Community recommendations on cryoEM data archiving and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Cryo-EM structure and B-factor refinement with ensemble representation.

Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Community recommendations on cryoEM data archiving and validation: Outcomes of a wwPDB/EMDB workshop on cryoEM data management, deposition and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and consensus recommendations resulting from the workshop. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures.

Cryo-electron microscopy (cryo-EM), through resolution advancements, has become pivotal in structure-based drug discovery. However, most cryo-EM structures are solved at 3-4 Å resolution, posing challenges for small-molecule docking and structure-based virtual screening due to issues in the precise positioning of ligands and the surrounding side chains. We present ChemEM, a software package that employs cryo-EM data for the accurate docking of one or multiple ligands in a protein-binding site. Validated against a highly curated benchmark of high- and medium-resolution cryo-EM structures and the corresponding high-resolution controls, ChemEM displayed impressive performance, accurately placing ligands in all but one case, often surpassing cryo-EM PDB-deposited solutions. Even without including the cryo-EM density, the ChemEM scoring function outperformed the well-established AutoDock Vina score. Using ChemEM, we illustrate that valuable information can be extracted from maps at medium resolution and underline the utility of cryo-EM structures for drug discovery.

CASP15 cryo-EM protein and RNA targets: Refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.

Critical assessment of methods of protein structure prediction (CASP)-Round XV.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

PICKLUSTER: a protein-interface clustering and analysis plug-in for UCSF ChimeraX.

SUMMARY: The identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER [Protein Interface C(K)luster]-a program for identifying "sub-interfaces" in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces. AVAILABILITY AND IMPLEMENTATION: PICKLUSTER is implemented as a plug-in for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions. It is freely available for download in the ChimeraX Toolshed and https://gitlab.com/topf-lab/pickluster.git.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

RIBFIND2: Identifying rigid bodies in protein and nucleic acid structures.

Molecular structures are often fitted into cryo-EM maps by flexible fitting. When this requires large conformational changes, identifying rigid bodies can help optimize the model-map fit. Tools for identifying rigid bodies in protein structures exist, however an equivalent for nucleic acid structures is lacking. With the increase in cryo-EM maps containing RNA and progress in RNA structure prediction, there is a need for such tools. We previously developed RIBFIND, a program for clustering protein secondary structures into rigid bodies. In RIBFIND2, this approach is extended to nucleic acid structures. RIBFIND2 can identify biologically relevant rigid bodies in important groups of complex RNA structures, capturing a wide range of dynamics, including large rigid-body movements. The usefulness of RIBFIND2-assigned rigid bodies in cryo-EM model refinement was demonstrated on three examples, with two conformations each: Group II Intron complexed IEP, Internal Ribosome Entry Site and the Processome, using cryo-EM maps at 2.7-5 Å resolution. A hierarchical refinement approach, performed on progressively smaller sets of RIBFIND2 rigid bodies, was clearly shown to have an advantage over classical all-atom refinement. RIBFIND2 is available via a web server with structure visualization and as a standalone tool.

CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, errors in the reference structures can potentially reduce the accuracy of the assessment. This issue is particularly prominent in cryoEM-determined structures, and therefore, in the assessment of CASP15 cryoEM targets, we directly utilized density maps to evaluate the predictions. A method for ranking the quality of protein chain predictions based on rigid fitting to experimental density was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy although local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. The top 136 predictions associated with 9 protein target reference structures were selected for refinement, in addition to the top 40 predictions for 11 RNA targets. To this end, we have developed an automated hierarchical refinement pipeline in cryoEM maps. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure, including some regions with better fit to the density. This refinement was successful despite large conformational changes and secondary structure element movements often being required, suggesting that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryoEM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors with even short loops failing to be accurately modeled or refined at times. The lack of consensus amongst models suggests that modeling holds the potential for identifying more flexible regions within the structure.

Protein target highlights in CASP15: Analysis of models by structure providers.

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

Structural journey of an insecticidal protein against western corn rootworm.

The broad adoption of transgenic crops has revolutionized agriculture. However, resistance to insecticidal proteins by agricultural pests poses a continuous challenge to maintaining crop productivity and new proteins are urgently needed to replace those utilized for existing transgenic traits. We identified an insecticidal membrane attack complex/perforin (MACPF) protein, Mpf2Ba1, with strong activity against the devastating coleopteran pest western corn rootworm (WCR) and a novel site of action. Using an integrative structural biology approach, we determined monomeric, pre-pore and pore structures, revealing changes between structural states at high resolution. We discovered an assembly inhibition mechanism, a molecular switch that activates pre-pore oligomerization upon gut fluid incubation and solved the highest resolution MACPF pore structure to-date. Our findings demonstrate not only the utility of Mpf2Ba1 in the development of biotechnology solutions for protecting maize from WCR to promote food security, but also uncover previously unknown mechanistic principles of bacterial MACPF assembly.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Assessment of three-dimensional RNA structure prediction in CASP15.

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

Loss-of-Function Variants in DRD1 in Infantile Parkinsonism-Dystonia.

The human dopaminergic system is vital for a broad range of neurological processes, including the control of voluntary movement. Here we report a proband presenting with clinical features of dopamine deficiency: severe infantile parkinsonism-dystonia, characterised by frequent oculogyric crises, dysautonomia and global neurodevelopmental impairment. CSF neurotransmitter analysis was unexpectedly normal. Triome whole-genome sequencing revealed a homozygous variant (c.110C>A, (p.T37K)) in DRD1, encoding the most abundant dopamine receptor (D1) in the central nervous system, most highly expressed in the striatum. This variant was absent from gnomAD, with a CADD score of 27.5. Using an in vitro heterologous expression system, we determined that DRD1-T37K results in loss of protein function. Structure-function modelling studies predicted reduced substrate binding, which was confirmed in vitro. Exposure of mutant protein to the selective D1 agonist Chloro APB resulted in significantly reduced cyclic AMP levels. Numerous D1 agonists failed to rescue the cellular defect, reflected clinically in the patient, who had no benefit from dopaminergic therapy. Our study identifies DRD1 as a new disease-associated gene, suggesting a crucial role for the D1 receptor in motor control.

Clinical Phenotype in Individuals With Birk-Landau-Perez Syndrome Associated With Biallelic SLC30A9 Pathogenic Variants.

BACKGROUND AND OBJECTIVES: Birk-Landau-Perez syndrome is a genetic disorder caused by biallelic pathogenic variants in SLC30A9 presenting with a complex movement disorder, developmental regression, oculomotor abnormalities, and renal impairment. It has previously been reported in 2 families. We describe the clinical phenotype of 8 further individuals from 4 unrelated families with SLC30A9-related disease. METHOD: Following detailed clinical phenotyping, 1 family underwent research whole-genome sequencing (WGS), 1 research whole-exome sequencing, and 2 diagnostic WGS. Variants of interest were assessed for pathogenicity using in silico prediction tools, homology modeling, and, where relevant, sequencing of complementary DNA (cDNA) for splicing effect. RESULTS: In 2 unrelated families of Pakistani origin (1 consanguineous and 1 not), the same homozygous missense variant in SLC30A9 (c.1253G>T, p.Gly418Val) was identified. Family 1 included 2 affected brothers, and family 2 one affected boy. In family 3, also consanguineous, there were 4 affected siblings homozygous for the variant c.1049delCAG, pAla350del. The fourth family was nonconsanguineous: the 1 affected individual was compound heterozygous for c.1083dup, p.Val362Cysfs*5, and c.1413A>G, p.Ser471=. Despite phenotypic variability between the 4 families, all affected patients manifested with a progressive hyperkinetic movement disorder, associated with oculomotor apraxia and ptosis. None had evidence of severe renal impairment. For the novel missense variant, the conformation of the loop domain and packing of transmembrane helices are likely to be disrupted based on structure modeling. Its presence in 2 unrelated Pakistani families suggests a possible founder variant. For the synonymous variant p.Ser471=, an effect on splicing was confirmed through cDNA analysis. DISCUSSION: Pathogenic variants in SLC30A9 cause a progressive autosomal recessive neurologic syndrome associated with a complex hyperkinetic movement disorder. Our report highlights the expanding disease phenotype, which can present with a wider spectrum of severity than has previously been recognized.

PTPN11 Mosaicism Causes a Spectrum of Pigmentary and Vascular Neurocutaneous Disorders and Predisposes to Melanoma.

Phakomatosis pigmentovascularis is a diagnosis that denotes the coexistence of pigmentary and vascular birthmarks of specific types, accompanied by variable multisystem involvement, including CNS disease, asymmetrical growth, and a predisposition to malignancy. Using a tight phenotypic group and high-depth next-generation sequencing of affected tissues, we discover here clonal mosaic variants in gene PTPN11 encoding SHP2 phosphatase as a cause of phakomatosis pigmentovascularis type III or spilorosea. Within an individual, the same variant is found in distinct pigmentary and vascular birthmarks and is undetectable in blood. We go on to show that the same variants can cause either the pigmentary or vascular phenotypes alone, and drive melanoma development within pigmentary lesions. Protein structure modeling highlights that although variants lead to loss of function at the level of the phosphatase domain, resultant conformational changes promote longer ligand binding. In vitro modeling of the missense variants confirms downstream MAPK pathway overactivation and widespread disruption of human endothelial cell angiogenesis. Importantly, patients with PTPN11 mosaicism theoretically risk passing on the variant to their children as the germline RASopathy Noonan syndrome with lentigines. These findings improve our understanding of the pathogenesis and biology of nevus spilus and capillary malformation syndromes, paving the way for better clinical management.

MED27, SLC6A7, and MPPE1 Variants in a Complex Neurodevelopmental Disorder with Severe Dystonia.

BACKGROUND: Despite advances in next generation sequencing technologies, the identification of variants of uncertain significance (VUS) can often hinder definitive diagnosis in patients with complex neurodevelopmental disorders. OBJECTIVE: The objective of this study was to identify and characterize the underlying cause of disease in a family with two children with severe developmental delay associated with generalized dystonia and episodic status dystonicus, chorea, epilepsy, and cataracts. METHODS: Candidate genes identified by autozygosity mapping and whole-exome sequencing were characterized using cellular and vertebrate model systems. RESULTS: Homozygous variants were found in three candidate genes: MED27, SLC6A7, and MPPE1. Although the patients had features of MED27-related disorder, the SLC6A7 and MPPE1 variants were functionally investigated. SLC6A7 variant in vitro overexpression caused decreased proline transport as a result of reduced cell-surface expression, and zebrafish knockdown of slc6a7 exhibited developmental delay and fragile motor neuron morphology that could not be rescued by L-proline transporter-G396S RNA. Lastly, patient fibroblasts displayed reduced cell-surface expression of glycophosphatidylinositol-anchored proteins linked to MPPE1 dysfunction. CONCLUSIONS: We report a family harboring a homozygous MED27 variant with additional loss-of-function SLC6A7 and MPPE1 gene variants, which potentially contribute to a blended phenotype caused by multilocus pathogenic variants. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Stoichiometry-Selective Antagonism of α4ß2 Nicotinic Acetylcholine Receptors by Fluoroquinolone Antibiotics.

Quinolone antibiotics disrupt bacterial DNA synthesis by interacting with DNA gyrase and topoisomerase IV. However, in addition, they have been shown to act as inhibitors of pentameric ligand-gated ion channels such as GABAA receptors and the α7 nicotinic acetylcholine receptor (nAChR). In the present study, we have examined the effects of quinolone antibiotics on the human α4ß2 nAChR, an important subtype that is widely expressed in the central nervous system. A key feature of α4ß2 nAChRs is their ability to coassemble into two distinct stoichiometries, (α4)2(ß2)3 and (α4)3(ß2)2, which results in differing affinities for acetylcholine. The effects of nine quinolone antibiotics were examined on both stoichiometries of the α4ß2 receptor by two-electrode voltage-clamp recording. All compounds exhibited significant inhibition of α4ß2 nAChRs. However, all of the fluoroquinolone antibiotics examined (ciprofloxacin, enoxacin, enrofloxacin, difloxacin, norfloxacin, pefloxacin, and sparfloxacin) were significantly more potent inhibitors of (α4)2(ß2)3 nAChRs than of (α4)3(ß2)2 nAChRs. This stoichiometry-selective effect was most pronounced with pefloxacin, which inhibited (α4)2(ß2)3 nAChRs with an IC50 of 26.4 ± 3.4 µM but displayed no significant inhibition of (α4)3(ß2)2 nAChRs. In contrast, two nonfluorinated quinolone antibiotics (cinoxacin and oxolinic acid) exhibited no selectivity in their inhibition of the two stoichiometries of α4ß2. Computational docking studies suggest that pefloxacin interacts selectively with an allosteric transmembrane site at the ß2(+)/ß2(-) subunit interface, which is consistent with its selective inhibition of (α4)2(ß2)3. These findings concerning the antagonist effects of fluoroquinolones provide further evidence that differences in the subunit stoichiometry of heteromeric nAChRs can result in substantial differences in pharmacological properties.