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Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

Desmosterolosis is a rare sterol biosynthesis disorder characterized by multiple congenital anomalies, failure to thrive, severe developmental delay, progressive epileptic encephalopathy, and elevated levels of desmosterol caused by biallelic mutations of DHCR24 encoding 3-β-hydroxysterol Δ-24-reductase. DHCR24 is regarded as the key enzyme of cholesterol synthesis in the metabolism of brain cholesterol as it catalyzes the reduction of the Δ-24 double bond of sterol intermediates during cholesterol biosynthesis. To date, 15 DHCR24 variants, detected in 2 related and 14 unrelated patients, have been associated with the desmosterolosis disorder. Here, we describe a proband harboring the never-described DHCR24 homozygous missense variant NM_014762.4:c.506T>C, NP_055577.1:p.M169T, whose functional validation was confirmed through biochemical assay. By using molecular dynamics simulation techniques, we investigated the impact of this variant on the protein stability and interaction network with the flavin adenine dinucleotide cofactor, thereby providing a preliminary assessment of its mechanistic role in comparison to all known pathogenic variants, the wild-type protein, and a known benign DHCR24 variant. This report expands the clinical and molecular spectra of the DHCR24-related disorder, reports on a novel DHCR24 deleterious variant associated with desmosterolosis, and gives new insights into genotype-phenotype correlations.

Elucidating the structure of an enzyme and how substrates bind to the active site is an important step for understanding its reaction mechanism and function. Nevertheless, the methods available to obtain three-dimensional structures of proteins, such as x-ray crystallography and NMR, can be expensive and time-consuming. Considering this, an alternative is using structural bioinformatic tools to predict the tertiary structure of a protein from its primary sequence, followed by molecular docking of one or more substrates into the enzyme structure model. In the past few years, significant advances have been made in these computational tools, which can give useful information about the active site and enzyme-substrate interactions before the structure can be resolved using physical methods. Here, using common bean (Phaseolus vulgaris) hydroxycinnamoyl-coenzyme A:tetrahydroxyhexanedioic acid hydroxycinnamoyltransferase (HHHT) as an example, we describe methods and workflows for protein structure prediction and molecular docking that can be performed on a personal computer using only open-source tools.

A figure in the article by Barbarin-Bocahu & Graille [(2022), Acta Cryst. D78, 517-531] is corrected.

Small angle neutron scattering is a powerful complementary technique in structural biology. It generally requires, or benefits from, deuteration to achieve its unique potentials. Molecular deuteration has become a mature expertise, with deuteration facilities located worldwide to support access to the technique for a wide breadth of structural biology and life sciences. The sorts of problems well answered by small angle scattering and deuteration involve large (>10Å) scale flexible movements, and this approach is best used where high-resolution methods (crystallography, NMR, cryo-EM) leave questions unanswered. This chapter introduces deuteration, reviewing biological deuteration of proteins, lipids and sterols, and then steps through the ever-expanding range of deuterated molecules being produced by chemical synthesis and enabling sophisticated experiments using physiologically relevant lipids. Case studies of recent successful use of deuteration may provide illustrative examples for strategies for future experiments. We discuss issues of nomenclature for synthesised molecules of novel labeling and make recommendations for their naming. We reflect on our experiences, with cost associated with achieving an arbitrary deuteration level, and on the benefits of experimental co-design by user scientist, deuteration scientist, and neutron scattering scientist working together. Although methods for biological and chemical deuteration are published in the public domain, we recommend that the best method to deuterate is to engage with a deuteration facility.

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.

In the quest to understand how single-stranded DNA-binding proteins function and evolve at a molecular level, determination of their high-resolution three-dimensional structure using methods such as X-ray crystallography is indispensable. Here we present a collection of methods used in crystallographic studies of the single-stranded DNA-binding protein from the bacteriophage Enc34, from designing expression constructs through to protein production, purification, and crystallization, to determination and analysis of the protein\'s three-dimensional structure. The chapter aims to shed light on all the essential stages in a structural study of a single-stranded DNA-binding protein, with a spotlight on procedures specific to X-ray crystallography to aid those interested in venturing into structural biology.

The contributions of structural biology to drug discovery have expanded over the last 20 years from structure-based ligand optimization to a broad range of clinically relevant topics including the understanding of disease, target discovery, screening for new types of ligands, discovery of new modes of action, addressing clinical challenges such as side effects or resistance, and providing data to support drug registration. This expansion of scope is due to breakthroughs in the technology, which allow structural information to be obtained rapidly and for more complex molecular systems, but also due to the combination of different technologies such as X-ray, NMR, and other biophysical methods, which allows one to get a more complete molecular understanding of disease and ways to treat it. In this review, we provide examples of the types of impact molecular structure information can have in the clinic for both low molecular weight and biologic drug discovery and describe several case studies from our own work to illustrate some of these contributions.

Phytoestrogens are plant-derived compounds that functionally and structurally mimic mammalian estrogens. Phytoestrogens have broad inhibitory activities toward several steroidogenic enzymes, such as the 17β-hydroxysteroid dehydrogenases (17β-HSDs), which modulate the biological potency of androgens and estrogens in mammals. However, to date, no crystallographic data are available to explain phytoestrogens binding to mammalian 17β-HSDs. NADP(H)-dependent 17β-HSD from the filamentous fungus Cochliobolus lunatus (17β-HSDcl) has been the subject of extensive biochemical, kinetic and quantitative structure-activity relationship studies that have shown that the flavonols are the most potent inhibitors. In the present study, we investigated the structure-activity relationships of the ternary complexes between the holo form of 17β-HSDcl and the flavonols kaempferol and 3,7-dihydroxyflavone, in comparison with the isoflavones genistein and biochanin A. Crystallographic data are accompanied by kinetic analysis of the inhibition mechanisms for six flavonols (3-hydroxyflavone, 3,7-dihydroxyflavone, kaempferol, quercetin, fisetin, myricetin), one flavanone (naringenin), one flavone (luteolin), and two isoflavones (genistein, biochanin A). The kinetics analysis shows that the degree of hydroxylation of ring B significantly influences the overall inhibitory efficacy of the flavonols. A distinct binding mode defines the interactions between 17β-HSDcl and the flavones and isoflavones. Moreover, the complex with biochanin A reveals an unusual binding mode that appears to account for its greater inhibition of 17β-HSDcl with respect to genistein. Overall, these data provide a blueprint for identification of the distinct molecular determinants that underpin 17β-HSD inhibition by phytoestrogens.