HH-suite3 for fast remote homology detection and deep protein annotation

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

A lineage-specific protein network at the trypanosome nuclear envelope

The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization and nucleocytoplasmic transport. The molecular basis for many of these functions have diverged between eukaryotic lineages. Trypanosoma brucei, a member of the early branching eukaryotic lineage Discoba, highlights many of these, including a distinct lamina and kinetochore composition. Here, we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with S. cerevisiae and H. sapiens, and additional peripheral components of the NPC. While many of the LAPs are Kinetoplastid specific, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the trypanosome NE proteome, highlighting the diversity of nuclear biology across the eukaryotes, increasing our understanding of eukaryotic and NPC evolution.

DepoScope: Accurate phage depolymerase annotation and domain delineation using large language models

Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.

The parasitic lifestyle of an archaeal symbiont

DPANN archaea are a diverse group of microorganisms characterised by small cells and reduced genomes. To date, all cultivated DPANN archaea are ectosymbionts that require direct cell contact with an archaeal host species for growth and survival. However, these interactions and their impact on the host species are poorly understood. Here, we show that a DPANN archaeon (Candidatus Nanohaloarchaeum antarcticus) engages in parasitic interactions with its host (Halorubrum lacusprofundi) that result in host cell lysis. During these interactions, the nanohaloarchaeon appears to enter, or be engulfed by, the host cell. Our results provide experimental evidence for a predatory-like lifestyle of an archaeon, suggesting that at least some DPANN archaea may have roles in controlling host populations and their ecology.

Exploring the Structural Insights of Thermostable Geobacillus esterases by Computational Characterization

This study conducted an in silico analysis of two biochemically characterized thermostable esterases, Est2 and Est3, from Geobacillus strains. To achieve this, the amino acid sequences of Est2 and Est3 were examined to assess their biophysicochemical properties, evolutionary connections, and sequence similarities. Three-dimensional models were constructed and validated through diverse bioinformatics tools. Molecular dynamics (MD) simulation was employed on a pNP-C2 ligand to explore interactions between enzymes and ligand. Biophysicochemical property analysis indicated that aliphatic indices and theoretical T m values of enzymes were between 82-83 and 55-65 °C, respectively. Molecular phylogeny placed Est2 and Est3 within Family XIII, alongside other Geobacillus esterases. DeepMSA2 revealed that Est2, Est3, and homologous sequences shared 12 conserved residues in their core domain (L39, D50, G53, G55, S57, G92, S94, G96, P108, P184, D193, and H223). BANΔIT analysis indicated that Est2 and Est3 had a significantly more rigid cap domain compared to Est30. Salt bridge analysis revealed that E150-R136, E124-K165, E137-R141, and E154-K157 salt bridges made Est2 and Est3 more stable compared to Est30. MD simulation indicated that Est3 exhibited greater fluctuations in the N-terminal region including conserved F25, cap domain, and C-terminal region, notably including H223, suggesting that these regions might influence esterase catalysis. The common residues in the ligand-binding sites of Est2-Est3 were determined as F25 and L167. The analysis of root mean square fluctuation (RMSF) revealed that region 1, encompassing F25 within the β2-α1 loop of Est3, exhibited higher fluctuations compared to those of Est2. Overall, this study might provide valuable insights for future investigations aimed at improving esterase thermostability and catalytic efficiency, critical industrial traits, through targeted amino acid modifications within the N-terminal region, cap domain, and C-terminal region using rational protein engineering techniques.

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

BACKGROUND

Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER.

RESULTS

The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a ∼  $3000 Xilinx Alveo U50 FPGA accelerator card, ∼  227× faster than the optimized SSV implementation in nhmmer. Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer's SSV with one thread and 35× faster than nhmmer with four threads, and uses ∼  31% the energy of a traditional high end Intel CPU.

CONCLUSIONS

HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

Exploring SDHI fungicide resistance in Botrytis cinerea through genetic transformation system and AlphaFold model-based molecular docking

BACKGROUND

Gray mold caused by Botrytis cinerea is one of the most serious diseases affecting strawberry. Succinate dehydrogenase inhibitor (SDHI) fungicides have been used for more than a decade to control the disease. Monitoring resistance and improving in-depth understanding of resistance mechanisms are essential for the control of B. cinerea.

RESULTS

In this study, resistance monitoring of a SDHI fungicide boscalid was conducted on B. cinerea isolated from strawberries in Korea during 2020 and 2021, with resistance rates of 76.92% and 72.25%, respectively. In resistant strains, mutations P225F/H and H272R were found in SdhB, with P225F representing the dominant mutation type. Simultaneous mutations G85A, I93V, M158V, and V168I in SdhC were detected in 54.84% of sensitive strains. Sensitivity profiles of different Sdh genotypes of B. cinerea strains to six SDHIs were determined in vitro and in vivo. In addition, the mutation(s) were genetically validated through in situ SdhB (SdhC) expression. Docking assays between SDHIs and AlphaFold model-based Sdh complexes revealed generally consistent patterns with their in vitro phenotypes.

CONCLUSION

Resistance of B. cinerea to SDHI fungicide on strawberry was systematically investigated in this study. Deciphering of SDHI resistance through the genetic transformation system and AlphaFold model-based molecular docking will provide valuable insights into other target site-based fungicide resistance in fungal pathogens. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

"Tear down that wall"-a critical evaluation of bioinformatic resources available for lysin researchers

Lytic enzymes, or lysins for short, break down peptidoglycan and interrupt the continuity of the cell wall, which, in turn, causes osmotic lysis of the bacterium. Their ability to destroy bacteria from within makes them promising antimicrobial agents that can be used as alternatives or supplements to antibiotics. In this paper, we briefly summarize basic terms and concepts used to describe lysin sequences and delineate major lysin groups. More importantly, we describe the domain repertoire found in lysins and critically review bioinformatic tools or databases which are used in studies of these enzymes (with particular emphasis on the repositories of Hidden Markov models). Finally, we present a novel comprehensive, meticulously curated set of lysin-related family and domain models, sort them into clusters that reflect major families, and demonstrate that the selected models can be used to efficiently search for new lysins.

African Swine Fever Virus Protein-Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

MOTIVATION

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.

RESULTS

We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.

AVAILABILITY AND IMPLEMENTATION

Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.

SUPPLEMENTARY INFORMATION

Supplementary Information is available at Bioinformatics online.

Mushroom Tyrosinase: Six Isoenzymes Catalyzing Distinct Reactions

"Mushroom tyrosinase" from the common button mushroom is the most frequently used source of tyrosinase activity, both for basic and applied research. Here, the complete tyrosinase family from Agaricus bisporus var. bisporus (abPPO1-6) was cloned from mRNA and expressed heterologously using a single protocol. All six isoenzymes accept a wide range of phenolic and catecholic substrates, but display pronounced differences in their specificity and enzymatic reaction rate. AbPPO3 ignores γ-l-glutaminyl-4-hydroxybenzene (GHB), a natural phenol present in mM concentrations in A. bisporus, while AbPPO4 processes 100 μM GHB at 4-times the rate of the catechol l-DOPA. All six AbPPOs are biochemically distinct enzymes fit for different roles in the fungal life cycle, which challenges the traditional concept of isoenzymes as catalyzing the same physiological reaction and varying only in secondary properties. Transferring this approach to other enzymes and organisms will greatly stimulate both the study of the in vivo function(s) of enzymes and the application of these highly efficient catalysts.

Genomic analysis of hyperparasitic viruses associated with entomopoxviruses

Polinton-like viruses (PLVs) are a diverse group of small integrative dsDNA viruses that infect diverse eukaryotic hosts. Many PLVs are hypothesized to parasitize viruses in the phylum Nucleocytoviricota for their own propagation and spread. Here, we analyze the genomes of novel PLVs associated with the occlusion bodies of entomopoxvirus (EPV) infections of two separate lepidopteran hosts. The presence of these elements within EPV occlusion bodies suggests that they are the first known hyperparasites of poxviruses. We find that these PLVs belong to two distinct lineages that are highly diverged from known PLVs. These PLVs possess mosaic genomes, and some essential genes share homology with mobile genes within EPVs. Based on this homology and observed PLV mosaicism, we propose a mechanism to explain the turnover of PLV replication and integration genes.

Actomyosin organelle functions of SPIRE actin nucleators precede animal evolution

An important question in cell biology is how cytoskeletal proteins evolved and drove the development of novel structures and functions. Here we address the origin of SPIRE actin nucleators. Mammalian SPIREs work with RAB GTPases, formin (FMN)-subgroup actin assembly proteins and class-5 myosin (MYO5) motors to transport organelles along actin filaments towards the cell membrane. However, the origin and extent of functional conservation of SPIRE among species is unknown. Our sequence searches show that SPIRE exist throughout holozoans (animals and their closest single-celled relatives), but not other eukaryotes. SPIRE from unicellular holozoans (choanoflagellate), interacts with RAB, FMN and MYO5 proteins, nucleates actin filaments and complements mammalian SPIRE function in organelle transport. Meanwhile SPIRE and MYO5 proteins colocalise to organelles in Salpingoeca rosetta choanoflagellates. Based on these observations we propose that SPIRE originated in unicellular ancestors of animals providing an actin-myosin driven exocytic transport mechanism that may have contributed to the evolution of complex multicellular animals.

Defects in Glabrous 3 (GL3) functionality underlie the absence of trichomes in Brassica napus

Previously, expression of the Arabidopsis thaliana GLABRA3 (GL3) induced trichome formation in Brassica napus. GL3 orthologues were examined from glabrous (B. oleracea), semi-glabrous (B. napus), moderately hirsute (B. rapa), and very hirsute (B. villosa) Brassica species. Ectopic expression of BnGL3, BrGL3 alleles, or BvGL3 induced trichome formation in glabrous B. napus with the effect on trichome number commensurate with density in the original accessions. Chimeric GL3 proteins in which the B. napus amino terminal region, which interacts with MYB proteins, or the middle region, which interacts with the WD40 protein TTG1, was exchanged with corresponding regions from A. thaliana were as stimulatory to trichome production as AtGL3. Exchange of the carboxy-terminal region containing a bHLH domain and an ACT domain did not alter the trichome stimulatory activity, although modeling of the ACT domain identified differences that could affect GL3 dimerization. B. napus A- and C-genomes orthologues differed in their abilities to form homo- and heterodimers. Modeling of the amino-terminal region revealed a conserved domain that may represent the MYB factor binding pocket. This region interacted with the MYB factors GL1, CPC, and TRY, as well as with JAZ8, which is involved in jasmonic acid-mediated regulation of MYC-like transcription factors. Protein interaction studies indicated that GL1 interaction with GL3 from B. napus and A. thaliana may underlie the difference in their respective abilities to induce trichome formation.

Viperin immunity evolved across the tree of life through serial innovations on a conserved scaffold

Evolutionary arms races between cells and viruses drive the rapid diversification of antiviral genes in diverse life forms. Recent discoveries have revealed the existence of immune genes that are shared between prokaryotes and eukaryotes and show molecular and mechanistic similarities in their response to viruses. However, the evolutionary dynamics underlying the conservation and adaptation of these antiviral genes remain mostly unexplored. Here, we show that viperins constitute a highly conserved family of immune genes across diverse prokaryotes and eukaryotes and identify mechanisms by which they diversified in eukaryotes. Our findings indicate that viperins are enriched in Asgard archaea and widely distributed in all major eukaryotic clades, suggesting their presence in the last eukaryotic common ancestor and their acquisition in eukaryotes from an archaeal lineage. We show that viperins maintain their immune function by producing antiviral nucleotide analogues and demonstrate that eukaryotic viperins diversified through serial innovations on the viperin gene, such as the emergence and selection of substrate specificity towards pyrimidine nucleotides, and through partnerships with genes maintained through genetic linkage, notably with nucleotide kinases. These findings unveil biochemical and genomic transitions underlying the adaptation of immune genes shared by prokaryotes and eukaryotes. Our study paves the way for further understanding of the conservation of immunity across domains of life.

PhyloAln: A Convenient Reference-Based Tool to Align Sequences and High-Throughput Reads for Phylogeny and Evolution in the Omic Era

The current trend in phylogenetic and evolutionary analyses predominantly relies on omic data. However, prior to core analyses, traditional methods typically involve intricate and time-consuming procedures, including assembly from high-throughput reads, decontamination, gene prediction, homology search, orthology assignment, multiple sequence alignment, and matrix trimming. Such processes significantly impede the efficiency of research when dealing with extensive data sets. In this study, we develop PhyloAln, a convenient reference-based tool capable of directly aligning high-throughput reads or complete sequences with existing alignments as a reference for phylogenetic and evolutionary analyses. Through testing with simulated data sets of species spanning the tree of life, PhyloAln demonstrates consistently robust performance compared with other reference-based tools across different data types, sequencing technologies, coverages, and species, with percent completeness and identity at least 50 percentage points higher in the alignments. Additionally, we validate the efficacy of PhyloAln in removing a minimum of 90% foreign and 70% cross-contamination issues, which are prevalent in sequencing data but often overlooked by other tools. Moreover, we showcase the broad applicability of PhyloAln by generating alignments (completeness mostly larger than 80%, identity larger than 90%) and reconstructing robust phylogenies using real data sets of transcriptomes of ladybird beetles, plastid genes of peppers, or ultraconserved elements of turtles. With these advantages, PhyloAln is expected to facilitate phylogenetic and evolutionary analyses in the omic era. The tool is accessible at https://github.com/huangyh45/PhyloAln.

Improved prediction of site-rates from structure with averaging across homologs

Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site-specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site-specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site-specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site-specific rates across many extant protein structures can lead to correct recovery of site-rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site-specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site-specific rates in protein families.

Unraveling the potential of uninvestigated thermoalkaliphilic lipases by molecular docking and molecular dynamic simulation: an in silico characterization study

Thermoalkaliphilic lipase enzymes are mostly favored for use in the detergent industry. While there has been considerable research on Geobacillus lipases, a significant portion of these enzymes remains unexplored or undocumented in the scientific literature. This work performed in silico phylogeny, sequence alignment, structural and enzyme-substrate interaction analyses of the five thermoalkaliphilic lipases belonging to different Geobacillus species (Geobacillus stearothermophilus lipase = GsLip, Geobacillus sp. B4113_201601 lipase = Gb4Lip, Geobacillus kaustophilus HTA426 lipase = GkLip, Geobacillus sp. SP22 lipase = GspLip, Geobacillus sp. NTU 03 lipase = GntLip). For this purpose, unreviewed enzyme sequences of five Geobacillus thermoalkaliphilic lipases were analyzed at sequence and phylogeny levels. 3D homology enzyme models were built, validated, and investigated by different bioinformatics tools. The ligand interactions screening using seven para-nitrophenyl (pNP) esters and enzyme-ligand interactions were analyzed on Gb4Lip:pNP-C12 and BTL2:pNP-C12 by MD simulation. Biophysicochemical characteristic analysis showed that Gb4Lip had a theoretical T m value of above 65 ºC, and a higher aliphatic index indicating greater thermal stability. Sequence alignment showed a hydrophilic threonine in the α6 helix of Gb4Lip, indicating high enzymatic activity. A normalized temperature factor B (B'-factor) analysis showed that the lid domains of five lipases significantly possessed lower B'-factor values, compared to G. thermocatenulatus lipase 2 (BTL2), indicating that they had higher rigidity. Molecular docking results indicated that the five lipases had the highest binding affinity toward pNP-C12. The RMSF investigation revealed that the thermostability of Gb4Lip is influenced by specific molecular elements: D202-S203 within the αB region of the lid domain, and E274-Q275 within the b3 strand, as well as W278 in the b3-b4 loop, and H282 in the b4 strand of the Ca2+-binding region. MD simulation analysis showed that catalytic residue S114 and at least one oxyanion hole residue (F17 and/or Q114) in Gb4Lip frequently formed hydrogen bonds with the pNP-C12 ligand at 343 K and 348 K throughout the simulation process, indicating that Gb4Lip might catalyze relatively long-chain ligand pNP-C12 with high performance. In conclusion, Gb4Lip might be a more suitable candidate as the detergent additive. In addition, this investigation can offer valuable perspectives on Family I.5 lipases such as Gb4Lip for future exploration in the field of protein engineering.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04023-5.

Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile

Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 487 protein complexes where AFM fails and obtain an increased success rate (MMscore>0.75) of 33% on these difficult targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks.

Solvent concentration at 50% protein unfolding may reform enzyme stability ranking and process window identification

As water miscible organic co-solvents are often required for enzyme reactions to improve e.g., the solubility of the substrate in the aqueous medium, an enzyme is required which displays high stability in the presence of this co-solvent. Consequently, it is of utmost importance to identify the most suitable enzyme or the appropriate reaction conditions. Until now, the melting temperature is used in general as a measure for stability of enzymes. The experiments here show, that the melting temperature does not correlate to the activity observed in the presence of the solvent. As an alternative parameter, the concentration of the co-solvent at the point of 50% protein unfolding at a specific temperature T in shortc U 50 T is introduced. Analyzing a set of ene reductases,c U 50 T is shown to indicate the concentration of the co-solvent where also the activity of the enzyme drops fastest. Comparing possible rankings of enzymes according to melting temperature andc U 50 T reveals a clearly diverging outcome also depending on the specific solvent used. Additionally, plots ofc U 50 versus temperature enable a fast identification of possible reaction windows to deduce tolerated solvent concentrations and temperature.

A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening

Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features.

The Legionella collagen-like protein employs a distinct binding mechanism for the recognition of host glycosaminoglycans

Bacterial adhesion is a fundamental process which enables colonisation of niche environments and is key for infection. However, in Legionella pneumophila, the causative agent of Legionnaires' disease, these processes are not well understood. The Legionella collagen-like protein (Lcl) is an extracellular peripheral membrane protein that recognises sulphated glycosaminoglycans on the surface of eukaryotic cells, but also stimulates bacterial aggregation in response to divalent cations. Here we report the crystal structure of the Lcl C-terminal domain (Lcl-CTD) and present a model for intact Lcl. Our data reveal that Lcl-CTD forms an unusual trimer arrangement with a positively charged external surface and negatively charged solvent exposed internal cavity. Through molecular dynamics simulations, we show how the glycosaminoglycan chondroitin-4-sulphate associates with the Lcl-CTD surface via distinct binding modes. Our findings show that Lcl homologs are present across both the Pseudomonadota and Fibrobacterota-Chlorobiota-Bacteroidota phyla and suggest that Lcl may represent a versatile carbohydrate-binding mechanism.

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Membraneless channels sieve cations in ammonia-oxidizing marine archaea

Nitrosopumilus maritimus is an ammonia-oxidizing archaeon that is crucial to the global nitrogen cycle1,2. A critical step for nitrogen oxidation is the entrapment of ammonium ions from a dilute marine environment at the cell surface and their subsequent channelling to the cell membrane of N. maritimus. Here we elucidate the structure of the molecular machinery responsible for this process, comprising the surface layer (S-layer), using electron cryotomography and subtomogram averaging from cells. We supplemented our in situ structure of the ammonium-binding S-layer array with a single-particle electron cryomicroscopy structure, revealing detailed features of this immunoglobulin-rich and glycan-decorated S-layer. Biochemical analyses showed strong ammonium binding by the cell surface, which was lost after S-layer disassembly. Sensitive bioinformatic analyses identified similar S-layers in many ammonia-oxidizing archaea, with conserved sequence and structural characteristics. Moreover, molecular simulations and structure determination of ammonium-enriched specimens enabled us to examine the cation-binding properties of the S-layer, revealing how it concentrates ammonium ions on its cell-facing side, effectively acting as a multichannel sieve on the cell membrane. This in situ structural study illuminates the biogeochemically essential process of ammonium binding and channelling, common to many marine microorganisms that are fundamental to the nitrogen cycle.

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure-structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

A comprehensive survey on protein-ligand binding site prediction

Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.

AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the heavy-metal-associated superfamily

Three-dimensional (3D) structure information, now available at the proteome scale, may facilitate the detection of remote evolutionary relationships in protein superfamilies. Here, we illustrate this with the identification of a novel family of protein domains related to the ferredoxin-like superfold, by combining (i) transitive sequence similarity searches, (ii) clustering approaches, and (iii) the use of AlphaFold2 3D structure models. Domains of this family were initially identified in relation with the intracellular biomineralization of calcium carbonates by Cyanobacteria. They are part of the large heavy-metal-associated (HMA) superfamily, departing from the latter by specific sequence and structural features. In particular, most of them share conserved basic amino acids  (hence their name CoBaHMA for Conserved Basic residues HMA), forming a positively charged surface, which is likely to interact with anionic partners. CoBaHMA domains are found in diverse modular organizations in bacteria, existing in the form of monodomain proteins or as part of larger proteins, some of which are membrane proteins involved in transport or lipid metabolism. This suggests that the CoBaHMA domains may exert a regulatory function, involving interactions with anionic lipids. This hypothesis might have a particular resonance in the context of the compartmentalization observed for cyanobacterial intracellular calcium carbonates.

Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands

Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from 14 characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters or novel potential pathogenicity islands (NPPIs) within the Salmonella genome, suggesting their potential contribution to the bacterium's virulence. One of the NPPIs revealed significant overrepresentation of potentially pathogenic proteins. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied.

Structure prediction of protein-ligand complexes from sequence information with Umol

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol .

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

Contemporary HIV-1 consensus Env with AI-assisted redesigned hypervariable loops promote antibody binding

An effective HIV-1 vaccine must elicit broadly neutralizing antibodies (bnAbs) against highly diverse Envelope glycoproteins (Env). Since Env with the longest hypervariable (HV) loops is more resistant to the cognate bnAbs than Env with shorter HV loops, we redesigned hypervariable loops for updated Env consensus sequences of subtypes B and C and CRF01_AE. Using modeling with AlphaFold2, we reduced the length of V1, V2, and V5 HV loops while maintaining the integrity of the Env structure and glycan shield, and modified the V4 HV loop. Spacers are designed to limit strain-specific targeting. All updated Env are infectious as pseudoviruses. Preliminary structural characterization suggests that the modified HV loops have a limited impact on Env's conformation. Binding assays show improved binding to modified subtype B and CRF01_AE Env but not to subtype C Env. Neutralization assays show increases in sensitivity to bnAbs, although not always consistently across clades. Strikingly, the HV loop modification renders the resistant CRF01_AE Env sensitive to 10-1074 despite the absence of a glycan at N332.

Frequent nonhomologous replacement of replicative helicase loaders by viruses in Vibrionaceae

Several microbial genomes lack textbook-defined essential genes. If an essential gene is absent from a genome, then an evolutionarily independent gene of unknown function complements its function. Here, we identified frequent nonhomologous replacement of an essential component of DNA replication initiation, a replicative helicase loader gene, in Vibrionaceae. Our analysis of Vibrionaceae genomes revealed two genes with unknown function, named vdhL1 and vdhL2, that were substantially enriched in genomes without the known helicase-loader genes. These genes showed no sequence similarities to genes with known function but encoded proteins structurally similar with a viral helicase loader. Analyses of genomic syntenies and coevolution with helicase genes suggested that vdhL1/2 encodes a helicase loader. The in vitro assay showed that Vibrio harveyi VdhL1 and Vibrio ezurae VdhL2 promote the helicase activity of DnaB. Furthermore, molecular phylogenetics suggested that vdhL1/2 were derived from phages and replaced an intrinsic helicase loader gene of Vibrionaceae over 20 times. This high replacement frequency implies the host's advantage in acquiring a viral helicase loader gene.

Spatially resolved proteomics of the Arabidopsis stomatal lineage identifies polarity complexes for cell divisions and stomatal pores

Cell polarity is used to guide asymmetric divisions and create morphologically diverse cells. We find that two oppositely oriented cortical polarity domains present during the asymmetric divisions in the Arabidopsis stomatal lineage are reconfigured into polar domains marking ventral (pore-forming) and outward-facing domains of maturing stomatal guard cells. Proteins that define these opposing polarity domains were used as baits in miniTurboID-based proximity labeling. Among differentially enriched proteins, we find kinases, putative microtubule-interacting proteins, and polar SOSEKIs with their effector ANGUSTIFOLIA. Using AI-facilitated protein structure prediction models, we identify potential protein-protein interaction interfaces among them. Functional and localization analyses of the polarity protein OPL2 and its putative interaction partners suggest a positive interaction with mitotic microtubules and a role in cytokinesis. This combination of proteomics and structural modeling with live-cell imaging provides insights into how polarity is rewired in different cell types and cell-cycle stages.

The Pentameric Ligand-Gated Ion Channel Family: A New Member of the Voltage Gated Ion Channel Superfamily?

In this report we present seven lines of bioinformatic evidence supporting the conclusion that the Pentameric Ligand-gated Ion Channel (pLIC) Family is a member of the Voltage-gated Ion Channel (VIC) Superfamily. In our approach, we used the Transporter Classification Database (TCDB) as a reference and applied a series of bioinformatic methods to search for similarities between the pLIC family and members of the VIC superfamily. These include: (1) sequence similarity, (2) compatibility of topology and hydropathy profiles, (3) shared domains, (4) conserved motifs, (5) similarity of Hidden Markov Model profiles between families, (6) common 3D structural folds, and (7) clustering analysis of all families. Furthermore, sequence and structural comparisons as well as the identification of a 3-TMS repeat unit in the VIC superfamily suggests that the sixth transmembrane segment evolved into a re-entrant loop. This evidence suggests that the voltage-sensor domain and the channel domain have a common origin. The classification of the pLIC family within the VIC superfamily sheds light onto the topological origins of this family and its evolution, which will facilitate experimental verification and further research into this superfamily by the scientific community.

Petabase-Scale Homology Search for Structure Prediction

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

Ligand cross-feeding resolves bacterial vitamin B12 auxotrophies

Cobalamin (vitamin B12, herein referred to as B12) is an essential cofactor for most marine prokaryotes and eukaryotes1,2. Synthesized by a limited number of prokaryotes, its scarcity affects microbial interactions and community dynamics2-4. Here we show that two bacterial B12 auxotrophs can salvage different B12 building blocks and cooperate to synthesize B12. A Colwellia sp. synthesizes and releases the activated lower ligand α-ribazole, which is used by another B12 auxotroph, a Roseovarius sp., to produce the corrin ring and synthesize B12. Release of B12 by Roseovarius sp. happens only in co-culture with Colwellia sp. and only coincidently with the induction of a prophage encoded in Roseovarius sp. Subsequent growth of Colwellia sp. in these conditions may be due to the provision of B12 by lysed cells of Roseovarius sp. Further evidence is required to support a causative role for prophage induction in the release of B12. These complex microbial interactions of ligand cross-feeding and joint B12 biosynthesis seem to be widespread in marine pelagic ecosystems. In the western and northern tropical Atlantic Ocean, bacteria predicted to be capable of salvaging cobinamide and synthesizing only the activated lower ligand outnumber B12 producers. These findings add new players to our understanding of B12 supply to auxotrophic microorganisms in the ocean and possibly in other ecosystems.

The Kir2.1E299V mutation increases atrial fibrillation vulnerability while protecting the ventricles against arrhythmias in a mouse model of short QT syndrome type 3

AIMS

Short QT syndrome type 3 (SQTS3) is a rare arrhythmogenic disease caused by gain-of-function mutations in KCNJ2, the gene coding the inward rectifier potassium channel Kir2.1. We used a multidisciplinary approach and investigated arrhythmogenic mechanisms in an in-vivo model of de-novo mutation Kir2.1E299V identified in a patient presenting an extremely abbreviated QT interval and paroxysmal atrial fibrillation.

METHODS AND RESULTS

We used intravenous adeno-associated virus-mediated gene transfer to generate mouse models, and confirmed cardiac-specific expression of Kir2.1WT or Kir2.1E299V. On ECG, the Kir2.1E299V mouse recapitulated the QT interval shortening and the atrial-specific arrhythmia of the patient. The PR interval was also significantly shorter in Kir2.1E299V mice. Patch-clamping showed extremely abbreviated action potentials in both atrial and ventricular Kir2.1E299V cardiomyocytes due to a lack of inward-going rectification and increased IK1 at voltages positive to -80 mV. Relative to Kir2.1WT, atrial Kir2.1E299V cardiomyocytes had a significantly reduced slope conductance at voltages negative to -80 mV. After confirming a higher proportion of heterotetrameric Kir2.x channels containing Kir2.2 subunits in the atria, in-silico 3D simulations predicted an atrial-specific impairment of polyamine block and reduced pore diameter in the Kir2.1E299V-Kir2.2WT channel. In ventricular cardiomyocytes, the mutation increased excitability by shifting INa activation and inactivation in the hyperpolarizing direction, which protected the ventricle against arrhythmia. Moreover, Purkinje myocytes from Kir2.1E299V mice manifested substantially higher INa density than Kir2.1WT, explaining the abbreviation in the PR interval.

CONCLUSION

The first in-vivo mouse model of cardiac-specific SQTS3 recapitulates the electrophysiological phenotype of a patient with the Kir2.1E299V mutation. Kir2.1E299V eliminates rectification in both cardiac chambers but protects against ventricular arrhythmias by increasing excitability in both Purkinje-fiber network and ventricles. Consequently, the predominant arrhythmias are supraventricular likely due to the lack of inward rectification and atrial-specific reduced pore diameter of the Kir2.1E299V-Kir2.2WT heterotetramer.

Rapid and accurate prediction of protein homo-oligomer symmetry with Seq2Symm

The majority of proteins must form higher-order assemblies to perform their biological functions. Despite the importance of protein quaternary structure, there are few machine learning models that can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by training several classes of protein foundation models, including ESM-MSA, ESM2, and RoseTTAFold2, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods. It achieves an average PR-AUC of 0.48 and 0.44 across homo-oligomer symmetries on two different held-out test sets compared to 0.32 and 0.23 for the template-based method. Because Seq2Symm can rapidly predict homo-oligomer symmetries using a single sequence as input (~ 80,000 proteins/hour), we have applied it to 5 entire proteomes and ~ 3.5 million unlabeled protein sequences to identify patterns in protein assembly complexity across biological kingdoms and species.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

Structural determinants for activation of the Tau kinase CDK5 by the serotonin receptor 5-HT7R

BACKGROUND

Multiple neurodegenerative diseases are induced by the formation and deposition of protein aggregates. In particular, the microtubule-associated protein Tau leads to the development of so-called tauopathies characterized by the aggregation of hyperphosphorylated Tau within neurons. We recently showed that the constitutive activity of the serotonin receptor 7 (5-HT7R) is required for Tau hyperphosphorylation and aggregation through activation of the cyclin-dependent kinase 5 (CDK5). We also demonstrated physical interaction between 5-HT7R and CDK5 at the plasma membrane suggesting that the 5-HT7R/CDK5 complex is an integral part of the signaling network involved in Tau-mediated pathology.

METHODS

Using biochemical, microscopic, molecular biological, computational and AI-based approaches, we investigated structural requirements for the formation of 5-HT7R/CDK5 complex.

RESULTS

We demonstrated that 5-HT7R domains responsible for coupling to Gs proteins are not involved in receptor interaction with CDK5. We also created a structural model of the 5-HT7R/CDK5 complex and refined the interaction interface. The model predicted two conserved phenylalanine residues, F278 and F281, within the third intracellular loop of 5-HT7R to be potentially important for complex formation. While site-directed mutagenesis of these residues did not influence Gs protein-mediated receptor signaling, replacement of both phenylalanines by alanine residues significantly reduced 5-HT7R/CDK5 interaction and receptor-mediated CDK5 activation, leading to reduced Tau hyperphosphorylation and aggregation. Molecular dynamics simulations of 5-HT7R/CDK5 complex for wild-type and receptor mutants confirmed binding interface stability of the initial model.

CONCLUSIONS

Our results provide a structural basis for the development of novel drugs targeting the 5-HT7R/CDK5 interaction interface for the selective treatment of Tau-related disorders, including frontotemporal dementia and Alzheimer's disease.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes

BACKGROUND

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance.

RESULTS

We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered.

CONCLUSIONS

Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Lethal perturbation of an Escherichia coli regulatory network is triggered by a restriction-modification system's regulator and can be mitigated by excision of the cryptic prophage Rac

Bacterial gene regulatory networks orchestrate responses to environmental challenges. Horizontal gene transfer can bring in genes with regulatory potential, such as new transcription factors (TFs), and this can disrupt existing networks. Serious regulatory perturbations may even result in cell death. Here, we show the impact on Escherichia coli of importing a promiscuous TF that has adventitious transcriptional effects within the cryptic Rac prophage. A cascade of regulatory network perturbations occurred on a global level. The TF, a C regulatory protein, normally controls a Type II restriction-modification system, but in E. coli K-12 interferes with expression of the RacR repressor gene, resulting in de-repression of the normally-silent Rac ydaT gene. YdaT is a prophage-encoded TF with pleiotropic effects on E. coli physiology. In turn, YdaT alters expression of a variety of bacterial regulons normally controlled by the RcsA TF, resulting in deficient lipopolysaccharide biosynthesis and cell division. At the same time, insufficient RacR repressor results in Rac DNA excision, halting Rac gene expression due to loss of the replication-defective Rac prophage. Overall, Rac induction appears to counteract the lethal toxicity of YdaT. We show here that E. coli rewires its regulatory network, so as to minimize the adverse regulatory effects of the imported C TF. This complex set of interactions may reflect the ability of bacteria to protect themselves by having robust mechanisms to maintain their regulatory networks, and/or suggest that regulatory C proteins from mobile operons are under selection to manipulate their host's regulatory networks for their own benefit.

Protein language model-embedded geometric graphs power inter-protein contact prediction

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

Prediction of protein-ATP binding residues using multi-view feature learning via contextual-based co-attention network

Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting more discriminative features and inadequate algorithms for integrating protein and residue information. To address the problems, we propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models and incorporates domain-specific evolutionary context information from homologous sequences. It further refines the embedding at the residue level through integration with corresponding protein-level information and employs a contextual-based co-attention mechanism to adeptly fuse multiple sources of features. The performance evaluation results on the benchmark datasets reveal that ATP-Deep achieves an AUC of 0.954 and 0.951, respectively, surpassing the performance of the state-of-the-art model. These findings underscore the effectiveness of assimilating protein-level information and deploying a contextual-based co-attention mechanism grounded in context to bolster the prediction performance of protein-ATP binding residues.

2,4-disubstituted 6-fluoroquinolines as potent antiplasmodial agents: QSAR, homology modeling, molecular docking and ADMET studies

Objective

This work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target.

Methods

Quantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated.

Results

A robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from -8.200 to -10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target.

Conclusion

QSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

PLMSearch: Protein language model powers accurate and fast sequence search for remote homology

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

PyCoM: a python library for large-scale analysis of residue-residue coevolution data

MOTIVATION

Computational methods to detect correlated amino acid positions in proteins have become a valuable tool to predict intra- and inter-residue protein contacts, protein structures, and effects of mutation on protein stability and function. While there are many tools and webservers to compute coevolution scoring matrices, there is no central repository of alignments and coevolution matrices for large-scale studies and pattern detection leveraging on biological and structural annotations already available in UniProt.

RESULTS

We present a Python library, PyCoM, which enables users to query and analyze coevolution matrices and sequence alignments of 457 622 proteins, selected from UniProtKB/Swiss-Prot database (length ≤ 500 residues), from a precompiled coevolution matrix database (PyCoMdb). PyCoM facilitates the development of statistical analyses of residue coevolution patterns using filters on biological and structural annotations from UniProtKB/Swiss-Prot, with simple access to PyCoMdb for both novice and advanced users, supporting Jupyter Notebooks, Python scripts, and a web API access. The resource is open source and will help in generating data-driven computational models and methods to study and understand protein structures, stability, function, and design.

AVAILABILITY AND IMPLEMENTATION

PyCoM code is freely available from https://github.com/scdantu/pycom and PyCoMdb and the Jupyter Notebook tutorials are freely available from https://pycom.brunel.ac.uk.

Repeated co-option of HMG-box genes for sex determination in brown algae and animals

In many eukaryotes, genetic sex determination is not governed by XX/XY or ZW/ZZ systems but by a specialized region on the poorly studied U (female) or V (male) sex chromosomes. Previous studies have hinted at the existence of a dominant male-sex factor on the V chromosome in brown algae, a group of multicellular eukaryotes distantly related to animals and plants. The nature of this factor has remained elusive. Here, we demonstrate that an HMG-box gene acts as the male-determining factor in brown algae, mirroring the role HMG-box genes play in sex determination in animals. Over a billion-year evolutionary timeline, these lineages have independently co-opted the HMG box for male determination, representing a paradigm for evolution's ability to recurrently use the same genetic "toolkit" to accomplish similar tasks.

Caspase-1 activates gasdermin A in non-mammals

Gasdermins oligomerize to form pores in the cell membrane, causing regulated lytic cell death called pyroptosis. Mammals encode five gasdermins that can trigger pyroptosis: GSDMA, B, C, D, and E. Caspase and granzyme proteases cleave the linker regions of and activate GSDMB, C, D, and E, but no endogenous activation pathways are yet known for GSDMA. Here, we perform a comprehensive evolutionary analysis of the gasdermin family. A gene duplication of GSDMA in the common ancestor of caecilian amphibians, reptiles, and birds gave rise to GSDMA-D in mammals. Uniquely in our tree, amphibian, reptile, and bird GSDMA group in a separate clade than mammal GSDMA. Remarkably, GSDMA in numerous bird species contain caspase-1 cleavage sites like YVAD or FASD in the linker. We show that GSDMA from birds, amphibians, and reptiles are all cleaved by caspase-1. Thus, GSDMA was originally cleaved by the host-encoded protease caspase-1. In mammals the caspase-1 cleavage site in GSDMA is disrupted; instead, a new protein, GSDMD, is the target of caspase-1. Mammal caspase-1 uses exosite interactions with the GSDMD C-terminal domain to confer the specificity of this interaction, whereas we show that bird caspase-1 uses a stereotypical tetrapeptide sequence to confer specificity for bird GSDMA. Our results reveal an evolutionarily stable association between caspase-1 and the gasdermin family, albeit a shifting one. Caspase-1 repeatedly changes its target gasdermin over evolutionary time at speciation junctures, initially cleaving GSDME in fish, then GSDMA in amphibians/reptiles/birds, and finally GSDMD in mammals.