Scoring residue conservation

Understanding the structural and functional diversity of ATP-PPases using protein domains and functional families in the CATH database

ATP-pyrophosphatases (ATP-PPases) are the most primordial lineage of the large and diverse HUP (high-motif proteins, universal stress proteins, ATP-pyrophosphatase) superfamily. There are four different ATP-PPase substrate-specificity groups (SSGs), and members of each group show considerable sequence variation across the domains of life despite sharing the same catalytic function. Owing to the expansion in the number of ATP-PPase domain structures from advances in protein structure prediction by AlphaFold2 (AF2), we have characterized the two most populated ATP-PPase SSGs, the nicotinamide adenine dinucleotide synthases (NADSs) and guanosine monophosphate synthases (GMPSs). Local structural and sequence comparisons of NADS and GMPS identified taxonomic-group-specific functional motifs. As GMPS and NADS are potential drug targets of pathogenic microorganisms including Mycobacterium tuberculosis, bacterial GMPS and NADS specific functional motifs reported in this study, may contribute to antibacterial-drug development.

Design of SARS-CoV-2 RBD Immunogens to Focus Immune Responses Towards Conserved Coronavirus Epitopes

SARS-CoV-2 continues to evolve, with new variants emerging that evade pre-existing immunity and limit the efficacy of existing vaccines. One approach towards developing superior, variant-proof vaccines is to engineer immunogens that preferentially elicit antibodies with broad cross-reactivity against SARS-CoV-2 and its variants by targeting conserved epitopes on spike. The inner and outer faces of the Receptor Binding Domain (RBD) are two such conserved regions targeted by antibodies that recognize diverse human and animal coronaviruses. To promote the elicitation of such antibodies by vaccination, we engineered "resurfaced" RBD immunogens that contained mutations at exposed RBD residues outside the target epitopes. In the context of pre-existing immunity, these vaccine candidates aim to disfavor the elicitation of strain-specific antibodies against the immunodominant Receptor Binding Motif (RBM) while boosting the induction of inner and outer face antibodies. The engineered resurfaced RBD immunogens were stable, lacked binding to monoclonal antibodies with limited breadth, and maintained strong interactions with target broadly neutralizing antibodies. When used as vaccines, they limited humoral responses against the RBM as intended. Multimerization on nanoparticles further increased the immunogenicity of the resurfaced RBDs immunogens, thus supporting resurfacing as a promising immunogen design approach to rationally shift natural immune responses to develop more protective vaccines.

CATH v4.4: major expansion of CATH by experimental and predicted structural data

CATH (https://www.cathdb.info) is a structural classification database that assigns domains to the structures in the Protein Data Bank (PDB) and AlphaFold Protein Structure Database (AFDB) and adds layers of biological information, including homology and functional annotation. This article covers developments in the CATH classification since 2021. We report the significant expansion of structural information (180-fold) for CATH superfamilies through classification of PDB domains and predicted domain structures from the Encyclopedia of Domains (TED) resource. TED provides information on predicted domains in AFDB. CATH v4.4 represents an expansion of ∼64 844 experimentally determined domain structures from PDB. We also present a mapping of ∼90 million predicted domains from TED to CATH superfamilies. New PDB and TED data increases the number of superfamilies from 5841 to 6573, folds from 1349 to 2078 and architectures from 41 to 77. TED data comprises predicted structures, so these new folds and architectures remain hypothetical until experimentally confirmed. CATH also classifies domains into functional families (FunFams) within a superfamily. We have updated sequences in FunFams by scanning FunFam-HMMs against UniProt release 2024_02, giving a 276% increase in FunFams coverage. The mapping of TED structural domains has resulted in a 4-fold increase in FunFams with structural information.

Exploitation of phylum-spanning omics resources reveals complexity in the nematode FLP signalling system and provides insights into flp-gene evolution

BACKGROUND

Parasitic nematodes significantly undermine global human and animal health and productivity. Parasite control is reliant on anthelmintic administration however over-use of a limited number of drugs has resulted in escalating parasitic nematode resistance, threatening the sustainability of parasite control and underscoring an urgent need for the development of novel therapeutics. FMRFamide-like peptides (FLPs), the largest family of nematode neuropeptides, modulate nematode behaviours including those important for parasite survival, highlighting FLP receptors (FLP-GPCRs) as appealing putative novel anthelmintic targets. Advances in omics resources have enabled the identification of FLPs and neuropeptide-GPCRs in some parasitic nematodes, but remaining gaps in FLP-ligand libraries hinder the characterisation of receptor-ligand interactions, which are required to drive the development of novel control approaches.

RESULTS

In this study we exploited recent expansions in nematode genome data to identify 2143 flp-genes in > 100 nematode species across free-living, entomopathogenic, plant, and animal parasitic lifestyles and representing 7 of the 12 major nematode clades. Our data reveal that: (i) the phylum-spanning flps, flp-1, -8, -14, and - 18, may be representative of the flp profile of the last common ancestor of nematodes; (ii) the majority of parasitic nematodes have a reduced flp complement relative to free-living species; (iii) FLP prepropeptide architecture is variable within and between flp-genes and across nematode species; (iv) FLP prepropeptide signatures facilitate flp-gene discrimination; (v) FLP motifs display variable length, amino acid sequence, and conservation; (vi) CLANS analysis provides insight into the evolutionary history of flp-gene sequelogues and reveals putative flp-gene paralogues and, (vii) flp expression is upregulated in the infective larval stage of several nematode parasites.

CONCLUSIONS

These data provide the foundation required for phylum-spanning FLP-GPCR deorphanisation screens in nematodes to seed the discovery and development of novel parasite control approaches.

A conserved fertilization complex bridges sperm and egg in vertebrates

Fertilization, the basis for sexual reproduction, culminates in the binding and fusion of sperm and egg. Although several proteins are known to be crucial for this process in vertebrates, the molecular mechanisms remain poorly understood. Using an AlphaFold-Multimer screen, we identified the protein Tmem81 as part of a conserved trimeric sperm complex with the essential fertilization factors Izumo1 and Spaca6. We demonstrate that Tmem81 is essential for male fertility in zebrafish and mice. In line with trimer formation, we show that Izumo1, Spaca6, and Tmem81 interact in zebrafish sperm and that the human orthologs interact in vitro. Notably, complex formation creates the binding site for the egg fertilization factor Bouncer in zebrafish. Together, our work presents a comprehensive model for fertilization across vertebrates, where a conserved sperm complex binds to divergent egg proteins-Bouncer in fish and JUNO in mammals-to mediate sperm-egg interaction.

Evolution of sequence, structural and functional diversity of the ubiquitous DNA/RNA-binding Alba domain

The DNA/RNA-binding Alba domain is prevalent across all kingdoms of life. First discovered in archaea, this protein domain has evolved from RNA- to DNA-binding, with a concomitant expansion in the range of cellular processes that it regulates. Despite its widespread presence, the full extent of its sequence, structural, and functional diversity remains unexplored. In this study, we employed iterative searches in PSI-BLAST to identify 15,161 unique Alba domain-containing proteins from the NCBI non-redundant protein database. Sequence similarity network (SSN) analysis clustered them into 13 distinct subgroups, including the archaeal Alba and eukaryotic Rpp20/Pop7 and Rpp25/Pop6 groups, as well as novel fungal and Plasmodium-specific Albas. Sequence and structural conservation analysis of the subgroups indicated high preservation of the dimer interface, with Alba domains from unicellular eukaryotes notably exhibiting structural deviations towards their C-terminal end. Finally, phylogenetic analysis, while supporting SSN clustering, revealed the evolutionary branchpoint at which the eukaryotic Rpp20- and Rpp25-like clades emerged from archaeal Albas, and the subsequent taxonomic lineage-based divergence within each clade. Taken together, this comprehensive analysis enhances our understanding of the evolutionary history of Alba domain-containing proteins across diverse organisms.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

Structure and RNA-binding of the helically extended Roquin CCCH-type zinc finger

Zinc finger (ZnF) domains appear in a pool of structural contexts and despite their small size achieve varying target specificities, covering single-stranded and double-stranded DNA and RNA as well as proteins. Combined with other RNA-binding domains, ZnFs enhance affinity and specificity of RNA-binding proteins (RBPs). The ZnF-containing immunoregulatory RBP Roquin initiates mRNA decay, thereby controlling the adaptive immune system. Its unique ROQ domain shape-specifically recognizes stem-looped cis-elements in mRNA 3'-untranslated regions (UTR). The N-terminus of Roquin contains a RING domain for protein-protein interactions and a ZnF, which was suggested to play an essential role in RNA decay by Roquin. The ZnF domain boundaries, its RNA motif preference and its interplay with the ROQ domain have remained elusive, also driven by the lack of high-resolution data of the challenging protein. We provide the solution structure of the Roquin-1 ZnF and use an RBNS-NMR pipeline to show that the ZnF recognizes AU-rich RNAs. We systematically refine the contributions of adenines in a poly(U)-background to specific complex formation. With the simultaneous binding of ROQ and ZnF to a natural target transcript of Roquin, our study for the first time suggests how Roquin integrates RNA shape and sequence features through the ROQ-ZnF tandem.

Clustering protein functional families at large scale with hierarchical approaches

Proteins, fundamental to cellular activities, reveal their function and evolution through their structure and sequence. CATH functional families (FunFams) are coherent clusters of protein domain sequences in which the function is conserved across their members. The increasing volume and complexity of protein data enabled by large-scale repositories like MGnify or AlphaFold Database requires more powerful approaches that can scale to the size of these new resources. In this work, we introduce MARC and FRAN, two algorithms developed to build upon and address limitations of GeMMA/FunFHMMER, our original methods developed to classify proteins with related functions using a hierarchical approach. We also present CATH-eMMA, which uses embeddings or Foldseek distances to form relationship trees from distance matrices, reducing computational demands and handling various data types effectively. CATH-eMMA offers a highly robust and much faster tool for clustering protein functions on a large scale, providing a new tool for future studies in protein function and evolution.

CATH 2024: CATH-AlphaFlow Doubles the Number of Structures in CATH and Reveals Nearly 200 New Folds

CATH (https://www.cathdb.info) classifies domain structures from experimental protein structures in the PDB and predicted structures in the AlphaFold Database (AFDB). To cope with the scale of the predicted data a new NextFlow workflow (CATH-AlphaFlow), has been developed to classify high-quality domains into CATH superfamilies and identify novel fold groups and superfamilies. CATH-AlphaFlow uses a novel state-of-the-art structure-based domain boundary prediction method (ChainSaw) for identifying domains in multi-domain proteins. We applied CATH-AlphaFlow to process PDB structures not classified in CATH and AFDB structures from 21 model organisms, expanding CATH by over 100%. Domains not classified in existing CATH superfamilies or fold groups were used to seed novel folds, giving 253 new folds from PDB structures (September 2023 release) and 96 from AFDB structures of proteomes of 21 model organisms. Where possible, functional annotations were obtained using (i) predictions from publicly available methods (ii) annotations from structural relatives in AFDB/UniProt50. We also predicted functional sites and highly conserved residues. Some folds are associated with important functions such as photosynthetic acclimation (in flowering plants), iron permease activity (in fungi) and post-natal spermatogenesis (in mice). CATH-AlphaFlow will allow us to identify many more CATH relatives in the AFDB, further characterising the protein structure landscape.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation

MOTIVATION

Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes.

RESULTS

We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance.

AVAILABILITY AND IMPLEMENTATION

https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Conserved proline residues prevent dimerization and aggregation in the β-lactamase BlaC

Evolution leads to conservation of amino acid residues in protein families. Conserved proline residues are usually considered to ensure the correct folding and to stabilize the three-dimensional structure. Surprisingly, proline residues that are highly conserved in class A β-lactamases were found to tolerate various substitutions without large losses in enzyme activity. We investigated the roles of three conserved prolines at positions 107, 226, and 258 in the β-lactamase BlaC from Mycobacterium tuberculosis and found that mutations can lead to dimerization of the enzyme and an overall less stable protein that is prone to aggregate over time. For the variant Pro107Thr, the crystal structure shows dimer formation resembling domain swapping. It is concluded that the proline substitutions loosen the structure, enhancing multimerization. Even though the enzyme does not lose its properties without the conserved proline residues, the prolines ensure the long-term structural integrity of the enzyme.

Type 2 vomeronasal receptor-A4 subfamily: Potential predator sensors in mice

Sensory signals detected by olfactory sensory organs are critical regulators of animal behavior. An accessory olfactory organ, the vomeronasal organ, detects cues from other animals and plays a pivotal role in intra- and inter-species interactions in mice. However, how ethologically relevant cues control mouse behavior through approximately 350 vomeronasal sensory receptor proteins largely remains elusive. The type 2 vomeronasal receptor-A4 (V2R-A4) subfamily members have been repeatedly detected from vomeronasal sensory neurons responsive to predator cues, suggesting a potential role of this receptor subfamily as a sensor for predators. This review focuses on this intriguing subfamily, delving into its receptor functions and genetic characteristics.

Mechanical communication within the microtubule through network-based analysis of tubulin dynamics

The identification of the mechanisms underlying the transfer of mechanical vibrations in protein complexes is crucial to understand how these super-assemblies are stabilized to perform specific functions within the cell. In this context, the study of the structural communication and the propagation of mechanical stimuli within the microtubule (MT) is important given the pivotal role of the latter in cell viability. In this study, we employed molecular modelling and the dynamical network analysis approaches to analyse the MT. The results highlight that β -tubulin drives the transfer of mechanical information between protofilaments (PFs), which is altered at the seam due to a different interaction pattern. Moreover, while the key residues involved in the structural communication along the PF are generally conserved, a higher diversity was observed for amino acids mediating the lateral communication. Taken together, these results might explain why MTs with different PF numbers are formed in different organisms or with different β -tubulin isotypes.

Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes

Tetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.

Structure of glyceraldehyde-3-phosphate dehydrogenase from Paracoccidioides lutzii in complex with an aldonic sugar acid

The pathogen Paracoccidioides lutzii (Pb01) is found in South America countries Colombia, Ecuador, Venezuela and Brazil, especially in the central, west, and north regions of the latter. It belongs to the Ajellomycetaceae family, Onygenales order, and is typically thermodimorphic, presenting yeast cells when it grows in animal tissues, but mycelia when in the environment, where it produces the infectious propagule. This fungus is one of the etiologic agents of Paracoccidioidomycosis (PCM), the most important endemic fungal infection in Latin America. Investigations on its genome have contributed to a better understanding about its metabolism and revealed the complexity of several metabolic glycolytic pathways. Glyceraldehyde-3-Phosphate Dehydrogenase from Paracoccidioides lutzii (PlGAPDH) is considered a moonlighting protein and participates in several biological processes of this pathogen. The enzyme was expressed and purified, as seen in SDS-PAGE gel, crystallized and had its three dimensional structure (3D) determined in complex with NAD+, a sulphate ion and d-galactonic acid, therefore, a type of 'GAA site'. It is the first GAPDH structure to show this chemical type in this site and how this protein can bind an acid derived from oxidation of a linear hexose.

Whole-exome sequencing reveals genetic variants that may play a role in neurocytomas

OBJECTIVES

Neurocytomas (NCs) are rare intracranial tumors that can often be surgically resected. However, disease course is unpredictable in many patients and medical therapies are lacking. We have used whole exome sequencing to explore the molecular etiology for neurocytoma and assist in target identification to develop novel therapeutic interventions.

METHODS

We used whole exome sequencing (WES) to compare the molecular landscape of 21 primary & recurrent NCs to five normal cerebellar control samples. WES data was analyzed using the Qiagen Clinical Insight program, variants of interest (VOI) were interrogated using ConSurf, ScoreCons, & Ingenuity Pathway Analysis Software to predict their potential functional effects, and Copy number variations (CNVs) in the genes of interest were analyzed by Genewiz (Azenta Life Sciences).

RESULTS

Of 40 VOI involving thirty-six genes, 7 were pathogenic, 17 likely-pathogenic, and 16 of uncertain-significance. Of seven pathogenic NC associated variants, Glucosylceramidase beta 1 [GBA1 c.703T > C (p.S235P)] was mutated in 5/21 (24%), Coagulation factor VIII [F8 c.3637dupA (p.I1213fs*28)] in 4/21 (19%), Phenylalanine hydroxylase [PAH c.975C > A (p.Y325*)] in 3/21 (14%), and Fanconi anemia complementation group C [FANCC c.1162G > T (p.G388*)], Chromodomain helicase DNA binding protein 7 [CHD7 c.2839C > T (p.R947*)], Myosin VIIA [MYO7A c.940G > T (p.E314*)] and Dynein axonemal heavy chain 11 [DNAH11 c.3544C > T (p.R1182*)] in 2/21 (9.5%) NCs respectively. CNVs were noted in 85% of these latter 7 genes. Interestingly, a Carboxy-terminal domain RNA polymerase II polypeptide A small phosphatase 2 [CTDSP2 c.472G > A (p.E158K)] of uncertain significance was also found in > 70% of NC cases.

INTERPRETATION

The variants of interest we identified in the NCs regulate a variety of neurological processes including cilia motility, cell metabolism, immune responses, and DNA damage repair and provide novel insights into the molecular pathogenesis of these extremely rare tumors.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.

Interaction between the mitochondrial adaptor MIRO and the motor adaptor TRAK

MIRO (mitochondrial Rho GTPase) consists of two GTPase domains flanking two Ca2+-binding EF-hand domains. A C-terminal transmembrane helix anchors MIRO to the outer mitochondrial membrane, where it functions as a general adaptor for the recruitment of cytoskeletal proteins that control mitochondrial dynamics. One protein recruited by MIRO is TRAK (trafficking kinesin-binding protein), which in turn recruits the microtubule-based motors kinesin-1 and dynein-dynactin. The mechanism by which MIRO interacts with TRAK is not well understood. Here, we map and quantitatively characterize the interaction of human MIRO1 and TRAK1 and test its potential regulation by Ca2+ and/or GTP binding. TRAK1 binds MIRO1 with low micromolar affinity. The interaction was mapped to a fragment comprising MIRO1's EF-hands and C-terminal GTPase domain and to a conserved sequence motif within TRAK1 residues 394 to 431, immediately C-terminal to the Spindly motif. This sequence is sufficient for MIRO1 binding in vitro and is necessary for MIRO1-dependent localization of TRAK1 to mitochondria in cells. MIRO1's EF-hands bind Ca2+ with dissociation constants (KD) of 3.9 μM and 300 nM. This suggests that under cellular conditions one EF-hand may be constitutively bound to Ca2+ whereas the other EF-hand binds Ca2+ in a regulated manner, depending on its local concentration. Yet, the MIRO1-TRAK1 interaction is independent of Ca2+ binding to the EF-hands and of the nucleotide state (GDP or GTP) of the C-terminal GTPase. The interaction is also independent of TRAK1 dimerization, such that a TRAK1 dimer can be expected to bind two MIRO1 molecules on the mitochondrial surface.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

DeepBSRPred: deep learning-based binding site residue prediction for proteins

MOTIVATION

Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes.

RESULTS

We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods.

AVAILABILITY AND IMPLEMENTATION

The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .

A genomic platform for surveillance and antigen discovery in Plasmodium spp. using long-read amplicon sequencing

Many vaccine candidate proteins in the malaria parasite Plasmodium falciparum are under strong immunological pressure and confer antigenic diversity. We present a sequencing and data analysis platform for the genomic surveillance of the insertion or deletion (indel)-rich antigens merozoite surface protein 1 (MSP1), MSP2, glutamate-rich protein (GLURP), and CSP from P. falciparum using long-read circular consensus sequencing (CCS) in multiclonal malaria isolates. Our platform uses 40 PCR primers per gene to asymmetrically barcode and identify multiclonal infections in pools of up to 384 samples. With msp2, we validated the method using 235 mock infections combining 10 synthetic variants at different concentrations and infection complexities. We applied this strategy to P. falciparum isolates from a longitudinal cohort in Tanzania. Finally, we constructed an analysis pipeline that streamlines the processing and interpretation of epidemiological and antigenic diversity data from demultiplexed FASTQ files. This platform can be easily adapted to other polymorphic antigens of interest in Plasmodium or any other human pathogen.

Structural insights into human TFIIIC promoter recognition

Transcription factor (TF) IIIC recruits RNA polymerase (Pol) III to most of its target genes. Recognition of intragenic A- and B-box motifs in transfer RNA (tRNA) genes by TFIIIC modules τA and τB is the first critical step for tRNA synthesis but is mechanistically poorly understood. Here, we report cryo-electron microscopy structures of the six-subunit human TFIIIC complex unbound and bound to a tRNA gene. The τB module recognizes the B-box via DNA shape and sequence readout through the assembly of multiple winged-helix domains. TFIIIC220 forms an integral part of both τA and τB connecting the two subcomplexes via a ~550-amino acid residue flexible linker. Our data provide a structural mechanism by which high-affinity B-box recognition anchors TFIIIC to promoter DNA and permits scanning for low-affinity A-boxes and TFIIIB for Pol III activation.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Bacterial NLR-related proteins protect against phage

Bacteria use a wide range of immune pathways to counter phage infection. A subset of these genes shares homology with components of eukaryotic immune systems, suggesting that eukaryotes horizontally acquired certain innate immune genes from bacteria. Here, we show that proteins containing a NACHT module, the central feature of the animal nucleotide-binding domain and leucine-rich repeat containing gene family (NLRs), are found in bacteria and defend against phages. NACHT proteins are widespread in bacteria, provide immunity against both DNA and RNA phages, and display the characteristic C-terminal sensor, central NACHT, and N-terminal effector modules. Some bacterial NACHT proteins have domain architectures similar to the human NLRs that are critical components of inflammasomes. Human disease-associated NLR mutations that cause stimulus-independent activation of the inflammasome also activate bacterial NACHT proteins, supporting a shared signaling mechanism. This work establishes that NACHT module-containing proteins are ancient mediators of innate immunity across the tree of life.

Role of the proline-rich disordered domain of DROSHA in intronic microRNA processing

DROSHA serves as a gatekeeper of the microRNA (miRNA) pathway by processing primary transcripts (pri-miRNAs). While the functions of structured domains of DROSHA have been well documented, the contribution of N-terminal proline-rich disordered domain (PRD) remains elusive. Here we show that the PRD promotes the processing of miRNA hairpins located within introns. We identified a DROSHA isoform (p140) lacking the PRD, which is produced by proteolytic cleavage. Small RNA sequencing revealed that p140 is significantly impaired in the maturation of intronic miRNAs. Consistently, our minigene constructs demonstrated that PRD enhances the processing of intronic hairpins, but not those in exons. Splice site mutations did not affect the PRD's enhancing effect on intronic constructs, suggesting that the PRD acts independently of splicing reaction by interacting with sequences residing within introns. The N-terminal regions from zebrafish and Xenopus DROSHA can replace the human counterpart, indicating functional conservation despite poor sequence alignment. Moreover, we found that rapidly evolving intronic miRNAs are generally more dependent on PRD than conserved ones, suggesting a role of PRD in miRNA evolution. Our study reveals a new layer of miRNA regulation mediated by a low-complexity disordered domain that senses the genomic contexts of miRNA loci.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Using evolutionary data to make sense of macromolecules with a "face-lifted" ConSurf

The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

Experimental and clinical data analysis for identification of COVID-19 resistant ACE2 mutations

The high magnitude zoonotic event has caused by Severe Acute Respitarory Syndrome CoronaVirus-2 (SARS-CoV-2) is Coronavirus Disease-2019 (COVID-19) epidemics. This disease has high rate of spreading than mortality in humans. The human receptor, Angiotensin-Converting Enzyme 2 (ACE2), is the leading target site for viral Spike-protein (S-protein) that function as binding ligands and are responsible for their entry in humans. The patients infected with COVID-19 with comorbidities, particularly cancer patients, have a severe effect or high mortality rate because of the suppressed immune system. Nevertheless, there might be a chance wherein cancer patients cannot be infected with SARS-CoV-2 because of mutations in the ACE2, which may be resistant to the spillover between species. This study aimed to determine the mutations in the sequence of the human ACE2 protein and its dissociation with SARS-CoV-2 that might be rejecting viral transmission. The in silico approaches were performed to identify the impact of SARS-CoV-2 S-protein with ACE2 mutations, validated experimentally, occurred in the patient, and reported in cell lines. The identified changes significantly affect SARS-CoV-2 S-protein interaction with ACE2, demonstrating the reduction in the binding affinity compared to SARS-CoV. The data presented in this study suggest ACE2 mutants have a higher and lower affinity with SARS-Cov-2 S-protein to the wild-type human ACE2 receptor. This study would likely be used to report SARS-CoV-2 resistant ACE2 mutations and can be used to design active peptide development to inactivate the viral spread of SARS-CoV-2 in humans.

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Deep-learning (DL) methods like DeepMind's AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification. The remaining cluster into 2367 putative novel superfamilies. Detailed manual analysis on 618 of these, having at least one human relative, reveal extremely remote homologies and further unusual features. Only 25 novel superfamilies could be confirmed. Although most models map to existing superfamilies, AF2 domains expand CATH by 67% and increases the number of unique 'global' folds by 36% and will provide valuable insights on structure function relationships. CATH-Assign will harness the huge expansion in structural data provided by DeepMind to rationalise evolutionary changes driving functional divergence.

KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.

Mapping the Constrained Coding Regions in the Human Genome to Their Corresponding Proteins

Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD) [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population. More surprisingly, we also found that linear motifs, linear interacting peptides (LIPs), disorder-order transitions upon binding with other protein partners and liquid-liquid phase separating (LLPS) regions are also strongly associated with high constraint for variability. We also compared intra-species constraints in the human CCRs with inter-species conservation and functional residues to explore how such CCRs may contribute to the analysis of protein variants. As has been previously observed, CCRs are only weakly correlated with conservation, suggesting that intraspecies constraints complement interspecies conservation and can provide more information to interpret variant effects.

Mutagenesis studies of TRPV1 subunit interfaces informed by genomic variant analysis

Protein structures and mutagenesis studies have been instrumental in elucidating molecular mechanisms of ion channel function, but making informed choices about which residues to target for mutagenesis can be challenging. Therefore, we investigated the potential for using human population genomic data to further refine our selection of mutagenesis sites in TRPV1. Single nucleotide polymorphism data of TRPV1 from gnomAD 2.1.1 revealed a lower number of missense variants within buried residues of the ankyrin repeat domain and an increased number of variants between secondary structure elements of the transmembrane segments. We hypothesized that residues critical to interactions at interfaces between subunits or domains in the channel would exhibit a similar reduction in variants. We identified in the structure of ground squirrel TRPV1 (PDB: 7LQY) a possible electrostatic network between K155 and K160 in the N-terminal ankyrin repeat domain and E761 and D762 in the C-terminus (K-KED). Consistent with our hypothesis for residues at key interface sites, none of the four residues have any variants reported in gnomAD 2.1.1. Ca2+ imaging of TRPV1 K-KED mutants confirmed significant roles for these residues, but we found that the electrostatic interaction is not essential since channel function is still observed in total charge reversals on the C-terminal side of the interface (E761K/D762K). Interestingly, Ca2+ imaging responses for a charge swap experiment with K155D/D762K showed partially restored wild-type responses. Using electrophysiology, we found that charge reversals on either K155 or D762 increased the baseline currents of TRPV1, and the charge swapped double mutant, K155D/D762K, partially restored baseline currents to wild-type levels. We interpret these results to mean that contacts across residues in the K-KED interface shift the equilibria of conformations to closed pore states. Our study demonstrates the utility and applicability of a combined missense variant and structure targeted investigation of residues at TRPV1 subunit interfaces.

CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models

MOTIVATION

CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set.

RESULTS

The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3%, respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate <0.5%), we were able to provide CATH annotations for 4.62 million Pfam domains. For a subset of these domains from Homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold2 structures with structures from the CATH superfamilies to which they were assigned.

AVAILABILITY AND IMPLEMENTATION

The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases

Influenza and COVID-19 are infectious respiratory diseases that represent a major concern to public health with social and economic impact worldwide, for which the available therapeutic options are not satisfactory. The RdRp has a central role in viral replication and thus represents a major target for the development of antiviral approaches. In this study, we focused on Influenza A virus PB1 polymerase protein and the betacoronaviruses nsp12 polymerase protein, considering their functional and structural similarities. We have performed conservation and druggability analysis to map conserved druggable regions, that may have functional or structural importance in these proteins. We disclosed the most promising and new targeting regions for the discovery of new potential polymerase inhibitors. Conserved druggable regions of putative interaction with favipiravir and molnupiravir were also mapped. We have also compared and integrated the current findings with previous research.

The arithmetic topology of genetic alignments

We propose a novel mathematical paradigm for the study of genetic variation in sequence alignments. This framework originates from extending the notion of pairwise relations, upon which current analysis is based on, to k-ary dissimilarity. This dissimilarity naturally leads to a generalization of simplicial complexes by endowing simplices with weights, compatible with the boundary operator. We introduce the notion of k-stances and dissimilarity complex, the former encapsulating arithmetic as well as topological structure expressing these k-ary relations. We study basic mathematical properties of dissimilarity complexes and show how this approach captures watershed moments of viral dynamics in the context of SARS-CoV-2 and H1N1 flu genomic data.

DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins.

Recent changes in the mutational dynamics of the SARS-CoV-2 main protease substantiate the danger of emerging resistance to antiviral drugs

Introduction

The current coronavirus pandemic is being combated worldwide by nontherapeutic measures and massive vaccination programs. Nevertheless, therapeutic options such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main-protease (Mpro) inhibitors are essential due to the ongoing evolution toward escape from natural or induced immunity. While antiviral strategies are vulnerable to the effects of viral mutation, the relatively conserved Mpro makes an attractive drug target: Nirmatrelvir, an antiviral targeting its active site, has been authorized for conditional or emergency use in several countries since December 2021, and a number of other inhibitors are under clinical evaluation. We analyzed recent SARS-CoV-2 genomic data, since early detection of potential resistances supports a timely counteraction in drug development and deployment, and discovered accelerated mutational dynamics of Mpro since early December 2021.

Methods

We performed a comparative analysis of 10.5 million SARS-CoV-2 genome sequences available by June 2022 at GISAID to the NCBI reference genome sequence NC_045512.2. Amino-acid exchanges within high-quality regions in 69,878 unique Mpro sequences were identified and time- and in-depth sequence analyses including a structural representation of mutational dynamics were performed using in-house software.

Results

The analysis showed a significant recent event of mutational dynamics in Mpro. We report a remarkable increase in mutational variability in an eight-residue long consecutive region (R188-G195) near the active site since December 2021.

Discussion

The increased mutational variability in close proximity to an antiviral-drug binding site as described herein may suggest the onset of the development of antiviral resistance. This emerging diversity urgently needs to be further monitored and considered in ongoing drug development and lead optimization.

Gene family evolution and natural selection signatures in Datura spp. (Solanaceae)

Elucidating the diversification process of congeneric species makes it necessary to identify the factors promoting species variation and diversification. Comparative gene family analysis allows us to elucidate the evolutionary history of species by identifying common genetic/genomic mechanisms underlying species responses to biotic and abiotic environments at the genomic level. In this study, we analyzed the high-quality transcriptomes of four Datura species, D. inoxia, D. pruinosa, D. stramonium, and D. wrightii. We performed a thorough comparative gene family analysis to infer the role of selection in molecular variation, changes in protein physicochemical properties, and gain/loss of genes during their diversification processes. The results revealed common and species-specific signals of positive selection, physicochemical divergence and/or expansion of metabolic genes (e.g., transferases and oxidoreductases) associated with terpene and tropane metabolism and some resistance genes (R genes). The gene family analysis presented here is a valuable tool for understanding the genome evolution of economically and ecologically significant taxa such as the Solanaceae family.

Structural and energetic analyses of SARS-CoV-2 N-terminal domain characterise sugar binding pockets and suggest putative impacts of variants on COVID-19 transmission

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic that causes significant health/socioeconomic burden. Variants of concern (VOCs) have emerged affecting transmissibility, disease severity and re-infection risk. Studies suggest that the - N-terminal domain (NTD) of the spike protein may have a role in facilitating virus entry via sialic-acid receptor binding. Furthermore, most VOCs include novel NTD variants. Despite global sequence and structure similarity, most sialic-acid binding pockets in NTD vary across coronaviruses. Our work suggests ongoing evolutionary tuning of the sugar-binding pockets and recent analyses have shown that NTD insertions in VOCs tend to lie close to loops. We extended the structural characterisation of these sugar-binding pockets and explored whether variants could enhance sialic acid-binding. We found that recent NTD insertions in VOCs (i.e., Gamma, Delta and Omicron variants) and emerging variants of interest (VOIs) (i.e., Iota, Lambda and Theta variants) frequently lie close to sugar-binding pockets. For some variants, including the recent Omicron VOC, we find increases in predicted sialic acid-binding energy, compared to the original SARS-CoV-2, which may contribute to increased transmission. These binding observations are supported by molecular dynamics simulations (MD). We examined the similarity of NTD across Betacoronaviruses to determine whether the sugar-binding pockets are sufficiently similar to be exploited in drug design. Whilst most pockets are too structurally variable, we detected a previously unknown highly structurally conserved pocket which can be investigated in pursuit of a generic pan-Betacoronavirus drug. Our structure-based analyses help rationalise the effects of VOCs and provide hypotheses for experiments. Our findings suggest a strong need for experimental monitoring of changes in NTD of VOCs.

Mollusc Crystallins: Physical and Chemical Properties and Phylogenetic Analysis

The purpose of the present study was to perform bioinformatic analysis of crystallin diversity in aquatic molluscs based on the sequences in the NCBI Protein database. The objectives were as follows: (1) analysis of some physical and chemical properties of mollusc crystallins, (2) comparison of mollusc crystallins with zebrafish and cubomedusa Tripedalia cystophora crystallins, and (3) determination of the most probable candidates for the role of gastropod eye crystallins. The calculated average GRAVY values revealed that the majority of the seven crystallin groups, except for μ- and ζ-crystallins, were hydrophilic proteins. The predominant predicted secondary structures of the crystallins in most cases were α-helices and coils. The highest values of refractive index increment (dn/dc) were typical for crystallins of aquatic organisms with known lens protein composition (zebrafish, cubomedusa, and octopuses) and for S-crystallin of Pomacea canaliculata. The evolutionary relationships between the studied crystallins, obtained from multiple sequence alignments using Clustal Omega and MUSCLE, and the normalized conservation index, calculated by Mirny, showed that the most conservative proteins were Ω-crystallins but the most diverse were S-crystallins. The phylogenetic analysis of crystallin was generally consistent with modern mollusc taxonomy. Thus, α- and S-, and, possibly, J1A-crystallins, can be assumed to be the most likely candidates for the role of gastropod lens crystallins.

Architecture of the yeast Pol III pre-termination complex and pausing mechanism on poly(dT) termination signals

RNA polymerase (Pol) III is specialized to transcribe short, abundant RNAs, for which it terminates transcription on polythymine (dT) stretches on the non-template (NT) strand. When Pol III reaches the termination signal, it pauses and forms the pre-termination complex (PTC). Here, we report cryoelectron microscopy (cryo-EM) structures of the yeast Pol III PTC and complementary functional states at resolutions of 2.7-3.9 Å. Pol III recognizes the poly(dT) termination signal with subunit C128 that forms a hydrogen-bond network with the NT strand and, thereby, induces pausing. Mutating key interacting residues interferes with transcription termination in vitro, impairs yeast growth, and causes global termination defects in vivo, confirming our structural results. Additional cryo-EM analysis reveals that C53-C37, a Pol III subcomplex and key termination factor, participates indirectly in Pol III termination. We propose a mechanistic model of Pol III transcription termination and rationalize why Pol III, unlike Pol I and Pol II, terminates on poly(dT) signals.

Rotavirus vaccine-derived cases in Belgium: Evidence for reversion of attenuating mutations and alternative causes of gastroenteritis

Since the introduction of live-attenuated rotavirus vaccines in Belgium in 2006, surveillance has routinely detected rotavirus vaccine-derived strains. However, their genomic landscape and potential role in gastroenteritis have not been thoroughly investigated. We compared VP7 and VP4 nucleotide sequences obtained from rotavirus surveillance with the Rotarix vaccine sequence. As a result, we identified 80 vaccine-derived strains in 5125 rotavirus-positive infants with gastroenteritis from 2007 to 2018. Using both viral metagenomics and reverse transcription qPCR, we evaluated the vaccine strains and screened for co-infecting enteropathogens. Among the 45 patients with known vaccination status, 39 were vaccinated and 87% received the vaccine less than a month before the gastroenteritis episode. Reconstruction of 30 near complete vaccine-derived genomes revealed 0-11 mutations per genome, with 88% of them being non-synonymous. This, in combination with several shared amino acid changes among strains, pointed at selection of minor variant(s) present in the vaccine. We also found that some of these substitutions were true revertants (e.g., F167L on VP4, and I45T on NSP4). Finally, co-infections with known (e.g., Clostridioides difficile and norovirus) and divergent or emerging (e.g., human parechovirus A1, salivirus A2) pathogens were detected, and we estimated that 35% of the infants likely had gastroenteritis due to a 'non-rotavirus' cause. Conversely, we could not rule out the vaccine-derived gastroenteritis in over half of the cases. Continued studies inspecting reversion to pathogenicity should monitor the long-time safety of live-attenuated rotavirus vaccines. All in all, the complementary approach with NGS and qPCR provided a better understanding of rotavirus vaccine strain evolution in the Belgian population and epidemiology of co-infecting enteropathogens in suspected rotavirus vaccine-derived gastroenteritis cases.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.