mTM-align: an algorithm for fast and accurate multiple protein structure alignment

Exploring the Structural Diversity and Evolution of the D1 Subunit of Photosystem II Using AlphaFold and Foldtree

Although our knowledge of photosystem II has expanded to include time-resolved atomic details, the diversity of experimental structures of the enzyme remains limited. Recent advances in protein structure prediction with AlphaFold offer a promising approach to fill this gap in structural diversity in non-model systems. This study used AlphaFold to predict the structures of the D1 protein, the core subunit of photosystem II, across a broad range of photosynthetic organisms. The prediction produced high-confidence structures, and structural alignment analyses highlighted conserved regions across the different D1 groups, which were in line with high pLDDT scoring regions. In contrast, varying pLDDT in the DE loop and terminal regions appears to correlate with different degrees of structural flexibility or disorder. Subsequent structural phylogenetic analysis using Foldtree provided a tree that is in good agreement with previous sequence-based studies. Moreover, the phylogeny supports a parsimonious scenario in which far-red D1 and D1INT evolved from an ancestral form of G4 D1. This work demonstrates the potential of AlphaFold and Foldtree to study the molecular evolution of photosynthesis.

In silico characterization of the interaction of AKR1B1 with the deacetylase activation domain (DAD)

The aldo-keto reductase family 1 member B1 (AKR1B1) plays a key role in cancer progression by competing with histone deacetylase 3 to bind to the deacetylase activation domain (DAD) of the nuclear receptor corepressor SMRT. Previous studies showed that the L289A mutation in AKR1B1 disrupts its ability to form a dimer with DAD but further details of this interaction remain uncharacterized. This study aimed to model the DAD/AKR1B1 dimer by molecular docking and characterize the complex using molecular dynamics simulations. We identified a new model with increased structural stability for AKR1B1, reduced disruption of secondary structures of DAD, and lower ΔG than a previously reported one. In silico mutagenesis of AKR1B1 assessed the contributions from individual residues. We identified six hotspot residues that mediate the complex interface. Those residues are located in the α8 and H2 alpha helices of AKR1B1 and include the experimentally determined L289. These results propose new hypotheses regarding the interaction between DAD and AKR1B1, guiding future experimental approaches.

Polysaccharide degradation in an Antarctic bacterium: Discovery of glycoside hydrolases from remote regions of the sequence space

Glycoside hydrolases (GHs) are enzymes involved in the degradation of oligosaccharides and polysaccharides. The sequence space of GHs is rapidly expanding due to the increasing number of available sequences. This expansion paves the way for the discovery of novel enzymes with peculiar structural and functional properties. This work is focused on two GHs, Ps_GH5 and Ps_GH50, from the genome of the Antarctic bacterium Pseudomonas sp. ef1. These enzymes are in an unexplored region of the sequence space of their respective GH families, not allowing a reliable sequence-based function prediction. For this reason, a computational pipeline was developed that combines deep learning "dynamic docking" on AlphaFold 3D models with physics-based molecular dynamics simulations to infer their substrate specificity. From in silico screening of a repertoire of potential oligosaccharides, only xylooligosaccharides for Ps_GH5 and galactooligosaccharides for Ps_GH50 emerged as catalytically competent substrates. Biochemical characterization agrees with computational simulations indicating that Ps_GH5 is an endo-β-xylanase, and Ps_GH50 is active mainly on small galactooligosaccharides. In conclusion, this study identifies two novel GHs subfamilies placed in remote regions of the sequence space and highlights the efficacy of substrate specificity prediction by computational approaches in the discovery of new enzymes.

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.

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

Development of Miniprotein-Type Inhibitors of Biofilm Formation in Candida albicans and Candida auris

Candida auris is a pathogenic fungus associated with high-mortality infections and forms resilient biofilms on various surfaces. In this study, we introduced a novel antifungal strategy against C. auris by integrating an AI-powered protein design tool, ProteinMPNN, with classical molecular dynamics (MD) simulations to design artificial proteins from a miniprotein library. This combined approach accelerated and enhanced the design process, enabling the rapid development of effective miniprotein inhibitors specifically targeting C. auris biofilm formation. The miniproteins developed in this study exhibited potent inhibitory effects on C. auris biofilms, representing a significant advancement in antifungal therapy. Notably, the combined application of these miniproteins enhanced suppression of biofilm formation. These findings highlight not only the strong therapeutic potential of these designed miniproteins but also the power of combining AI-driven protein design with MD simulations to advance biomedical research.

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

Identification and functional analysis of a novel L-type lectin (NdLTL1) from Neocaridina denticulata sinensis

This study investigates an L-type lectin, NdLTL1, derived from Neocaridina denticulata sinensis, emphasizing its role in immune defense through carbohydrate binding and bacterial agglutination. Bioinformatics analysis identified 179 lectin sequences, leading to subsequent investigations into the structure and function of NdLTL1. The open reading frame (ORF) of NdLTL1 spans 966 bp and encodes a protein consisting of 321 amino acids (36.25 kDa), which features a signal peptide, a transmembrane domain and Lectin_leg-like domain. Three-dimensional modeling revealed three antiparallel β-sheets characteristic of Lectin_leg-like domain, confirming evolutionary links with proteins such as VIP36. Protein-carbohydrate and protein-protein interaction studies showed that NdLTL1 binds to both carbohydrates like N-acetylglucosamine, peptidoglycan, lipopolysaccharides (LPS), and mannose, as well as sorting proteins (COPI/COPII). Gene expression analyses indicated that NdLTL1 exhibits the highest expression levels in cardiac tissues and significant upregulation in gills following exposure to Vibrio parahaemolyticus. Recombinant NdLTL1 expressed in Escherichia coli was shown to bind multiple bacterial strains and exhibit calcium-dependent agglutination properties. Enzyme-linked immunosorbent assay (ELISA) confirmed concentration-dependent carbohydrate binding, particularly rapid for LPS. In vitro experiments suggested that recombinant NdLTL1 may promote bacterial growth under nutrient-limited conditions while potentially triggering immune defenses indirectly.

multistrap: boosting phylogenetic analyses with structural information

In a phylogeny, trustworthy reliability branch support estimates are as important as the tree itself. We show that reliability support values based on bootstrapping can be improved by combining sequence and structural information from proteins. Our approach relies on the systematic comparison of homologous intra-molecular structural distances. These variations exhibit less saturation than sequence-based Hamming distances and support the computation of tree-like distance matrices resolvable into phylogenetic trees using distance-based methods such as minimum evolution. These trees bear strong similarities to their sequence-based counterparts and allow the estimation of bootstrap support values, but they are sufficiently distinct so that their information content may be combined. The combined sequence and structure bootstrap support values yield improved discrimination between correct and incorrect branches. In this work we show that our approach, named multistrap, is suitable for the improvement of bootstrap branch support values using both predicted and experimental 3D structures.

A crucial active site network of titratable residues guides catalysis and NAD+ binding in human succinic semialdehyde dehydrogenase

Human succinic semialdehyde dehydrogenase is a mitochondrial enzyme fundamental in the neurotransmitter γ-aminobutyric acid catabolism. It catalyzes the NAD+-dependent oxidative degradation of its derivative, succinic semialdehyde, to succinic acid. Mutations in its gene lead to an inherited neurometabolic rare disease, succinic semialdehyde dehydrogenase deficiency, characterized by mental and developmental delay. Due to the poor characterization of this enzyme, we carried out evolutionary and kinetic investigations to contribute to its functional behavior, a prerequisite to interpreting pathogenic variants. An in silico analysis shows that succinic semialdehyde dehydrogenases belong to two families, one human-like and the other of bacterial origin, differing in the oligomeric state and in a network of active site residues. This information is coupled to the biophysical-biochemical characterization of the human recombinant enzyme uncovering that (i) catalysis proceeds by an ordered bi-bi mechanism with NAD+ binding before the aldehyde that exerts a partial non-competitive inhibition; (ii) a stabilizing complex between the catalytic Cys340 and NAD+ is observed and interpreted as a protective mechanism; and (iii) a concerted non-covalent network assists the action of the catalytic residues Cys340 and Glu306. Through mutational analyses of Lys214, Glu306, Cys340, and Glu515 associated with pH studies, we showed that NAD+ binding is controlled by the dyad Lys214-Glu515. Moreover, catalysis is assured by proton transfer exerted by the same dyad networked with the catalytic Glu306, involved in catalytic Cys340 deprotonation/reprotonation. The identification of this weak bond network essential for cofactor binding and catalysis represents a first step to tackling the molecular basis for its deficiency.

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures

Chemical shifts are a readily obtainable NMR observable that can be measured with high accuracy, and because they are sensitive to conformational averages and the local molecular environment, they yield detailed information about protein structure in solution. To predict chemical shifts of protein structures, we introduced the UCBShift method that uniquely fuses a transfer prediction module, which employs sequence and structure alignments to select reference chemical shifts from an experimental database, with a machine learning model that uses carefully curated and physics-inspired features derived from X-ray crystal structures to predict backbone chemical shifts for proteins. In this work, we extend the UCBShift 1.0 method to side chain chemical shift prediction to perform whole protein analysis, which, when validated against well-defined test data shows higher accuracy and better reliability compared to the popular SHIFTX2 method. With the greater abundance of cleaned protein shift-structure data and the modularity of the general UCBShift algorithms, users can gain insight into different features important for residue-specific stabilizing interactions for protein backbone and side chain chemical shift prediction. We suggest several backward and forward applications of UCBShift 2.0 that can help validate AlphaFold structures and probe protein dynamics.

Complete genomes of Asgard archaea reveal diverse integrated and mobile genetic elements

Asgard archaea are of great interest as the progenitors of Eukaryotes, but little is known about the mobile genetic elements (MGEs) that may shape their ongoing evolution. Here, we describe MGEs that replicate in Atabeyarchaeia, a wetland Asgard archaea lineage represented by two complete genomes. We used soil depth-resolved population metagenomic data sets to track 18 MGEs for which genome structures were defined and precise chromosome integration sites could be identified for confident host linkage. Additionally, we identified a complete 20.67 kbp circular plasmid and two family-level groups of viruses linked to Atabeyarchaeia, via CRISPR spacer targeting. Closely related 40 kbp viruses possess a hypervariable genomic region encoding combinations of specific genes for small cysteine-rich proteins structurally similar to restriction-homing endonucleases. One 10.9 kbp integrative conjugative element (ICE) integrates genomically into the Atabeyarchaeum deiterrae-1 chromosome and has a 2.5 kbp circularizable element integrated within it. The 10.9 kbp ICE encodes an expressed Type IIG restriction-modification system with a sequence specificity matching an active methylation motif identified by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing. Restriction-modification of Atabeyarchaeia differs from that of another coexisting Asgard archaea, Freyarchaeia, which has few identified MGEs but possesses diverse defense mechanisms, including DISARM and Hachiman, not found in Atabeyarchaeia. Overall, defense systems and methylation mechanisms of Asgard archaea likely modulate their interactions with MGEs, and integration/excision and copy number variation of MGEs in turn enable host genetic versatility.

Structural insights into the role of deleterious mutations at the dimeric interface of Toll-like receptor interferon-β related adaptor protein

Toll-like receptors (TLRs) are major players in the innate immune system-recognizing pathogens and differentiating self/non-self components of immunity. These proteins are present either on the plasma membrane or endosome and recognize pathogens at their extracellular domains. They are characterized by a single transmembrane helix and an intracellular toll-interleukin-1 receptor (TIR) domain. Few TIRs directly invoke downstream signaling, while others require other TIR domains of adaptors like TIR domain-containing adaptor-inducing interferon-β (TRIF) and TRIF-related adaptor molecule (TRAM). On recognizing pathogenic lipopolysaccharides, TLR4 dimerises and interacts with the intracellular TRAM dimer through the TIR domain to recruit a downstream signaling adaptor (TRIF). We have performed an in-depth study of the structural effect of two mutations (P116H and C117H) at the dimeric interface of the adaptor TRAM, which are known to abrogate downstream signaling. We modeled the structure and performed molecular dynamics studies in order to decipher the structural basis of this effect. We observed that these mutations led to an increased radius of gyration of the complex and resulted in several changes to the interaction energy values when compared against the wild type (WT) and positive control mutants. We identified highly interacting residues as hubs in the WT dimer, and a few such hubs that were lost in the mutant dimers. Changes in the protein residue path, hampering the information flow between the crucial A86/E87/D88/D89 and T155/S156 sites, were observed for the mutants. Overall, we show that such residue changes can have subtle but long-distance effects, impacting the signaling path allosterically.

Whole cell affinity for 4-amino-5-hydroxymethyl-2-methylpyrimidine (HMP) in the marine bacterium Candidatus Pelagibacter st. HTCC7211 explains marine dissolved HMP concentrations

Vitamin B1 is a universally required coenzyme in carbon metabolism. However, most marine microorganisms lack the complete biosynthetic pathway for this compound and must acquire thiamin, or precursor molecules, from the dissolved pool. The most common version of Vitamin B1 auxotrophy is for thiamin's pyrimidine precursor moiety, 4-amino-5-hydroxymethyl-2-methylpyrimidine (HMP). Frequent HMP auxotrophy in plankton and vanishingly low dissolved concentrations (approximately 0.1-50 pM) suggest that high-affinity HMP uptake systems are responsible for maintaining low ambient HMP concentrations. We used tritium-labelled HMP to investigate HMP uptake mechanisms and kinetics in cell cultures of Candidatus Pelagibacter st. HTCC7211, a representative of the globally distributed and highly abundant SAR11 clade. A single protein, the sodium solute symporter ThiV, which is conserved across SAR11 genomes, is the likely candidate for HMP transport. Experimental evidence indicated transport specificity for HMP and mechanistically complex, high-affinity HMP uptake kinetics. Km values ranged from 9.5 pM to 1.2 nM and were dramatically lower when cells were supplied with a carbon source. These results suggest that HMP uptake in HTCC7211 is subject to complex regulation and point to a strategy for high-affinity uptake of this essential growth factor that can explain natural HMP levels in seawater.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search-an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

Discovery of a Thermostable Tagatose 4-Epimerase Powered by Structure- and Sequence-Based Protein Clustering

d-Tagatose is a highly promising functional sweetener known for its various physiological functions. In this study, a novel tagatose 4-epimerase from Thermoprotei archaeon (Thar-T4Ease), with the ability to convert d-fructose to d-tagatose, was discovered through a combination of structure similarity search and sequence-based protein clustering. The recombinant Thar-T4Ease exhibited optimal activity at pH 8.5 and 85 °C, in the presence of 1 mM Ni2+. Its kcat and kcat/Km values toward d-fructose were measured to be 248.5 min-1 and 2.117 mM-1·min-1, respectively. Notably, Thar-T4Ease exhibited remarkable thermostability, with a t1/2 value of 198 h at 80 °C. Moreover, it achieved a conversion ratio of 18.9% using 100 g/L d-fructose as the substrate. Finally, based on sequence and structure analysis, crucial residues for the catalytic activity of Thar-T4Ease were identified by molecular docking and site-directed mutagenesis. This research expands the repertoire of enzymes with C4-epimerization activity and opens up new possibilities for the cost-effective production of d-tagatose from d-fructose.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Asgard archaea defense systems and their roles in the origin of eukaryotic immunity

Dozens of new antiviral systems have been recently characterized in bacteria. Some of these systems are present in eukaryotes and appear to have originated in prokaryotes, but little is known about these defense mechanisms in archaea. Here, we explore the diversity and distribution of defense systems in archaea and identify 2610 complete systems in Asgardarchaeota, a group of archaea related to eukaryotes. The Asgard defense systems comprise 89 unique systems, including argonaute, NLR, Mokosh, viperin, Lassamu, and CBASS. Asgard viperin and argonaute proteins have structural homology to eukaryotic proteins, and phylogenetic analyses suggest that eukaryotic viperin proteins were derived from Asgard viperins. We show that Asgard viperins display anti-phage activity when heterologously expressed in bacteria. Eukaryotic and bacterial argonaute proteins appear to have originated in Asgardarchaeota, and Asgard argonaute proteins have argonaute-PIWI domains, key components of eukaryotic RNA interference systems. Our results support that Asgardarchaeota played important roles in the origin of antiviral defense systems in eukaryotes.

Retron-Eco1 assembles NAD+-hydrolyzing filaments that provide immunity against bacteriophages

Retrons are toxin-antitoxin systems protecting bacteria against bacteriophages via abortive infection. The Retron-Eco1 antitoxin is formed by a reverse transcriptase (RT) and a non-coding RNA (ncRNA)/multi-copy single-stranded DNA (msDNA) hybrid that neutralizes an uncharacterized toxic effector. Yet, the molecular mechanisms underlying phage defense remain unknown. Here, we show that the N-glycosidase effector, which belongs to the STIR superfamily, hydrolyzes NAD+ during infection. Cryoelectron microscopy (cryo-EM) analysis shows that the msDNA stabilizes a filament that cages the effector in a low-activity state in which ADPr, a NAD+ hydrolysis product, is covalently linked to the catalytic E106 residue. Mutations shortening the msDNA induce filament disassembly and the effector's toxicity, underscoring the msDNA role in immunity. Furthermore, we discovered a phage-encoded Retron-Eco1 inhibitor (U56) that binds ADPr, highlighting the intricate interplay between retron systems and phage evolution. Our work outlines the structural basis of Retron-Eco1 defense, uncovering ADPr's pivotal role in immunity.

An In Silico Analysis of Genetic Variants and Structural Modeling of the Human Frataxin Protein in Friedreich's Ataxia

Friedreich's Ataxia (FRDA) stands out as the most prevalent form of hereditary ataxias, marked by progressive movement ataxia, loss of vibratory sensitivity, and skeletal deformities, severely affecting daily functioning. To date, the only medication available for treating FRDA is Omaveloxolone (Skyclarys®), recently approved by the FDA. Missense mutations within the human frataxin (FXN) gene, responsible for intracellular iron homeostasis regulation, are linked to FRDA development. These mutations induce FXN dysfunction, fostering mitochondrial iron accumulation and heightened oxidative stress, ultimately triggering neuronal cell death pathways. This study amalgamated 226 FXN genetic variants from the literature and database searches, with only 18 previously characterized. Predictive analyses revealed a notable prevalence of detrimental and destabilizing predictions for FXN mutations, predominantly impacting conserved residues crucial for protein function. Additionally, an accurate, comprehensive three-dimensional model of human FXN was constructed, serving as the basis for generating genetic variants I154F and W155R. These variants, selected for their severe clinical implications, underwent molecular dynamics (MD) simulations, unveiling flexibility and essential dynamic alterations in their N-terminal segments, encompassing FXN42, FXN56, and FXN78 domains pivotal for protein maturation. Thus, our findings indicate potential interaction profile disturbances in the FXN42, FXN56, and FXN78 domains induced by I154F and W155R mutations, aligning with the existing literature.

Extracellular Kir2.1C122Y Mutant Upsets Kir2.1-PIP2 Bonds and Is Arrhythmogenic in Andersen-Tawil Syndrome

BACKGROUND

Andersen-Tawil syndrome type 1 is a rare heritable disease caused by mutations in the gene coding the strong inwardly rectifying K+ channel Kir2.1. The extracellular Cys (cysteine)122-to-Cys154 disulfide bond in the channel structure is crucial for proper folding but has not been associated with correct channel function at the membrane. We evaluated whether a human mutation at the Cys122-to-Cys154 disulfide bridge leads to Kir2.1 channel dysfunction and arrhythmias by reorganizing the overall Kir2.1 channel structure and destabilizing its open state.

METHODS

We identified a Kir2.1 loss-of-function mutation (c.366 A>T; p.Cys122Tyr) in an ATS1 family. To investigate its pathophysiological implications, we generated an AAV9-mediated cardiac-specific mouse model expressing the Kir2.1C122Y variant. We employed a multidisciplinary approach, integrating patch clamping and intracardiac stimulation, molecular biology techniques, molecular dynamics, and bioluminescence resonance energy transfer experiments.

RESULTS

Kir2.1C122Y mice recapitulated the ECG features of ATS1 independently of sex, including corrected QT prolongation, conduction defects, and increased arrhythmia susceptibility. Isolated Kir2.1C122Y cardiomyocytes showed significantly reduced inwardly rectifier K+ (IK1) and inward Na+ (INa) current densities independently of normal trafficking. Molecular dynamics predicted that the C122Y mutation provoked a conformational change over the 2000-ns simulation, characterized by a greater loss of hydrogen bonds between Kir2.1 and phosphatidylinositol 4,5-bisphosphate than wild type (WT). Therefore, the phosphatidylinositol 4,5-bisphosphate-binding pocket was destabilized, resulting in a lower conductance state compared with WT. Accordingly, on inside-out patch clamping, the C122Y mutation significantly blunted Kir2.1 sensitivity to increasing phosphatidylinositol 4,5-bisphosphate concentrations. In addition, the Kir2.1C122Y mutation resulted in channelosome degradation, demonstrating temporal instability of both Kir2.1 and NaV1.5 proteins.

CONCLUSIONS

The extracellular Cys122-to-Cys154 disulfide bond in the tridimensional Kir2.1 channel structure is essential for the channel function. We demonstrate that breaking disulfide bonds in the extracellular domain disrupts phosphatidylinositol 4,5-bisphosphate-dependent regulation, leading to channel dysfunction and defects in Kir2.1 energetic stability. The mutation also alters functional expression of the NaV1.5 channel and ultimately leads to conduction disturbances and life-threatening arrhythmia characteristic of Andersen-Tawil syndrome type 1.

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.

TM-search: An Efficient and Effective Tool for Protein Structure Database Search

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

Foldseek reveals a CBGA prenylating enzyme GlyMa_02G168000 from Glycine max

The present research provides an application for an aromatic prenyltransferase from Glycine max for use in heterologous microorganism expression to generate cannabinoids. The known cannabinoid prenyltransferase CsPT04 was queried in FoldSeek. An enzyme derived from Glycine max known as GLYMA_02G168000, which is a predicted homogentisate solanyltransferase, was identified and found to have affinity for the prenylation of geranyldiphosphate (GPP) and olivetolic acid (OA) to produce cannabigerolic acid (CBGA) and cannabigerol (CBG). The in vitro production of CBGA was accomplished through the heterologous expression of this prenyltransferase in Saccharomyces cerevisiae. After growing the yeast cells, a purified microsomal fraction was harvested, which was rich in the membrane-bound prenyltransferase GlyMa_02G168000. Addition of purified microsomal fraction to a reaction matrix facilitated the successful prenylation of externally supplied OA with GPP, culminating in the production of CBGA. Structural comparisons revealed a notably closer similarity between GLYMA_02G168000 and CsPT04, compared to the similarity of other cannabinoid prenyltransferases with CsPT04. Herein, a novel application for a homogentisate solanyltransferase has been established towards the production of cannabinoids.

Unraveling the multiplicity of geranylgeranyl reductases in Archaea: potential roles in saturation of terpenoids

The enzymology of the key steps in the archaeal phospholipid biosynthetic pathway has been elucidated in recent years. In contrast, the complete biosynthetic pathways for proposed membrane regulators consisting of polyterpenes, such as carotenoids, respiratory quinones, and polyprenols remain unknown. Notably, the multiplicity of geranylgeranyl reductases (GGRs) in archaeal genomes has been correlated with the saturation of polyterpenes. Although GGRs, which are responsible for saturation of the isoprene chains of phospholipids, have been identified and studied in detail, there is little information regarding the structure and function of the paralogs. Here, we discuss the diversity of archaeal membrane-associated polyterpenes which is correlated with the genomic loci, structural and sequence-based analyses of GGR paralogs.

Binding order of substrate and cofactor in sulfonamide monooxygenase during sulfa drug degradation: in silico studies

For decades, sulfonamide antibiotics have been used across industries such as agriculture and animal husbandry. However, the use and inadvertent misuse of these antibiotics have resulted in the advent of sulfonamide-drug-resistant strains due to antibiotic pollution. Enzymatic bioremediation of antibiotics remains a potential emerging solution to combat antibiotic pollution. Here, we propose an enzymatic model for the degradation of sulfonamides by Microbacterium sp. We have employed a multi-pronged computational strategy involving - protein structure modelling, ligand docking and molecular dynamics simulations to decipher a plausible binding order for the enzymatic degradation of sulfonamides by the bacterial sulfonamide monooxygenase, SulX. Our results enable us to predict that this degradation is achieved through the sequential binding of the antibiotic sulfonamide followed by the reduced flavin cofactor FMNH2, thereby laying the computational foundation for further advancements in enzyme-mediated degradation of the antibiotic. We also provide a list of experiments which may be performed to verify and follow-up on our in-silico studies.

Improved detection and phylogenetic analysis of plant proteins containing LysM domains

Plants perceive N-acetyl-d-glucosamine-containing oligosaccharides that play a role in the interaction with bacteria and fungi, through cell-surface receptors containing a tight bundle of three LysM domains in their extracellular region. However, the identification of LysM domains of receptor-like kinases (RLK)/receptor-like proteins (RLP) using sequence based methods has led to some ambiguity, as some proteins have been annotated with only one or two LysM domains. This missing annotation was likely produced by the failure of the LysM hidden Markov model (HMM) from the Pfam database to correctly identify some LysM domains in proteins of plant origin. In this work, we provide improved HMMs for LysM domain detection in plants, that were built from the structural alignment of manually curated LysM domain structures from the Protein Data Bank and AlphaFold Protein Structure Database. Furthermore, we evaluated different sets of ligand-specific HMMs that were able to correctly classify a limited set of fully characterised RLK/Ps by their ligand specificity. In contrast, the phylogenetic analysis of the extracellular region of RLK/Ps, or of their individual LysM domains, was unable to discriminate these proteins by their ligand specificity. The HMMs reported here will allow a more sensitive detection of plant proteins containing LysM domains and help improve their characterisation.

PC_ali: a tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score

MOTIVATION

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships.

RESULTS

Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues, and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main principal component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. The program constructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieves higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim, and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ugobas/PC_ali.

Specific Nucleotides in the Common Region of the Begomovirus Tomato Rugose Mosaic Virus (ToRMV) Are Responsible for the Negative Interference over Tomato Severe Rugose Virus (ToSRV) in Mixed Infection

Mixed infection between two or more begomoviruses is commonly found in tomato fields and can affect disease outcomes by increasing symptom severity and viral accumulation compared with single infection. Viruses that affect tomato include tomato severe rugose virus (ToSRV) and tomato rugose mosaic virus (ToRMV). Previous work showed that in mixed infection, ToRMV negatively affects the infectivity and accumulation of ToSRV. ToSRV and ToRMV share a high degree of sequence identity, including cis-elements in the common region (CR) and their specific recognition sites (iteron-related domain, IRD) within the Rep gene. Here, we investigated if divergent sites in the CR and IRD are involved in the interaction between these two begomoviruses. ToSRV clones were constructed containing the same nucleotides as ToRMV in the CR (ToSRV-A(ToR:CR)), IRD (ToSRV-A(ToR:IRD)) and in both regions (ToSRV-A(ToR:CR+IRD)). When plants were co-inoculated with ToRMV and ToSRV-A(ToR:IRD), the infectivity and accumulation of ToSRV were negatively affected. In mixed inoculation of ToRMV with ToSRV-A(ToR:CR), high infectivity of both viruses and high DNA accumulation of ToSRV-A(ToR:CR) were observed. A decrease in viral accumulation was observed in plants inoculated with ToSRV-A(ToR:CR+IRD). These results indicate that differences in the CR, but not the IRD, are responsible for the negative interference of ToRMV on ToSRV.

The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin-antitoxin and related phage defense systems

Toxin-antitoxin (TA) systems are a large group of small genetic modules found in prokaryotes and their mobile genetic elements. Type II TAs are encoded as bicistronic (two-gene) operons that encode two proteins: a toxin and a neutralizing antitoxin. Using our tool NetFlax (standing for Network-FlaGs for toxins and antitoxins), we have performed a large-scale bioinformatic analysis of proteinaceous TAs, revealing interconnected clusters constituting a core network of TA-like gene pairs. To understand the structural basis of toxin neutralization by antitoxins, we have predicted the structures of 3,419 complexes with AlphaFold2. Together with mutagenesis and functional assays, our structural predictions provide insights into the neutralizing mechanism of the hyperpromiscuous Panacea antitoxin domain. In antitoxins composed of standalone Panacea, the domain mediates direct toxin neutralization, while in multidomain antitoxins the neutralization is mediated by other domains, such as PAD1, Phd-C, and ZFD. We hypothesize that Panacea acts as a sensor that regulates TA activation. We have experimentally validated 16 NetFlax TA systems and used domain annotations and metabolic labeling assays to predict their potential mechanisms of toxicity (such as membrane disruption, and inhibition of cell division or protein synthesis) as well as biological functions (such as antiphage defense). We have validated the antiphage activity of a RosmerTA system encoded by Gordonia phage Kita, and used fluorescence microscopy to confirm its predicted membrane-depolarizing activity. The interactive version of the NetFlax TA network that includes structural predictions can be accessed at http://netflax.webflags.se/.

γ-Linolenic acid in maternal milk drives cardiac metabolic maturation

Birth presents a metabolic challenge to cardiomyocytes as they reshape fuel preference from glucose to fatty acids for postnatal energy production1,2. This adaptation is triggered in part by post-partum environmental changes3, but the molecules orchestrating cardiomyocyte maturation remain unknown. Here we show that this transition is coordinated by maternally supplied γ-linolenic acid (GLA), an 18:3 omega-6 fatty acid enriched in the maternal milk. GLA binds and activates retinoid X receptors4 (RXRs), ligand-regulated transcription factors that are expressed in cardiomyocytes from embryonic stages. Multifaceted genome-wide analysis revealed that the lack of RXR in embryonic cardiomyocytes caused an aberrant chromatin landscape that prevented the induction of an RXR-dependent gene expression signature controlling mitochondrial fatty acid homeostasis. The ensuing defective metabolic transition featured blunted mitochondrial lipid-derived energy production and enhanced glucose consumption, leading to perinatal cardiac dysfunction and death. Finally, GLA supplementation induced RXR-dependent expression of the mitochondrial fatty acid homeostasis signature in cardiomyocytes, both in vitro and in vivo. Thus, our study identifies the GLA-RXR axis as a key transcriptional regulatory mechanism underlying the maternal control of perinatal cardiac metabolism.

Trehalose transporter-like gene diversity and dynamics enhances stress response and recovery in Helicoverpa armigera

Trehalose is a primary sugar and its distribution across the insect body, regulated by trehalose transporters (TRETs), is essential for sugar metabolism and energy homeostasis. The large diversity of Tret-like sugar transporters (ST), belonging to SLC2A transporter family, in polyphagous insects probably contributes to their extremely adaptive nature. We aim to study spatio-temporal expression dynamics and functional relevance of ST transcript variants in the lepidopteran model organism, Helicoverpa armigera. Identification of 69 putative Tret-like HaST transcript variants from databases and their digital gene expression analysis indicated tissue and development-specific expression patterns. Phylogenetic and sequence similarity network analysis of HaSTs signify evolutionary divergence, while motif and structure analysis depicted conserved signatures. In vitro gene expression validation for selected genes depicts that HaST09 and 69 are fat body and haemolymph-specific. While, HaST06, 30, 36 and 57 are developmental stage or sex-specific. HaST69 has high expression in the haemolymph of fifth instar larvae. In the presence of trehalose metabolism inhibitors and abiotic stress, HaSTs expression show dysregulation, indicating their possible association with trehalose metabolism and stress recovery. In vivo gene silencing of HaST69 resulted in reduced trehalose accumulation in the insect body, suggesting its plausible role in sugar metabolism. The overall understanding of HaST diversity and expression dynamics highlights their putative roles in sugar transport during adaptation and stress recovery of insects.

The crystal structure of a simian Foamy Virus receptor binding domain provides clues about entry into host cells

The surface envelope glycoprotein (Env) of all retroviruses mediates virus binding to cells and fusion of the viral and cellular membranes. A structure-function relationship for the HIV Env that belongs to the Orthoretrovirus subfamily has been well established. Structural information is however largely missing for the Env of Foamy viruses (FVs), the second retroviral subfamily. In this work we present the X-ray structure of the receptor binding domain (RBD) of a simian FV Env at 2.57 Å resolution, revealing two subdomains and an unprecedented fold. We have generated a model for the organization of the RBDs within the trimeric Env, which indicates that the upper subdomains form a cage-like structure at the apex of the Env, and identified residues K342, R343, R359 and R369 in the lower subdomain as key players for the interaction of the RBD and viral particles with heparan sulfate.

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.

Analysis of KRAS-Ligand Interaction Modes and Flexibilities Reveals the Binding Characteristics

KRAS, a common human oncogene, has been recognized as a critical drug target in treating multiple cancers. After four decades of effort, one allosteric KRAS drug (Sotorasib) has been approved, inspiring more KRAS-targeted drug research. Here, we provide the features of KRAS binding pockets and ligand-binding characteristics of KRAS complexes using a structural systems pharmacology approach. Three distinct binding sites (conserved nucleotide-binding site, shallow Switch-I/II pocket, and allosteric Switch-II/α3 pocket) are characterized. Ligand-binding features are determined based on encoded KRAS-inhibitor interaction fingerprints. Finally, the flexibility of the three distinct binding sites to accommodate different potential ligands, based on MD simulation, is discussed. Collectively, these findings are intended to facilitate rational KRAS drug design.

Prophage-Derived Regions in Curtobacterium Genomes: Good Things, Small Packages

Curtobacterium is a genus of Gram-positive bacteria within the order Actinomycetales. Some Curtobacterium species (C. flaccumfaciens, C. plantarum) are harmful pathogens of agricultural crops such as soybean, dry beans, peas, sugar beet and beetroot, which occur throughout the world. Bacteriophages (bacterial viruses) are considered to be potential curative agents to control the spread of harmful bacteria. Temperate bacteriophages integrate their genomes into bacterial chromosomes (prophages), sometimes substantially influencing bacterial lifestyle and pathogenicity. About 200 publicly available genomes of Curtobacterium species, including environmental metagenomic sequences, were inspected for the presence of sequences of possible prophage origin using bioinformatic methods. The comparison of the search results with several ubiquitous bacterial groups showed the relatively low level of the presence of prophage traces in Curtobacterium genomes. Genomic and phylogenetic analyses were undertaken for the evaluation of the evolutionary and taxonomic positioning of predicted prophages. The analyses indicated the relatedness of Curtobacterium prophage-derived sequences with temperate actinophages of siphoviral morphology. In most cases, the predicted prophages can represent novel phage taxa not described previously. One of the predicted temperate phages was induced from the Curtobacterium genome. Bioinformatic analysis of the modelled proteins encoded in prophage-derived regions led to the discovery of some 100 putative glycopolymer-degrading enzymes that contained enzymatic domains with predicted cell-wall- and cell-envelope-degrading activity; these included glycosidases and peptidases. These proteins can be considered for the experimental design of new antibacterials against Curtobacterium phytopathogens.

Use of an Integrated Approach Involving AlphaFold Predictions for the Evolutionary Taxonomy of Duplodnaviria Viruses

The evaluation of the evolutionary relationships is exceptionally important for the taxonomy of viruses, which is a rapidly expanding area of research. The classification of viral groups belonging to the realm Duplodnaviria, which include tailed bacteriophages, head-tailed archaeal viruses and herpesviruses, has undergone many changes in recent years and continues to improve. One of the challenging tasks of Duplodnaviria taxonomy is the classification of high-ranked taxa, including families and orders. At the moment, only 17 of 50 families have been assigned to orders. The evaluation of the evolutionary relationships between viruses is complicated by the high level of divergence of viral proteins. However, the development of structure prediction algorithms, including the award-winning AlphaFold, encourages the use of the results of structural predictions to clarify the evolutionary history of viral proteins. In this study, the evolutionary relationships of two conserved viral proteins, the major capsid protein and terminase, representing different viruses, including all classified Duplodnaviria families, have been analysed using AlphaFold modelling. This analysis has been undertaken using structural comparisons and different phylogenetic methods. The results of the analyses mainly indicated the high quality of AlphaFold modelling and the possibility of using the AlphaFold predictions, together with other methods, for the reconstruction of the evolutionary relationships between distant viral groups. Based on the results of this integrated approach, assumptions have been made about refining the taxonomic classification of bacterial and archaeal Duplodnaviria groups, and problems relating to the taxonomic classification of Duplodnaviria have been discussed.

Pan-kinome of Legionella expanded by a bioinformatics survey

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases discovered recently, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella, available at http://bioinfo.sggw.edu.pl/kintaro/ . We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature's ingeniousness in the pathogen-host arms race and offers a useful resource for the study of infection mechanisms.

Human Gut Metagenomes Encode Diverse GH156 Sialidases

The intestinal lining is protected by a mucous barrier composed predominantly of complex carbohydrates. Gut microbes employ diverse glycoside hydrolases (GHs) to liberate mucosal sugars as a nutrient source to facilitate host colonization. Intensive catabolism of mucosal glycans, however, may contribute to barrier erosion, pathogen encroachment, and inflammation. Sialic acid is an acidic sugar featured at terminal positions of host glycans. Characterized sialidases from the microbiome belong to the GH33 family, according to CAZy (Carbohydrate-Active enZYmes Database). In 2018 a functional metagenomics screen using thermal spring DNA uncovered the founding member of the GH156 sialidase family, the presence of which has yet to be reported in the context of the human microbiome. A subset of GH156 sequences from the CAZy database containing key sialidase residues was used to build a hidden Markov model. HMMsearch against public databases revealed ~10× more putative GH156 sialidases than currently cataloged by CAZy. Represented phyla include Bacteroidota, Verrucomicrobiota, and Firmicutes_A from human microbiomes, all of which play notable roles in carbohydrate fermentation. Analyses of metagenomic data sets revealed that GH156s are frequently encoded in metagenomes, with a greater variety and abundance of GH156 genes observed in traditional hunter-gatherer or agriculturalist societies than in industrialized societies, particularly relative to individuals with inflammatory bowel disease (IBD). Nineteen GH156s were recombinantly expressed and assayed for sialidase activity. The five GH156 sialidases identified here share limited sequence identity to each other or the founding GH156 family member and are representative of a large subset of the family. IMPORTANCE Sialic acids occupy terminal positions of human glycans where they act as receptors for microbes, toxins, and immune signaling molecules. Microbial enzymes that remove sialic acids, sialidases, are abundant in the human microbiome where they may contribute to shaping the microbiota community structure or contribute to pathology. Furthermore, sialidases have proven to hold therapeutic potential for cancer therapy. Here, we examined the sequence space of a sialidase family of enzymes, GH156, previously unknown in the human gut environment. Our analyses suggest that human populations with disparate dietary practices harbor distinct varieties and abundances of GH156-encoding genes. Furthermore, we demonstrate the sialidase activity of 5 gut-derived GH156s. These results expand the diversity of sialidases that may contribute to host glycan degradation, and these sequences may have biotechnological or clinical utility.

Coronaviral PLpro proteases and the immunomodulatory roles of conjugated versus free Interferon Stimulated Gene product-15 (ISG15)

Ubiquitin-like proteins (Ubls) share some features with ubiquitin (Ub) such as their globular 3D structure and the ability to attach covalently to other proteins. Interferon Stimulated Gene 15 (ISG15) is an abundant Ubl that similar to Ub, marks many hundreds of cellular proteins, altering their fate. In contrast to Ub, , ISG15 requires interferon (IFN) induction to conjugate efficiently to other proteins. Moreover, despite the multitude of E3 ligases for Ub-modified targets, a single E3 ligase termed HERC5 (in humans) is responsible for the bulk of ISG15 conjugation. Targets include both viral and cellular proteins spanning an array of cellular compartments and metabolic pathways. So far, no common structural or biochemical feature has been attributed to these diverse substrates, raising questions about how and why they are selected. Conjugation of ISG15 mitigates some viral and bacterial infections and is linked to a lower viral load pointing to the role of ISG15 in the cellular immune response. In an apparent attempt to evade the immune response, some viruses try to interfere with the ISG15 pathway. For example, deconjugation of ISG15 appears to be an approach taken by coronaviruses to interfere with ISG15 conjugates. Specifically, coronaviruses such as SARS-CoV, MERS-CoV, and SARS-CoV-2, encode papain-like proteases (PL1pro) that bear striking structural and catalytic similarities to the catalytic core domain of eukaryotic deubiquitinating enzymes of the Ubiquitin-Specific Protease (USP) sub-family. The cleavage specificity of these PLpro enzymes is for flexible polypeptides containing a consensus sequence (R/K)LXGG, enabling them to function on two seemingly unrelated categories of substrates: (i) the viral polyprotein 1 (PP1a, PP1ab) and (ii) Ub- or ISG15-conjugates. As a result, PLpro enzymes process the viral polyprotein 1 into an array of functional proteins for viral replication (termed non-structural proteins; NSPs), and it can remove Ub or ISG15 units from conjugates. However, by de-conjugating ISG15, the virus also creates free ISG15, which in turn may affect the immune response in two opposite pathways: free ISG15 negatively regulates IFN signaling in humans by binding non-catalytically to USP18, yet at the same time free ISG15 can be secreted from the cell and induce the IFN pathway of the neighboring cells. A deeper understanding of this protein-modification pathway and the mechanisms of the enzymes that counteract it will bring about effective clinical strategies related to viral and bacterial infections.

Associating Biological Activity and Predicted Structure of Antimicrobial Peptides from Amphibians and Insects

Antimicrobial peptides (AMPs) are a diverse class of short, often cationic biological molecules that present promising opportunities in the development of new therapeutics to combat antimicrobial resistance. Newly developed in silico methods offer the ability to rapidly discover numerous novel AMPs with a variety of physiochemical properties. Herein, using the rAMPage AMP discovery pipeline, we bioinformatically identified 51 AMP candidates from amphibia and insect RNA-seq data and present their in-depth characterization. The studied AMPs demonstrate activity against a panel of bacterial pathogens and have undetected or low toxicity to red blood cells and human cultured cells. Amino acid sequence analysis revealed that 30 of these bioactive peptides belong to either the Brevinin-1, Brevinin-2, Nigrocin-2, or Apidaecin AMP families. Prediction of three-dimensional structures using ColabFold indicated an association between peptides predicted to adopt a helical structure and broad-spectrum antibacterial activity against the Gram-negative and Gram-positive species tested in our panel. These findings highlight the utility of associating the diverse sequences of novel AMPs with their estimated peptide structures in categorizing AMPs and predicting their antimicrobial activity.

Highly significant improvement of protein sequence alignments with AlphaFold2

MOTIVATION

Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis.

RESULTS

Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis.

AVAILABILITY AND IMPLEMENTATION

All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation

Ideonella sakaiensis PET hydrolase (IsPETase) is a well-characterized enzyme for effective PET biodegradation. However, the low soluble expression level of the enzyme hampers its practical implementation in the biodegradation of PET. Herein, the expression of IsPETase^Mut, one of the most active mutants of IsPETase obtained so far, was systematically explored in E. coli by adopting a series of strategies. A notable improvement of soluble IsPETase^Mut was observed by using chaperon co-expression and fusion expression systems. Under the optimized conditions, GroEL/ES co-expression system yielded 75 ± 3.4 mg·L^-1 purified soluble IsPETase^Mut (GroEL/ES), and NusA fusion expression system yielded 80 ± 3.7 mg·L^-1 purified soluble NusA-IsPETase^Mut, which are 12.5- and 4.6-fold, respectively, higher than its commonly expression in E. coli. The two purified enzymes were further characterized. The results showed that IsPETase^Mut (GroEL/ES) displayed the same catalytic behavior as IsPETase^Mut, while the fusion of NusA conferred new enzymatic properties to IsPETase^Mut. Although NusA-IsPETase^Mut displayed a lower initial hydrolysis capacity than IsPETase^Mut, it showed a 1.4-fold higher adsorption constant toward PET. Moreover, the product inhibition effect of terephthalic acid (TPA) on IsPETase was reduced with NusA-IsPETase^Mut. Taken together, the latter two catalytic properties of NusA-IsPETase^Mut are more likely to contribute to the enhanced product release by NusA-IsPETase^Mut PET degradation for two weeks.

US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes

Structure comparison and alignment are of fundamental importance in structural biology studies. We developed the first universal platform, US-align, to uniformly align monomer and complex structures of different macromolecules-proteins, RNAs and DNAs. The pipeline is built on a uniform TM-score objective function coupled with a heuristic alignment searching algorithm. Large-scale benchmarks demonstrated consistent advantages of US-align over state-of-the-art methods in pairwise and multiple structure alignments of different molecules. Detailed analyses showed that the main advantage of US-align lies in the extensive optimization of the unified objective function powered by efficient heuristic search iterations, which substantially improve the accuracy and speed of the structural alignment process. Meanwhile, the universal protocol fusing different molecular and structural types helps facilitate the heterogeneous oligomer structure comparison and template-based protein-protein and protein-RNA/DNA docking.

Escherichia coli transcription factors of unknown function: sequence features and possible evolutionary relationships

Organisms need mechanisms to perceive the environment and respond accordingly to environmental changes or the presence of hazards. Transcription factors (TFs) are required for cells to respond to the environment by controlling the expression of genes needed. Escherichia coli has been the model bacterium for many decades, and still, there are features embedded in its genome that remain unstudied. To date, 58 TFs remain poorly characterized, although their binding sites have been experimentally determined. This study showed that these TFs have sequence variation at the third codon position G+C content but maintain the same Codon Adaptation Index (CAI) trend as annotated functional transcription factors. Most of these transcription factors are in areas of the genome where abundant repetitive and mobile elements are present. Sequence divergence points to groups with distinctive sequence signatures but maintaining the same type of DNA binding domain. Finally, the analysis of the promoter sequences of the 58 TFs showed A+T rich regions that agree with the features of horizontally transferred genes. The findings reported here pave the way for future research of these TFs that may uncover their role as spare factors in case of lose-of-function mutations in core TFs and trace back their evolutionary history.

A new paradigm of multiheme cytochrome evolution by grafting and pruning protein modules

Multiheme cytochromes play key roles in diverse biogeochemical cycles, but understanding the origin and evolution of these proteins is a challenge due to their ancient origin and complex structure. Up until now, the evolution of multiheme cytochromes composed by multiple redox modules in a single polypeptide chain was proposed to occur by gene fusion events. In this context, the pentaheme nitrite reductase NrfA and the tetraheme cytochrome c₅₅₄ were previously proposed to be at the origin of the extant octa- and nonaheme cytochrome c involved in metabolic pathways that contribute to the nitrogen, sulfur, and iron biogeochemical cycles by a gene fusion event. Here, we combine structural and character-based phylogenetic analysis with an unbiased root placement method to refine the evolutionary relationships between these multiheme cytochromes. The evidence show that NrfA and cytochrome c₅₅₄ belong to different clades, which suggests that these two multiheme cytochromes are products of truncation of ancestral octaheme cytochromes related to extant octaheme nitrite reductase and MccA, respectively. From our phylogenetic analysis, the last common ancestor is predicted to be an octaheme cytochrome with nitrite reduction ability. Evolution from this octaheme framework led to the great diversity of extant multiheme cytochromes analyzed here by pruning and grafting of protein modules and hemes. By shedding light into the evolution of multiheme cytochromes that intervene in different biogeochemical cycles, this work contributes to our understanding about the interplay between biology and geochemistry across large time scales in the history of Earth.