Uncovering new families and folds in the natural protein universe.

We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database1. These models cover nearly all proteins that are known, including those challenging to annotate for function or putative biological role using standard homology-based approaches. In this study, we examine the extent to which the AlphaFold database has structurally illuminated this 'dark matter' of the natural protein universe at high predicted accuracy. We further describe the protein diversity that these models cover as an annotated interactive sequence similarity network, accessible at https://uniprot3d.org/atlas/AFDB90v4 . By searching for novelties from sequence, structure and semantic perspectives, we uncovered the ß-flower fold, added several protein families to Pfam database2 and experimentally demonstrated that one of these belongs to a new superfamily of translation-targeting toxin-antitoxin systems, TumE-TumA. This work underscores the value of large-scale efforts in identifying, annotating and prioritizing new protein families. By leveraging the recent deep learning revolution in protein bioinformatics, we can now shed light into uncharted areas of the protein universe at an unprecedented scale, paving the way to innovations in life sciences and biotechnology.

Clustering predicted structures at the scale of the known protein universe.

Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy1, and over 214 million predicted structures are available in the AlphaFold database2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignment-based clustering algorithm-Foldseek cluster-that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2.30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem to be species specific, representing lower-quality predictions or examples of de novo gene birth. We also show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote structural similarity. On the basis of these analyses, we identify several examples of human immune-related proteins with putative remote homology in prokaryotic species, illustrating the value of this resource for studying protein function and evolution across the tree of life.

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

MOTIVATION: Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. RESULTS: We present the predicted structural proteome (25 134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. AVAILABILITY AND IMPLEMENTATION: All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates.

Homology modeling is the most common technique to build structural models of a target protein based on the structure of proteins with high-sequence identity and available high-resolution structures. This technique is based on the idea that protein structure shows fewer changes than sequence through evolution. While in this scenario single mutations would minimally perturb the structure, experimental evidence shows otherwise: proteins with high conformational diversity impose a limit of the paradigm of comparative modeling as the same protein sequence can adopt dissimilar three-dimensional structures. These cases present challenges for modeling; at first glance, they may seem to be easy cases, but they have a complexity that is not evident at the sequence level. In this chapter, we address the following questions: Why should we care about conformational diversity? How to consider conformational diversity when doing template-based modeling in a practical way?

Specificities of Modeling of Membrane Proteins Using Multi-Template Homology Modeling.

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the Protein Data Bank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely, RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

Homology Modeling of Transporter Proteins.

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Protein Homology Modeling for Effective Drug Design.

The effective drug design, especially for combating the multi-drug-resistant bacterial pathogens, requires more and more sophisticated procedures to obtain novel lead-like compounds. New classes of enzymes should be explored, especially those that help bacteria overcome existing treatments. The homology modeling is useful in obtaining the models of new enzymes; however, the active sites of them are sometimes present in closed conformations in the crystal structures, not suitable for drug design purposes. In such difficult cases, the combination of homology modeling, molecular dynamics simulations, and fragment screening can give satisfactory results.

Modeling of Protein Complexes.

The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.

Structure and Mechanism of Human ABC Transporters.

ABC transporters are essential for cellular physiology. Humans have 48 ABC genes organized into seven distinct families. Of these genes, 44 (in five distinct families) encode for membrane transporters, of which several are involved in drug resistance and disease pathways resulting from transporter dysfunction. Over the last decade, advances in structural biology have vastly expanded our mechanistic understanding of human ABC transporter function, revealing details of their molecular arrangement, regulation, and interactions, facilitated in large part by advances in cryo-EM that have rendered hitherto inaccessible targets amenable to high-resolution structural analysis. As a result, experimentally determined structures of multiple members of each of the five families of ABC transporters in humans are now available. Here we review this recent progress, highlighting the physiological relevance of human ABC transporters and mechanistic insights gleaned from their direct structure determination. We also discuss the impact and limitations of model systems and structure prediction methods in understanding human ABC transporters and discuss current challenges and future research directions.

A global pangenome for the wheat fungal pathogen Pyrenophora tritici-repentis and prediction of effector protein structural homology.

The adaptive potential of plant fungal pathogens is largely governed by the gene content of a species, consisting of core and accessory genes across the pathogen isolate repertoire. To approximate the complete gene repertoire of a globally significant crop fungal pathogen, a pan genomic analysis was undertaken for Pyrenophora tritici-repentis (Ptr), the causal agent of tan (or yellow) spot disease in wheat. In this study, 15 new Ptr genomes were sequenced, assembled and annotated, including isolates from three races not previously sequenced. Together with 11 previously published Ptr genomes, a pangenome for 26 Ptr isolates from Australia, Europe, North Africa and America, representing nearly all known races, revealed a conserved core-gene content of 57 % and presents a new Ptr resource for searching natural homologues (orthologues not acquired by horizontal transfer from another species) using remote protein structural homology. Here, we identify for the first time a non-synonymous mutation in the Ptr necrotrophic effector gene ToxB, multiple copies of the inactive toxb within an isolate, a distant natural Pyrenophora homologue of a known Parastagonopora nodorum necrotrophic effector (SnTox3), and clear genomic break points for the ToxA effector horizontal transfer region. This comprehensive genomic analysis of Ptr races includes nine isolates sequenced via long read technologies. Accordingly, these resources provide a more complete representation of the species, and serve as a resource to monitor variations potentially involved in pathogenicity.

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning.

Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of the model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side-chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side-chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side-chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

Multidimensional Phylogenetic Metrics Identify Class I Aminoacyl-tRNA Synthetase Evolutionary Mosaicity and Inter-Modular Coupling.

The role of aminoacyl-tRNA synthetases (aaRS) in the emergence and evolution of genetic coding poses challenging questions concerning their provenance. We seek evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant "scaffold" shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics-mutation frequency, its uniformity, and row-by-row cladistic congruence-imply that the Class I scaffold is a mosaic assembled from successive genetic sources. Metrics for different modules vary in accordance with their presumed functionality. Sequences derived from the ATP- and amino acid- binding sites exhibit specific two-way coupling to those derived from Connecting Peptide 1, a third module whose metrics suggest later acquisition. The data help validate: (i) experimental fragmentations of the canonical Class I structure into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that the ancestral Class I aaRS gene also encoded a Class II ancestor in frame on the opposite strand. A 46-residue Class I "protozyme" roots the Class I tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting implies near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.

Evolutionary Analysis of Dipeptidyl Peptidase I.

Human dipeptidyl peptidase I (DPPI) belongs to the family of papain-like cysteine peptidases. Its distinctive features are the unique exclusion domain which enables the eponymous activity and homotetramerization of DPPI, and its dependence on chloride ions for enzymatic activity. The oligomeric state of DPPI is unique in this family of predominantly monomeric peptidases. However, a distant DPPI ortholog from Plasmodium falciparum has been shown to be monomeric, indicating that the oligomeric state of DPPI varies between lineages. The aim of this work was to study the evolution of DPPI, with particular attention to the structural features that determine its characteristic enzymatic activity and preferences, and to reconstruct the evolution of its oligomerization. We analyzed fifty-seven selected sequences of DPPI and confirmed its presence in three lineages, namely, Amorphea (including animals and Amoebozoa), Alveolates and the metamonad Giardia. The amino acid residues that bind the chloride ion are highly conserved in all species, indicating that the dependence on chloride ions for activity is an evolutionarily conserved feature of DPPI. The number of N-glycosylation sites is significantly increased in animals, particularly vertebrates. Analysis of homology models and subunit contacts suggests that oligomerization is likely restricted to DPPIs in the Amorphea group.

Crystal structure and substrate recognition mechanism of the prolyl endoprotease PEP from Aspergillus niger.

Proteases are enzymes that are not only essential for life but also industrially important. Understanding the substrate recognition mechanisms of proteases is important to enhance the use of proteases. The fungus Aspergillus produces a wide variety of proteases, including PEP, which is a prolyl endoprotease from A. niger. Although PEP exhibits amino acid sequence similarity to the serine peptidase family S28 proteins (PRCP and DPP7) that recognize Pro-X bonds in the terminal regions of peptides, PEP recognizes Pro-X bonds not only in peptides but also in proteins. To reveal the structural basis of the prolyl endoprotease activity of PEP, we determined the structure of PEP by X-ray crystallography at a resolution of 1.75 Å. The PEP structure shows that PEP has a wide-open catalytic pocket compared to its homologs. The characteristic catalytic pocket structure of PEP is predicted to be important for the recognition of protein substrates.

Structural modeling of Omicron spike protein and its complex with human ACE-2 receptor: Molecular basis for high transmissibility of the virus.

Omicron is a new variant of SARS-CoV-2, which is currently infecting people around the world. Spike glycoprotein, an important molecule in pathogenesis of infection has been modeled and the interaction of its Receptor Binding Domain with human ACE-receptor has been analysed by simulation studies. Structural analysis of Omicron spike glycoprotein shows the 30 mutations to be distributed over all domains of the trimeric protein, wherein the mutant residues are seen to be participating in higher number of intra-molecular interactions including two salt bridges emanating from mutant residues thereby stabilizing their conformation, as compared to wild type. Complex of Receptor Binding Domain (RBD) with human ACE-2 receptor shows seven mutations at interacting interface comprising of two ionic interactions, eight hydrogen bonds and seven Van der Waals interactions. The number and quality of these interactions along with other binding biophysical parameters suggests more potency of RBD domain to the receptor as compared to the wild type counterpart. Results of this study explains the high transmissibility of Omicron variant of SARS-CoV-2 that is currently observed across the world.

Mycobacterial MMAR_2193 catalyzes O-methylation of diverse polyketide cores.

O-methylation of small molecules is a common modification widely present in most organisms. Type III polyketides undergo O-methylation at hydroxyl end to play a wide spectrum of roles in bacteria, plants, algae, and fungi. Mycobacterium marinum harbours a distinctive genomic cluster with a type III pks gene and genes for several polyketide modifiers including a methyltransferase gene, mmar_2193. This study reports functional analyses of MMAR_2193 and reveals multi-methylating potential of the protein. Comparative sequence analyses revealed conservation of catalytically important motifs in MMAR_2193 protein. Homology-based structure-function and molecular docking studies suggested type III polyketide cores as possible substrates for MMAR_2193 catalysis. In vitro enzymatic characterization revealed the capability of MMAR_2193 protein to utilize diverse polyphenolic substrates to methylate several hydroxyl positions on a single substrate molecule. High-resolution mass spectrometric analyses identified multi-methylations of type III polyketides in cell-free reconstitution assays. Notably, our metabolomics analyses identified some of these methylated molecules in biofilms of wild type Mycobacterium marinum. This study characterizes a novel mycobacterial O-methyltransferase protein with multi-methylating enzymatic ability that could be exploited to generate a palette of structurally distinct bioactive molecules.

Receptor binding and complex structures of human ACE2 to spike RBD from omicron and delta SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic continues worldwide with many variants arising, some of which are variants of concern (VOCs). A recent VOC, omicron (B.1.1.529), which obtains a large number of mutations in the receptor-binding domain (RBD) of the spike protein, has risen to intense scientific and public attention. Here, we studied the binding properties between the human receptor ACE2 (hACE2) and the VOC RBDs and resolved the crystal and cryoelectron microscopy structures of the omicron RBD-hACE2 complex as well as the crystal structure of the delta RBD-hACE2 complex. We found that, unlike alpha, beta, and gamma, omicron RBD binds to hACE2 at a similar affinity to that of the prototype RBD, which might be due to compensation of multiple mutations for both immune escape and transmissibility. The complex structures of omicron RBD-hACE2 and delta RBD-hACE2 reveal the structural basis of how RBD-specific mutations bind to hACE2.

Crystal structure of the chromosome partition protein MukE homodimer.

The SMC (structural maintenance of chromosomes) proteins are known to be involved in chromosome pairing or aggregation and play an important role in cell cycle and division. Different from SMC-ScpAB complex maintaining chromosome structure in most bacteria, the MukB-MukE-MukF complex is responsible for chromosome condensation in E. coli and some γ-proteobacter. Though different models were proposed to illustrate the mechanism of how the MukBEF complex worked, the assembly of the MukBEF complex is a key. The MukE dimer interacted with the middle region of one MukF molecule, and was clamped by the N- and C-terminal domain of the latter, and then was involved in the interaction with the head domain of MukB. To reveal the structural basis of MukE involved in the dynamic equilibrium of potential different MukBEF assemblies, we determined the MukE structure at 2.44 Å resolution. We found that the binding cavity for the α10, ß4 and ß5 of MukF (residues 296-327) in the MukE dimer has been occupied by the α9 and ß7 strand of MukE. We proposed that the highly dynamic C-terminal region (173-225) was important for the MukE-F assembly and then involved in the MukBEF complex formation.

Functional and structural characterization of a GH3 ß-N-acetylhexosaminidase from Akkermansia muciniphila involved in mucin degradation.

Akkermansia muciniphila is a probiotic that colonizes the outer layer of intestinal mucus and is negatively associated with metabolic disorders. Amuc_2109 protein, a ß-N-acetylhexosaminidase from A. muciniphila, may be involved in the degradation of mucins and is associated with intestinal health. Here, we reported the crystal structure of Amuc_2109, which belongs to the GH family 3 enzymes and fell into the canonical (α/ß)8 TIM barrel structure with GlcNAc bound to the active center. Biochemical assay characterization of Amuc_2109 revealed that Amuc_2109 is a GlcNAc-specific glycosidase active over a wide temperature and pH range, reflecting the survival advantage of Amuc_2109 in the intestinal environment. Our structural and biochemical results will contribute to the understanding of the catalytic mechanism of the GH3 ß-N-acetylhexosaminidase and help to gain insight into the molecular mechanism of complex carbohydrate utilization and restoration of the intestinal barrier in A. muciniphila.

Structures of the TetR-like transcription regulator RcdA alone and in complexes with ligands.

RcdA is a helix-turn-helix (HTH) transcriptional regulator belonging to the TetR family. The protein regulates the transcription of curlin subunit gene D, the master regulator of biofilm formation. Moreover, it was predicted that it might be involved in the regulation of up to 27 different genes. However, an effector of RcdA and the environmental conditions which trigger RcdA action remain unknown. Herein, we report the first crystal structures of RcdA in complexes with ligands, trimethylamine N-oxide (TMAO) and tris(hydroxymethyl)aminomethane (Tris), which might serve as RcdA effectors. Based on these structures, the ligand-binding pocket of RcdA was characterized in detail. The conservation of the amino acid residues forming the ligand-binding cavity was analyzed and the comprehensive search for RcdA structural homologs was performed. This analysis indicated that RcdA is structurally similar to multidrug-binding TetR family members, however, its ligand-binding cavity differs significantly from the pockets of its structural homologs. The interaction of RcdA with TMAO and Tris indicates that the protein might be involved in alkaline stress response.