Ab initio molecular-replacement phasing for symmetric helical membrane proteins

The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report

The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 Å resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement.

Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis

The Shape, Elongation, Division, and Sporulation (“SEDS”) proteins are a large family of ubiquitous and essential transmembrane enzymes with critical roles in bacterial cell wall biology. The exact function of SEDS proteins was long enigmatic, but recent work^1–3 has revealed that the prototypical SEDS family member RodA is a peptidoglycan polymerase – a role previously attributed exclusively to members of the penicillin binding protein family⁴. This discovery has made RodA and other SEDS proteins promising targets for the development of next-generation antibiotics. However, little is known regarding the molecular basis for SEDS activity, and no structural data are available for RodA or any homolog thereof. Here, we report the crystal structure of Thermus thermophilus RodA at a resolution of 2.9 Å, determined using evolutionary covariance-based fold prediction to enable molecular replacement. The structure reveals a novel ten-pass transmembrane fold with large extracellular loops, one of which is partially disordered. The protein contains a highly conserved cavity in the transmembrane domain, reminiscent of ligand binding sites in transmembrane receptors. Mutagenesis experiments in Bacillus subtilis and Escherichia coli show that perturbation of this cavity abolishes RodA function both in vitro and in vivo, indicating it is catalytically essential. These results provide a framework for understanding bacterial cell wall synthesis and SEDS protein function.

Molecular symmetry-constrained systematic search approach to structure solution of the coiled-coil SRGAP2 F-BARx domain

SRGAP2 (Slit-Robo GTPase-activating protein 2) is a cytoplasmic protein found to be involved in neuronal branching, restriction of neuronal migration and restriction of the length and density of dendritic postsynaptic spines. The extended F-BAR (F-BARx) domain of SRGAP2 generates membrane protrusions when expressed in COS-7 cells, while most F-BARs induce the opposite effect: membrane invaginations. As a first step to understand this discrepancy, the F-BARx domain of SRGAP2 was isolated and crystallized after co-expression with the carboxy domains of the protein. Diffraction data were collected from two significantly non-isomorphous crystals in the same monoclinic C2 space group. A correct molecular-replacment solution was obtained by applying a molecular symmetry-constrained systematic search approach that took advantage of the conserved biological symmetry of the F-BAR domains. It is shown that similar approaches can solve other F-BAR structures that were previously determined by experimental phasing. Diffraction data were reprocessed with a high-resolution cutoff of 2.2 Å, chosen using less strict statistical criteria. This has improved the outcome of multi-crystal averaging and other density-modification procedures.

Initiating heavy-atom-based phasing by multi-dimensional molecular replacement

To obtain an electron-density map from a macromolecular crystal the phase problem needs to be solved, which often involves the use of heavy-atom derivative crystals and concomitant heavy-atom substructure determination. This is typically performed by dual-space methods, direct methods or Patterson-based approaches, which however may fail when only poorly diffracting derivative crystals are available. This is often the case for, for example, membrane proteins. Here, an approach for heavy-atom site identification based on a molecular-replacement parameter matrix (MRPM) is presented. It involves an n-dimensional search to test a wide spectrum of molecular-replacement parameters, such as different data sets and search models with different conformations. Results are scored by the ability to identify heavy-atom positions from anomalous difference Fourier maps. The strategy was successfully applied in the determination of a membrane-protein structure, the copper-transporting P-type ATPase CopA, when other methods had failed to determine the heavy-atom substructure. MRPM is well suited to proteins undergoing large conformational changes where multiple search models should be considered, and it enables the identification of weak but correct molecular-replacement solutions with maximum contrast to prime experimental phasing efforts.

Direct phasing of protein crystals with high solvent content

An iterative transform method is proposed for solving the phase problem in protein crystallography. In each iteration, a weighted average electron-density map is constructed to define an estimated protein mask. Solvent flattening is then imposed through the hybrid input–output algorithm [Fienup (1982).Appl. Opt.21, 2758–2769]. Starting from random initial phases, after thousands of iterations the mask evolves into the correct shape and the phases converge to the correct values with an average error of 30–40° for high-resolution data for several protein crystals with high solvent content. With the use of non-crystallographic symmetry, the method could potentially be extended to phase protein crystals with less than 50% solvent fraction. The new phasing algorithm can supplement and enhance the traditional refinement tools.

Molecular replacement then and now

The `phase problem' in crystallography results from the inability to directly measure the phases of individual diffracted X-ray waves. While intensities are directly measured during data collection, phases must be obtained by other means. Several phasing methods are available (MIR, SAR, MAD, SAD and MR) and they all rely on the premise that phase information can be obtained if the positions of marker atoms in the unknown crystal structure are known. This paper is dedicated to the most popular phasing method, molecular replacement (MR), and represents a personal overview of the development, use and requirements of the methodology. The first description of noncrystallographic symmetry as a tool for structure determination was explained by Rossmann and Blow [Rossmann & Blow (1962), Acta Cryst. 15, 24-31]. The term `molecular replacement' was introduced as the name of a book in which the early papers were collected and briefly reviewed [Rossmann (1972), The Molecular Replacement Method. New York: Gordon & Breach]. Several programs have evolved from the original concept to allow faster and more sophisticated searches, including six-dimensional searches and brute-force approaches. While careful selection of the resolution range for the search and the quality of the data will greatly influence the outcome, the correct choice of the search model is probably still the main criterion to guarantee success in solving a structure using MR. Two of the main parameters used to define the `best' search model are sequence identity (25% or more) and structural similarity. Another parameter that may often be undervalued is the quality of the probe: there is clearly a relationship between the quality and the correctness of the chosen probe and its usefulness as a search model. Efforts should be made by all structural biologists to ensure that their deposited structures, which are potential search probes for future systems, are of the best possible quality.

Advances, interactions, and future developments in the CNS, Phenix, and Rosetta structural biology software systems

Advances in our understanding of macromolecular structure come from experimental methods, such as X-ray crystallography, and also computational analysis of the growing number of atomic models obtained from such experiments. The later analyses have made it possible to develop powerful tools for structure prediction and optimization in the absence of experimental data. In recent years, a synergy between these computational methods for crystallographic structure determination and structure prediction and optimization has begun to be exploited. We review some of the advances in the algorithms used for crystallographic structure determination in the Phenix and Crystallography & NMR System software packages and describe how methods from ab initio structure prediction and refinement in Rosetta have been applied to challenging crystallographic problems. The prospects for future improvement of these methods are discussed.

Structure determination using poorly diffracting membrane-protein crystals: the H+-ATPase and Na+,K+-ATPase case history

An approach is presented for the structure determination of membrane proteins on the basis of poorly diffracting crystals which exploits molecular replacement for heavy-atom site identification at 6-9 A maximum resolution and improvement of the heavy-atom-derived phases by multi-crystal averaging using quasi-isomorphous data sets. The multi-crystal averaging procedure allows real-space density averaging followed by phase combination between non-isomorphous native data sets to exploit crystal-to-crystal nonisomorphism despite the crystals belonging to the same space group. This approach has been used in the structure determination of H(+)-ATPase and Na(+),K(+)-ATPase using Ca(2+)-ATPase models and its successful application to the Mhp1 symporter using LeuT as a search model is demonstrated.

Simultaneous prediction of protein folding and docking at high resolution

Interleaved dimers and higher order symmetric oligomers are ubiquitous in biology but present a challenge to de novo structure prediction methodology: The structure adopted by a monomer can be stabilized largely by interactions with other monomers and hence not the lowest energy state of a single chain. Building on the Rosetta framework, we present a general method to simultaneously model the folding and docking of multiple-chain interleaved homo-oligomers. For more than a third of the cases in a benchmark set of interleaved homo-oligomers, the method generates near-native models of large alpha-helical bundles, interlocking beta sandwiches, and interleaved alpha/beta motifs with an accuracy high enough for molecular replacement based phasing. With the incorporation of NMR chemical shift information, accurate models can be obtained consistently for symmetric complexes with as many as 192 total amino acids; a blind prediction was within 1 A rmsd of the traditionally determined NMR structure, and fit independently collected RDC data equally well. Together, these results show that the Rosetta "fold-and-dock" protocol can produce models of homo-oligomeric complexes with near-atomic-level accuracy and should be useful for crystallographic phasing and the rapid determination of the structures of multimers with limited NMR information.

Prospects for de novo phasing with de novo protein models

The prospect of phasing diffraction data sets ;de novo' for proteins with previously unseen folds is appealing but largely untested. In a first systematic exploration of phasing with Rosetta de novo models, it is shown that all-atom refinement of coarse-grained models significantly improves both the model quality and performance in molecular replacement with the Phaser software. 15 new cases of diffraction data sets that are unambiguously phased with de novo models are presented. These diffraction data sets represent nine space groups and span a large range of solvent contents (33-79%) and asymmetric unit copy numbers (1-4). No correlation is observed between the ease of phasing and the solvent content or asymmetric unit copy number. Instead, a weak correlation is found with the length of the modeled protein: larger proteins required somewhat less accurate models to give successful molecular replacement. Overall, the results of this survey suggest that de novo models can phase diffraction data for approximately one sixth of proteins with sizes of 100 residues or less. However, for many of these cases, ;de novo phasing with de novo models' requires significant investment of computational power, much greater than 10(3) CPU days per target. Improvements in conformational search methods will be necessary if molecular replacement with de novo models is to become a practical tool for targets without homology to previously solved protein structures.

Macromolecular modeling with rosetta

Advances over the past few years have begun to enable prediction and design of macromolecular structures at near-atomic accuracy. Progress has stemmed from the development of reasonably accurate and efficiently computed all-atom potential functions as well as effective conformational sampling strategies appropriate for searching a highly rugged energy landscape, both driven by feedback from structure prediction and design tests. A unified energetic and kinematic framework in the Rosetta program allows a wide range of molecular modeling problems, from fibril structure prediction to RNA folding to the design of new protein interfaces, to be readily investigated and highlights areas for improvement. The methodology enables the creation of novel molecules with useful functions and holds promise for accelerating experimental structural inference. Emerging connections to crystallographic phasing, NMR modeling, and lower-resolution approaches are described and critically assessed.

NCS-constrained exhaustive search using oligomeric models

The efficiency of the cross-rotation function step of molecular replacement (MR) is intrinsically limited as it uses only a fraction of the Patterson vectors. Along with general techniques extending the boundaries of the method, there are approaches that utilize specific features of a given structure. In special cases, where the directions of noncrystallographic symmetry axes can be unambiguously derived from the self-rotation function and the structure of the homologue protein is available in a related oligomeric state, the cross-rotation function step of MR can be omitted. In such cases, a small number of yet unknown parameters defining the orientation of the oligomer and/or its internal organization can be optimized using an exhaustive search. Three difficult MR cases are reported in which these parameters were determined and the oligomer was positioned according to the maximal value of the correlation coefficient in a series of translation searches.

High-resolution structure prediction and the crystallographic phase problem

The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using nuclear magnetic resonance data and to comparative models based on distant structural homologues, the method can significantly improve the accuracy of the structures in terms of both the backbone conformations and the placement of core side chains. Furthermore, the resulting models satisfy a particularly stringent test: they provide significantly better solutions to the X-ray crystallographic phase problem in molecular replacement trials. Finally, we show that all-atom refinement can produce de novo protein structure predictions that reach the high accuracy required for molecular replacement without any experimental phase information and in the absence of templates suitable for molecular replacement from the Protein Data Bank. These results suggest that the combination of high-resolution structure prediction with state-of-the-art phasing tools may be unexpectedly powerful in phasing crystallographic data for which molecular replacement is hindered by the absence of sufficiently accurate previous models.