligands, the lowest-energy Rosetta models were nearly all

Modeling the conformational changes that occur on binding of macromolecules is an unsolved challenge. In previous rounds of the Critical Assessment of PRediction of Interactions (CAPRI), it was demonstrated that the Rosetta approach to macromolecular modeling could capture side chain conformational changes on binding with high accuracy. In rounds 13–19 we tested the ability of various backbone remodeling strategies to capture the main-chain conformational changes observed during binding events. These approaches span a wide range of backbone motions, from limited refinement of loops to relieve clashes in homologous docking, through extensive remodeling of loop segments, to large-scale remodeling of RNA. Although the results are encouraging, major improvements in sampling and energy evaluation are clearly required for consistent high accuracy modeling. Analysis of our failures in the CAPRI challenges suggest that conformational sampling at the termini of exposed beta strands is a particularly pressing area for improvement. Proteins 2010. © Wiley-Liss, Inc.

SummaryDscam encodes a family of cell surface proteins required for establishing neural circuits in Drosophila. Alternative splicing of Drosophila Dscam can generate 19,008 distinct extracellular domains containing different combinations of three variable immunoglobulin domains. To test the binding properties of many Dscam isoforms, we developed a high-throughput ELISA-based binding assay. We provide evidence that 95% (>18,000) of Dscam isoforms exhibit striking isoform-specific homophilic binding. We demonstrate that each of the three variable domains binds to the same variable domain in an opposing isoform and identify the structural elements that mediate this self-binding of each domain. These studies demonstrate that self-binding domains can assemble in different combinations to generate an enormous family of homophilic binding proteins. We propose that this vast repertoire of Dscam recognition molecules is sufficient to provide each neuron with a unique identity and homotypic binding specificity, thereby allowing neuronal processes to distinguish between self and nonself.

Determination of pK(a) values of titrating residues in proteins provides a direct means of studying electrostatic coupling as well as pH-dependent stability. The B1 domain of protein G provides an excellent model system for such investigations. In this work, we analyze the observed pK(a) values of all carboxyl groups in a variant of PGB1 (T2Q, N8D, N37D) at low and high ionic strength as determined using (1)H-(13)C heteronuclear NMR in a structural context. The pK(a) values are used to calculate the pH-dependent stability in low and high salt and to investigate electrostatic coupling in the system. The observed pK(a) values can explain the pH dependence of protein stability but require pK(a) shifts relative to model values in the unfolded state, consistent with persistent residual structure in the denatured state. In particular, we find that most of the deviations from the expected random coil values can be explained by a significantly upshifted pK(a) value. We show also that (13)C backbone carbonyl data can be used to study electrostatic coupling in proteins and provide specific information on hydrogen bonding and electrostatic potential at nontitrating sites.

New protein design methods are needed to further improve the development of protein-binding scaffolds. Repeat proteins are linear tandem arrays of structurally similar building blocks, and they are established platforms for engineering proteins inhibitors and biosensors. However, current sequence-based engineering approaches lack the possibility of customizing the overall shape of a binder to its target molecule. Structure-based protein design offers a possibility of optimizing the overall shape of engineered binding scaffolds to better match their targets. We developed a protocol for the computational design of shape-optimized binding scaffolds that can better match their targets. By combining sequence optimization of existing repeats and de novo design of capping structures, we designed leucine-rich repeat (LRR) proteins where the building blocks assemble with a novel geometry. We validated the geometric design approach by engineering an artificial donut-like ring structure constructed from ten self-compatible repeats. Characterization of the design constructs revealed that buried cysteines play a central role for stability and folding cooperativity in certain LRR proteins. This may be used to selectively stabilize or destabilize specific parts of a protein. The computational procedure may now be employed to develop repeat proteins with various geometrical shapes for applications where greater control of the interface geometry is desired.

Calmodulin (CaM) operates as a Ca2+ sensor and is known to interact with and regulate hundreds of proteins involved in a great many aspects of cellular function. It is of considerable interest to understand the balance of forces in complex formation of CaM with its target proteins. Here we have studied the importance of electrostatic interactions in the complex between CaM and a peptide derived from smooth-muscle myosin light-chain kinase by experimental methods and Monte Carlo simulations of electrostatic interactions. We show by Monte Carlo simulations that, in agreement with experimental data, the binding affinity between CaM and highly charged peptides is surprisingly insensitive to changes in the net charge of both the protein and peptide. We observe an increase in the binding affinity between oppositely charged partners with increasing salt concentration from zero to 100mM, showing that formation of globular CaM-kinase type complexes is facilitated at physiological ionic strength. We conclude that ionic interactions in complex formation are optimized at pH and saline similar to the cell environment, which probably overrules the electrostatic repulsion between the negatively charged Ca2+-binding domains of CaM. We propose a conceivable rationalization of CaM electrostatics associated with interdomain repulsion.

Streptococcus pyogenes is a Gram-positive bacterium that causes several diseases, including acute tonsillitis and toxic shock syndrome. The surface-localized M protein, which is the most extensively studied virulence factor of S. pyogenes, has an approximately 50-residue N-terminal hypervariable region (HVR) that plays a key role in the escape of the host immunity. Despite the extensive sequence variability in this region, many HVRs specifically bind human C4b-binding protein (C4BP), a plasma protein that inhibits complement activation. Although the more conserved parts of M protein are known to have dimeric coiled-coil structure, it is unclear whether the HVR also is a coiled coil. Here, we use nuclear magnetic resonance (NMR) to study the conformational properties of HVRs from M4 and M22 proteins in isolation and in complex with the M protein binding portion of C4BP. We conclude that the HVRs of M4 and M22 are folded as coiled coils and that the folded nucleus of the M4 HVR has a length of approximately 27 residues. Moreover, we demonstrate that the C4BP binding surface of M4-N is found within a region of four heptad repeats. Using molecular modeling, we propose a model for the structure of the M4 HVR that is consistent with our experimental information from NMR spectroscopy.

Models generated byde novostructure prediction can be very useful starting points for molecular replacement for systems where suitable structural homologues cannot be readily identified. Protein–protein complexes andde novo-designed proteins are examples of systems that can be challenging to phase. In this study, the potential ofde novomodels of protein complexes for use as starting points for molecular replacement is investigated. The approach is demonstrated using homomeric coiled-coil proteins, which are excellent model systems for oligomeric systems. Despite the stereotypical fold of coiled coils, initial phase estimation can be difficult and many structures have to be solved with experimental phasing. A method was developed for automatic structure determination of homomeric coiled coils from X-ray diffraction data. In a benchmark set of 24 coiled coils, ranging from dimers to pentamers with resolutions down to 2.5 Å, 22 systems were automatically solved, 11 of which had previously been solved by experimental phasing. The generated models contained 71–103% of the residues present in the deposited structures, had the correct sequence and had freeRvalues that deviated on average by 0.01 from those of the respective reference structures. The electron-density maps were of sufficient quality that only minor manual editing was necessary to produce final structures. The method, namedCCsolve, combines methods forde novostructure prediction, initial phase estimation and automated model building into one pipeline.CCsolveis robust against errors in the initial models and can readily be modified to make use of alternative crystallographic software. The results demonstrate the feasibility ofde novophasing of protein–protein complexes, an approach that could also be employed for other small systems beyond coiled coils.

For highly oriented fibrillar molecules, three-dimensional structures can often be determined from X-ray fiber diffraction data. However, because of limited information content, structure determination and validation can be challenging. We demonstrate that automated structure determination of protein fibers can be achieved by guiding the building of macromolecular models with fiber diffraction data. We illustrate the power of our approach by determining the structures of six bacteriophage viruses de novo using fiber diffraction data alone and together with solid-state NMR data. Furthermore, we demonstrate the feasibility of molecular replacement from monomeric and fibrillar templates by solving the structure of a plant virus using homology modeling and protein-protein docking. The generated models explain the experimental data to the same degree as deposited reference structures but with improved structural quality. We also developed a cross-validation method for model selection. The results highlight the power of fiber diffraction data as structural constraints.

Protein and peptide self-assembly is a powerful design principle for engineering of new biomolecules. More sophisticated biomaterials could be built if both the structure of the overall assembly and that of the self-assembling building block could be controlled. To approach this problem, we developed a computational design protocol to enable de novo design of self-assembling peptides with predefined structure. The protocol was used to design a peptide building block with a βαβ fold that self-assembles into fibrillar structures. The peptide associates into a double β-sheet structure with tightly packed α-helices decorating the exterior of the fibrils. Using circular dichroism, Fourier transform infrared spectroscopy, electron microscopy and X-ray fiber diffraction, we demonstrate that the peptide adopts the designed conformation. The results demonstrate that computational protein design can be used to engineer protein and peptide assemblies with predefined three-dimensional structures, which can serve as scaffolds for the development of functional biomaterials. Rationally designed proteins and peptides could also be used to investigate the subtle energetic and entropic tradeoffs in natural self-assembly processes and the relation between assembly structure and assembly mechanism. We demonstrate that the de novo designed peptide self-assembles with a mechanism that is more complicated than expected, in a process where small changes in solution conditions can lead to significant differences in assembly properties and conformation. These results highlight that formation of structured protein/peptide assemblies is often dependent on the formation of weak but highly precise intermolecular interactions.

Homomeric coiled-coils can self-assemble into a wide range of structural states with different helix topologies and oligomeric states. In this study, we have combined de novo structure modeling with stability calculations to simultaneously predict structure and oligomeric states of homomeric coiled-coils. For dimers an asymmetric modeling protocol was developed. Modeling without symmetry constraints showed that backbone asymmetry is important for the formation of parallel dimeric coiled-coils. Collectively, our results demonstrate that high-resolution structure of coiled-coils, as well as parallel and antiparallel orientations of dimers and tetramers, can be accurately predicted from sequence. De novo modeling was also used to generate models of competing oligomeric states, which were used to compare stabilities and thus predict the native stoichiometry from sequence. In a benchmark set of 33 coiled-coil sequences, forming dimers to pentamers, up to 70% of the oligomeric states could be correctly predicted. The calculations demonstrated that the free energy of helix folding could be an important factor for determining stability and oligomeric state of homomeric coiled-coils. The computational methods developed here should be broadly applicable to studies of sequence-structure relationships in coiled-coils and the design of higher order assemblies with improved oligomerization specificity.

Design and Build Self-assembling biomolecules are attractive building blocks in the development of functional materials. Sophisticated DNA-based materials have been developed; however, progress in designing protein-based materials has been slower. King et al. (p. 1171 ) describe a general computational method in which protein building blocks are first symmetrically docked onto a target architecture, and then binding interfaces that drive self-assembly of the building blocks are designed. As a proof of principle, trimeric building blocks were used to design self-assembling 12-subunit complexes with tetrahedral symmetry and 24-subunit complexes with octahedral symmetry. Lai et al. (p. 1129 ) were able to build a 12-subunit tetrahedral protein cage from fused oligomeric protein domains.

Proteins SpoIIQ and SpoIIIAH interact through two membranes to connect the forespore and the mother cell during endospore development in the bacterium Bacillus subtilis . SpoIIIAH consists of a transmembrane segment and an extracellular domain with similarity to YscJ proteins. YscJ proteins form large multimeric rings that are the structural scaffolds for the assembly of type III secretion systems in Gram-negative bacteria. The predicted ring-forming motif of SpoIIIAH and other evidence led to the model that SpoIIQ and SpoIIIAH form the core components of a channel or transporter through which the mother cell nurtures forespore development. Therefore, to understand the roles of SpoIIIAH and SpoIIQ in channel formation, it is critical to determine whether SpoIIIAH adopts a ring-forming structural motif, and whether interaction of SpoIIIAH with SpoIIQ would preclude ring formation. We report a 2.8-Å resolution structure of a complex of SpoIIQ and SpoIIIAH. SpoIIIAH folds into the ring-building structural motif, and modeling shows that the structure of the SpoIIQ–SpoIIIAH complex is compatible with forming a symmetrical oligomer that is similar to those in type III systems. The inner diameters of the two most likely ring models are large enough to accommodate several copies of other integral membrane proteins. SpoIIQ contains a LytM domain, which is found in metalloendopeptidases, but lacks residues important for metalloprotease activity. Other LytM domains appear to be involved in protein–protein interactions. We found that the LytM domain of SpoIIQ contains an accessory region that interacts with SpoIIIAH.

Symmetric protein dimers, trimers, and higher-order cyclic oligomers play key roles in many biological processes. However, structural studies of oligomeric systems by solution NMR can be difficult due to slow tumbling of the system and the difficulty in identifying NOE interactions across protein interfaces. Here, we present an automated method (RosettaOligomers) for determining the solution structures of oligomeric systems using only chemical shifts, sparse NOEs, and domain orientation restraints from residual dipolar couplings (RDCs) without a need for a previously determined structure of the monomeric subunit. The method integrates previously developed Rosetta protocols for solving the structures of monomeric proteins using sparse NMR data and for predicting the structures of both nonintertwined and intertwined symmetric oligomers. We illustrated the performance of the method using a benchmark set of nine protein dimers, one trimer, and one tetramer with available experimental data and various interface topologies. The final converged structures are found to be in good agreement with both experimental data and previously published high-resolution structures. The new approach is more readily applicable to large oligomeric systems than conventional structure-determination protocols, which often require a large number of NOEs, and will likely become increasingly relevant as more high-molecular weight systems are studied by NMR.