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

Applicability of AlphaFold2 in the modeling of dimeric, trimeric, and tetrameric coiled-coil domains

Coiled coils are a common protein structural motif involved in cellular functions ranging from mediating protein-protein interactions to facilitating processes such as signal transduction or regulation of gene expression. They are formed by two or more alpha helices that wind around a central axis to form a buried hydrophobic core. Various forms of coiled-coil bundles have been reported, each characterized by the number, orientation, and degree of winding of the constituent helices. This variability is underpinned by short sequence repeats that form coiled coils and whose properties determine both their overall topology and the local geometry of the hydrophobic core. The strikingly repetitive sequence has enabled the development of accurate sequence-based coiled-coil prediction methods; however, the modeling of coiled-coil domains remains a challenging task. In this work, we evaluated the accuracy of AlphaFold2 in modeling coiled-coil domains, both in modeling local geometry and in predicting global topological properties. Furthermore, we show that the prediction of the oligomeric state of coiled-coil bundles can be achieved by using the internal representations of AlphaFold2, with a performance better than any previous state-of-the-art method (code available at https://github.com/labstructbioinf/dc2_oligo).

Deciphering the cleavage sites of 3C-like protease in Gammacoronaviruses and Deltacoronaviruses

Coronaviruses replicate by using the 3C-like protease (3CLpro) to cleave polyprotein precursors and host proteins. However, current tools for identifying 3CLpro cleavage sites are limited, particularly in Gammacoronaviruses (GammaCoV) and Deltacoronaviruses (DeltaCoV). This study aims to fill this gap by identifying 3CLpro cleavage sites in these viruses to provide deeper insights into their pathogenic mechanisms. By integrating sequence alignments and structural model comparisons, we developed a position-specific scoring matrix (PSSM) based on self-cleavage motifs, revealing specific preferences for each residue. Utilizing AlphaFold2's predicted alignment error (PAE) and predicted local distance difference test (pLDDT), we found that most cleavage sequences are located in regions with high PAE and low pLDDT values. KEGG pathway analysis showed that potential host protein cleavage targets are mainly concentrated in pathways related to nucleo-cytoplasmic transport and endocytosis. Through in vitro cleavage experiments and mutational analysis, we identified and validated three high-scoring proteins-nucleoporin 58 (NUP58), cell division cycle 73 (CDC73), and signal transducing adaptor molecule 2 (STAM2). These findings suggest that 3CLpro not only plays a vital role in viral replication but may also influence host cell functions by cleaving host proteins. This study provides an effective tool for identifying 3CLpro cleavage sites, revealing the pathogenic mechanisms of coronaviruses, and offering new insights for developing potential therapeutic targets.

Target protein identification in live cells and organisms with a non-diffusive proximity tagging system

Identifying target proteins for bioactive molecules is essential for understanding their mechanisms, developing improved derivatives, and minimizing off-target effects. Despite advances in target identification (target-ID) technologies, significant challenges remain, impeding drug development. Most target-ID methods use cell lysates, but maintaining an intact cellular context is vital for capturing specific drug-protein interactions, such as those with transient protein complexes and membrane-associated proteins. To address these limitations, we developed POST-IT (Pup-On-target for Small molecule Target Identification Technology), a non-diffusive proximity tagging system for live cells, orthogonal to the eukaryotic system. POST-IT utilizes an engineered fusion of proteasomal accessory factor A and HaloTag to transfer Pup to proximal proteins upon directly binding to the small molecule. After significant optimization to eliminate self-pupylation and polypupylation, minimize depupylation, and optimize chemical linkers, POST-IT successfully identified known targets and discovered a new binder, SEPHS2, for dasatinib, and VPS37C as a new target for hydroxychloroquine, enhancing our understanding these drugs' mechanisms of action. Furthermore, we demonstrated the application of POST-IT in live zebrafish embryos, highlighting its potential for broad biological research and drug development.

PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings

MOTIVATION

Recent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.

RESULTS

We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera. Compared against recent tools, PHIStruct exhibits the best balance of precision and recall, with the highest and most stable F1 score across a wide range of confidence thresholds and sequence similarity settings. The margin in performance is most pronounced when the sequence similarity between the training and test sets drops below 40%, wherein, at a relatively high-confidence threshold of above 50%, PHIStruct presents a 7%-9% increase in class-averaged F1 over machine learning tools that do not directly incorporate structure information, as well as a 5%-6% increase over BLASTp.

AVAILABILITY AND IMPLEMENTATION

The data and source code for our experiments and analyses are available at https://github.com/bioinfodlsu/PHIStruct.

Zinc promotes microbial p-coumaric acid production that protects against cholestatic liver injury

Cholestatic liver disease (CLD) is a common liver disorder with limited treatment options. Here, we demonstrate that zinc (Zn) supplementation can alter the gut microbiome to mitigate cholestatic liver injury. Oral Zn altered the microbiota of mice and humans (this study was registered at clinicaltrials.gov [NCT05597137]), increasing the abundance of Blautia producta (B. producta) and promoting the generation of p-coumaric acid. Additionally, p-coumaric acid concentrations were negatively correlated with liver injury parameters in CLD patients. In mice, the protective effects of Zn were partially mediated by p-coumaric acid, which directly bound to nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 2 (NOX2) and suppressed the production of reactive oxygen species (ROS) in hepatocytes, thus preventing hepatocyte cell death and liver damage. Additionally, knocking out the histidine ammonia-lyase, which catalyzes the conversion of tyrosine to p-coumaric acid in B. producta, blunted the protective effects of Zn. These findings highlight a host-microbiota interaction that is stimulated by Zn supplementation, providing potential benefits for CLD.

The key monooxygenase involved in phenanthrene degradation of Ruegeria sp. PrR005

As a typical polycyclic aromatic hydrocarbon (PAH), phenanthrene is often present in diverse environments, leading to severe environmental contamination. However, bacterial degradation plays a crucial role in remediating phenanthrene contamination and has been widely adopted. The widely distributed marine Roseobacter-clade bacteria are frequently found in phenanthrene-contaminated environments, but their catalyzing ability and related molecular mechanism have been rarely elucidated. Our previous work showed Ruegeria sp. PrR005 isolated from the Pearl River Estuary sediment could degrade phenanthrene and other PAHs. Integrated approaches including multi-omics and biochemical analysis were applied here to explore its catabolism mechanism. The genomic and transcriptomic analysis indicated that six new P450 monooxygenase proteins could be closely associated with phenanthrene degradation. Heterologous expression of P450 monooxygenase candidates revealed that PrR005_00615, PrR005_04282, PrR005_04577 have considerable activity in phenanthrene removal, with PrR005_00615 being the primary contributor. Further, the biochemical and metabolic analysis revealed that PrR005_00615 could catalyze phenanthrene to phenanthrene-9,10-epoxide by introducing an oxygen atom at 9,10-carbon positions, which functioned as a monooxygenase. The present study provides compelling evidences of a novel enzyme responsible for catalyzing the initial step of phenanthrene transformation in PrR005. These findings hold significant importance in unraveling the mechanism behind phenanthrene degradation by Roseobacter-clade bacteria.

Discovery of a novel marine Bacteroidetes with a rich repertoire of carbohydrate-active enzymes

Members of the phylum Bacteroidetes play a key role in the marine carbon cycle through their degradation of polysaccharides via carbohydrate-active enzymes (CAZymes) and polysaccharide utilization loci (PULs). The discovery of novel CAZymes and PULs is important for our understanding of the marine carbon cycle. In this study, we isolated and identified a potential new genus of the family Catalimonadaceae, in the phylum Bacteroidetes, from the southwest Indian Ocean. Strain TK19036, the type strain of the new genus, is predicted to encode CAZymes that are relatively abundant in marine Bacteroidetes genomes. Tunicatimonas pelagia NBRC 107804T, Porifericola rhodea NBRC 107748T and Catalinimonas niigatensis NBRC 109829T, which exhibit 16 S rRNA similarities exceeding 90% with strain TK19036, and belong to the same family, were selected as reference strains. These organisms possess a highly diverse repertoire of CAZymes and PULs, which may enable them to degrade a wide range of polysaccharides, especially pectin and alginate. In addition, some secretory CAZymes in strain TK19036 and its relatives were predicted to be transported by type IX secretion system (T9SS). Further, to the best of our knowledge, we propose the first reported "hybrid" PUL targeting alginates in T. pelagia NBRC 107804T. Our findings provide new insights into the polysaccharide degradation capacity of marine Bacteroidetes, and suggest that T9SS may play a more important role in this process than previously believed.

lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root-mean-square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Identification and Application of Novel Patulin-Degrading Enzymes from Bacillus subtilis 168

Patulin (PAT), a toxic secondary metabolite produced mainly by Penicillium species that frequently contaminates fruit and fruit-derived products, poses serious health risks to humans and animals. In the present study, three short-chain dehydrogenases/reductases (SDRs) with PAT-degrading ability, designated BsSDR1, BsSDR2, and BsSDR3, were identified from the genome of Bacillus subtilis 168. BsSDR1 and BsSDR2 showed powerful PAT elimination abilities, which can completely convert PAT to nontoxic E-ascladiol. Moreover, BsSDR1, BsSDR2, and BsSDR3 shared the highest sequence identity of 36.03% with the reported PAT-degrading enzymes, indicating that they are novel PAT-degrading enzymes. BsSDR1, BsSDR2, and BsSDR3 exhibited the highest activity against PAT at 40, 40, and 35 °C, respectively. Additionally, BsSDR1, BsSDR2, and BsSDR3 displayed remarkable thermostability, retaining 32.50, 24.63, and 46.74% residual activity, respectively, after incubation at 50 °C for 1 h. Three-dimensional (3D) simulation and site-directed mutagenesis indicated that the catalytic triad formed by the residues (Ser, Tyr, and Lys) was the key for SDR activity, and this conserved catalytic mechanism was followed in the catalytic process of novel PAT-degrading enzymes BsSDR1, BsSDR2, and BsSDR3. More importantly, BsSDR1, BsSDR2, and BsSDR3 can degrade PAT in apple juice at rates of 86.90, 90.17, and 61.57%, respectively. The identification of BsSDR1, BsSDR2, and BsSDR3 enriched the PAT-degrading enzyme libraries, providing promising candidates for PAT decontamination in the food industry.

Conserved effector families render Phytophthora species vulnerable to recognition by NLR receptors in nonhost plants

NLR receptor is suggested as a component of plant nonhost resistance (NHR). However, the evolutionary process of how plants develop receptors for recognizing broad-spectrum pathogens is still elusive. Here, we observe that multiple RxLR effector families including 12 reported avirulence effectors of Phytophthora infestans are broadly conserved across the Phytophthora species. We select 69 effectors distributed into 8 families from 6 Phytophthora species, and confirm that 60.87% of the tested effectors are recognized by Solanum NLRs according to their defined families. Furthermore, we confirm that expression of R1, R8, and Rpi-amr1 confer broad-spectrum resistance against multiple Phytophthora species. Combined results suggest that conserved effector families of Phytophthora species allow solanaceous plants to recognize broad-spectrum pathogens via NLRs that originally reported to recognize P. infestans. Thus, NLR-mediated recognition would contribute to NHR against pathogens that possess similar repertoires of effectors. Moreover, this homology-based approach would be applicable to other plant-pathogen systems and provide an alternative strategy of genetic mapping to identify functional NLRs against various crop-threatening pathogens.

Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

The discovery of CRISPR-Cas systems has paved the way for advanced gene editing tools. However, traditional Cas discovery methods relying on sequence similarity may miss distant homologs and aren't suitable for functional recognition. With protein large language models (LLMs) evolving, there is potential for Cas system modeling without extensive training data. Here, we introduce CHOOSER (Cas HOmlog Observing and SElf-processing scReening), an AI framework for alignment-free discovery of CRISPR-Cas systems with self-processing pre-crRNA capability using protein foundation models. By using CHOOSER, we identify 11 Casλ homologs, nearly doubling the known catalog. Notably, one homolog, EphcCasλ, is experimentally validated for self-processing pre-crRNA, DNA cleavage, and trans-cleavage, showing promise for CRISPR-based pathogen detection. This study highlights an innovative approach for discovering CRISPR-Cas systems with specific functions, emphasizing their potential in gene editing.

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

Structural diversity and clustering of bacterial flagellar outer domains

Supercoiled flagellar filaments function as mechanical propellers within the bacterial flagellum complex, playing a crucial role in motility. Flagellin, the building block of the filament, features a conserved inner D0/D1 core domain across different bacterial species. In contrast, approximately half of the flagellins possess additional, highly divergent outer domain(s), suggesting varied functional potential. In this study, we report atomic structures of flagellar filaments from three distinct bacterial species: Cupriavidus gilardii, Stenotrophomonas maltophilia, and Geovibrio thiophilus. Our findings reveal that the flagella from the facultative anaerobic G. thiophilus possesses a significantly more negatively charged surface, potentially enabling adhesion to positively charged minerals. Furthermore, we analyze all AlphaFold predicted structures for annotated bacterial flagellins, categorizing the flagellin outer domains into 682 structural clusters. This classification provides insights into the prevalence and experimental verification of these outer domains. Remarkably, two of the flagellar structures reported herein belong to a distinct cluster, indicating additional opportunities on the study of the functional diversity of flagellar outer domains. Our findings underscore the complexity of bacterial flagellins and open up possibilities for future studies into their varied roles beyond motility.

The genome of Lupinus mutabilis: Evolution and genetics of an emerging bio-based crop

Lupinus mutabilis is an under-domesticated legume species from the Andean region of South America. It belongs to the New World lupins clade, which groups several lupin species displaying large genetic variation and adaptability to highly different environments. L. mutabilis is attracting interest as a potential multipurpose crop to diversify the European supply of plant proteins, increase agricultural biodiversity, and fulfill bio-based applications. This study reports the first high-quality L. mutabilis genome assembly, which is also the first sequenced assembly of a New World lupin species. Through comparative genomics and phylogenetics, the evolution of L. mutabilis within legumes and lupins is described, highlighting both genomic similarities and patterns specific to L. mutabilis, potentially linked to environmental adaptations. Furthermore, the assembly was used to study the genetics underlying important traits for the establishment of L. mutabilis as a novel crop, including protein and quinolizidine alkaloids contents in seeds, genomic patterns of classic resistance genes, and genomic properties of L. mutabilis mycorrhiza-related genes. These analyses pointed out copy number variation, differential genomic gene contexts, and gene family expansion through tandem duplications as likely important drivers of the genomic diversity observed for these traits between L. mutabilis and other lupins and legumes. Overall, the L. mutabilis genome assembly will be a valuable resource to conduct genetic research and enable genomic-based breeding approaches to turn L. mutabilis into a multipurpose legume crop.

The structure investigation of GH174 endo-1,3-fucanase revealed an unusual glycoside hydrolase fold

Sulfated fucan has attracted increasing research interest due to its various biological activities. Endo-1,3-fucanases are favorable tools for structure investigation and structure-activity relationships establishment of sulfated fucan. However, the three-dimensional structure of enzymes from the GH174 family has not been disclosed, which hinders the understanding of the action mechanism. This study reports the first crystal structure of endo-1,3-fucanase from GH174 family (Fun174A) at a resolution of 1.60 Å. Notably, Fun174A exhibited an unusual distorted β-sandwich fold, which is distinct from other known glycoside hydrolase folds. The conserved amino acid residues D119 and H154 were proposed as the catalytic residues in the family. Molecular docking suggested that Fun174A primarily recognized sulfated fucan through a series of polar amino acid residues around the substrate binding pocket. Furthermore, structural bioinformatics analysis suggested that the structural analogs of Fun174A may be extensively implicated in the bacterial metabolism of polysaccharides, which provided opportunities for the discovery of novel glycoside hydrolases. This study offers new insights into the structural diversity of glycoside hydrolases and will contribute to the establishment of a novel clan of glycoside hydrolases.

Identification, isoform classification, ligand binding, and database construction of the protein-tyrosine sulfotransferase family in metazoans

Protein tyrosine sulfonation (PTS) influences various crucial physiological and pathological processes in animals. Protein-tyrosine sulfotransferase (TPST) serves as a pivotal enzyme in this process. Research on TPST is still in its early stages, and current identification methods have not yet effectively differentiated TPST from other type II sulfotransferases. Furthermore, this study has revealed that TPST in animals is highly conserved and exhibits significant differences when compared to other sulfotransferases and TPSTs in non-animal species. However, precise and efficient methods for identifying TPST, conducting subfamily classification, performing functional and sequence analyses, and accessing corresponding databases and analytical platforms for the entire TPST family of metazoan species are lacking. These findings provide a foundation for more in-depth research on TPST in animals and are crucial for advancing the understanding of PTS and its broader impacts. In this study, a Hidden Markov Model (TPST-HMM) was formulated based on the conserved motifs binding to the substrate PAPS and the ligand tyrosine in metazoan TPSTs. TPST-HMM successfully identified more than 91.8 % of metazoan TPSTs in UniProt (e-value < 1e-5). When the threshold was adjusted to 1e-20, the identification rate of TPST was 83.9 % in metazoans and approximately 0 % in other species (fungi, bacteria, etc.). Subsequently, 5638 TPSTs were identified from 1311 metazoan genomes, and these TPSTs were classified into three subfamilies. The classification of the TPST1 and TPST2 subtypes, which were initially annotated in mammals, was extended across vertebrates. Additionally, a novel subtype, TPST3, belonging to a distinct subfamily, was discovered in invertebrates. We proposed a molecular docking prediction method for TPST and tyrosine ligands based on the observation that TPST-tyrosine binding recognition and binding in metazoans were primarily driven by electrostatic interactions. Finally, a database website for animal TPST sequences was established (http://sz.bjfskj.com/). The website included an online tool for identifying TPST protein sequences, enabling annotation and visualization of functional motifs and active amino acids. Its design aimed to assist users in studying TPST in animals.

Deep generative design of RNA aptamers using structural predictions

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

Cognitive Impact of Neurotropic Pathogens: Investigating Molecular Mimicry through Computational Methods

Neurotropic pathogens, notably, herpesviruses, have been associated with significant neuropsychiatric effects. As a group, these pathogens can exploit molecular mimicry mechanisms to manipulate the host central nervous system to their advantage. Here, we present a systematic computational approach that may ultimately be used to unravel protein-protein interactions and molecular mimicry processes that have not yet been solved experimentally. Toward this end, we validate this approach by replicating a set of pre-existing experimental findings that document the structural and functional similarities shared by the human cytomegalovirus-encoded UL144 glycoprotein and human tumor necrosis factor receptor superfamily member 14 (TNFRSF14). We began with a thorough exploration of the Homo sapiens protein database using the Basic Local Alignment Search Tool (BLASTx) to identify proteins sharing sequence homology with UL144. Subsequently, we used AlphaFold2 to predict the independent three-dimensional structures of UL144 and TNFRSF14. This was followed by a comprehensive structural comparison facilitated by Distance-Matrix Alignment and Foldseek. Finally, we used AlphaFold-multimer and PPIscreenML to elucidate potential protein complexes and confirm the predicted binding activities of both UL144 and TNFRSF14. We then used our in silico approach to replicate the experimental finding that revealed TNFRSF14 binding to both B- and T-lymphocyte attenuator (BTLA) and glycoprotein domain and UL144 binding to BTLA alone. This computational framework offers promise in identifying structural similarities and interactions between pathogen-encoded proteins and their host counterparts. This information will provide valuable insights into the cognitive mechanisms underlying the neuropsychiatric effects of viral infections.

Ribosomal A site binding pattern differs between Arm methyltransferases from clinical pathogens and a natural producer of aminoglycosides

The extensive use of aminoglycosides to treat bacterial infections has led to significant resistance, posing a global health threat. Recent clinical reports highlight high levels of aminoglycoside resistance due to Arm/Kam methyltransferases, which methylate specific nucleotides in 16S rRNA, preventing antibiotic binding to the ribosome. This study compared the ribosomal A site binding patterns of Arm methyltransferases from clinical pathogens (ArmA, RmtB, RmtC, and RmtD) with those of the Sgm methyltransferase from a natural aminoglycoside producer. We introduced single mutations near the G1405 nucleotide in helix 44 of 16S rRNA to assess their impact on the methylation ability of Arm methyltransferases in E. coli cells with homogeneous mutant ribosomes. We evaluated how these mutations affected bacterial viability in cells with mixed and homogeneous ribosome populations and determined the minimal inhibitory concentration of kanamycin to assess their impact on Arm enzyme activity. Notably, Sgm methyltransferase exhibited a distinct methylation pattern compared to Arm methyltransferases from clinical strains. Structural comparisons of Sgm, RmtB, and RmtC revealed different spatial orientations of key amino acids involved in ribosomal binding, highlighting evolutionary differences. This research enhances understanding of Arm methyltransferases and lays the groundwork for designing inhibitors to combat this potent form of antibiotic resistance.

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

ARTEMIS: a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment

Non-coding RNAs play a major role in diverse processes in living cells with their sequence and spatial structure serving as the principal determinants of their function. Superposition of RNA 3D structures is the most accurate method for comparative analysis of RNA molecules and for inferring structure-based sequence alignments. Topology-independent superposition is particularly relevant, as evidenced by structurally similar RNAs with sequence permutations such as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure superposition rely on intricate heuristics, and the potential for topology-independent superposition has not been exhausted. Recently, we introduced the ARTEM method for unrestrained pairwise superposition of RNA 3D modules and now we developed it further to solve the global RNA 3D structure alignment problem. Our new tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition. Using ARTEMIS we discovered a helical packing motif to be preserved within different backbone topology contexts across various non-coding RNAs, including multiple ribozymes and riboswitches. We anticipate that ARTEMIS will be essential for elucidating the landscape of RNA 3D folds and motifs featuring sequence permutations that thus far remained unexplored due to limitations in previous computational approaches.

Accelerated discovery and miniaturization of novel single-stranded cytidine deaminases

Cytidine base editors (CBEs) hold significant potential in genetic disease treatment and in breeding superior traits into animals. However, their large protein sizes limit their delivery by adeno-associated virus (AAV), given its packing capacity of <4.7 kb. To overcome this, we employed a web-based fast generic discovery (WFG) strategy, identifying several small ssDNA deaminases (Sdds) and constructing multiple Sdd-CBE 1.0 versions. SflSdd-CBE 1.0 demonstrated high C-to-T editing efficiency, comparable to AncBE4max, while SviSdd-CBE 1.0 exhibited moderate C-to-T editing efficiency with a narrow editing window (C3 to C5). Utilizing AlphaFold2, we devised a one-step miniaturization strategy, reducing the size of Sdds while preserving their efficiency. Notably, we administered AAV8 expressing PCSK9 targeted sgRNA and SflSdd-CBEs (nSaCas9) 2.0 into mice, leading to gene-editing events (with editing efficiency up to 15%) and reduced serum cholesterol levels, underscoring the potential of Sdds in gene therapy. These findings offer new single-stranded editing tools for the treatment of rare genetic diseases.

Laccase surface-display for environmental tetracycline removal: From structure to function

Facing the increasingly prominent tetracycline pollution and the resulting environmental problems, how to find environmental and efficient treatment means is one of the current research hotspots. In this study, the laccase surface-display technology for tetracycline treatment was investigated. Via study, the type of anchoring protein had a minor influence on the laccase ability, while the type of laccase showed a major impact. Bacillus subtilis spore coat protein (CotA) exhibited higher laccase activity, stability, and efficiency in degrading tetracycline than Pleurotus ostreatus laccase 6 (Lacc6). The superiority of bacterial laccase over fungal laccase was elucidated from the perspective of crystal structure. Besides, a variety of technical means were used to verify the success of surface-display. pGSA-CotA surface-displayed bacteria exhibited good tolerance to high temperature, pH, and various heavy metals. Importantly, surface-displayed bacteria showed faster degradation efficiency and better treatment effects than the intracellular expression bacteria in tetracycline degradation. This implies that surface display technology has greater potential for laccase-mediated environmental remediation. Due to the adverse impacts of tetracycline on soil enzyme activity and microorganisms, our study found that pGSA-CotA surface-displayed bacteria can alleviate tetracycline stress in soil and partially activate the soil, thereby increasing soil enzyme activity and certain nitrogen cycling genes.

DockQ v2: improved automatic quality measure for protein multimers, nucleic acids, and small molecules

MOTIVATION

It is important to assess the quality of modeled biomolecules to benchmark and assess the performance of different prediction methods. DockQ has emerged as the standard tool for assessing the quality of protein interfaces in model structures against given references. However, as predictions of large multimers with multiple chains become more common, DockQ needs to be updated with more functionality for robustness and speed. Moreover, as the field progresses and more methods are released to predict interactions between proteins and other types of molecules, such as nucleic acids and small molecules, it becomes necessary to have a tool that can assess all types of interactions.

RESULTS

Here, we present a complete reimplementation of DockQ in pure Python. The updated version of DockQ is more portable, faster and introduces novel functionalities, such as automatic DockQ calculations for multiple interfaces and automatic chain mapping with multi-threading. These enhancements are designed to facilitate comparative analyses of protein complexes, particularly large multi-chain complexes. Furthermore, DockQ is now also able to score interfaces between proteins, nucleic acids, and small molecules.

AVAILABILITY AND IMPLEMENTATION

DockQ v2 is available online at: https://wallnerlab.org/DockQ.

PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity

BACKGROUND

Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types.

RESULTS

Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks.

CONCLUSIONS

PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Nucleocapsid Protein Variants

The COVID-19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until 23 July 2023. Since the three-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted co-operatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

High-throughput prioritization of target proteins for development of new antileishmanial compounds

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

The structure assessment web server: for proteins, complexes and more

The 'structure assessment' web server is a one-stop shop for interactive evaluation and benchmarking of structural models of macromolecular complexes including proteins and nucleic acids. A user-friendly web dashboard links sequence with structure information and results from a variety of state-of-the-art tools, which facilitates the visual exploration and evaluation of structure models. The dashboard integrates stereochemistry information, secondary structure information, global and local model quality assessment of the tertiary structure of comparative protein models, as well as prediction of membrane location. In addition, a benchmarking mode is available where a model can be compared to a reference structure, providing easy access to scores that have been used in recent CASP experiments and CAMEO. The structure assessment web server is available at https://swissmodel.expasy.org/assess.

MinActionPath2: path generation between different conformations of large macromolecular assemblies by action minimization

Recent progress in solving macromolecular structures and assemblies by cryogenic electron microscopy techniques enables sampling of their conformations in different states that are relevant to their biological function. Knowing the transition path between these conformations would provide new avenues for drug discovery. While the experimental study of transition paths is intrinsically difficult, in-silico methods can be used to generate an initial guess for those paths. The Elastic Network Model (ENM), along with a coarse-grained representation (CG) of the structures are among the most popular models to explore such possible paths. Here we propose an update to our software platform MinActionPath that generates non-linear transition paths based on ENM and CG models, using action minimization to solve the equations of motion. The new website enables the study of large structures such as ribosomes or entire virus envelopes. It provides direct visualization of the trajectories along with quantitative analyses of their behaviors at http://dynstr.pasteur.fr/servers/minactionpath/minactionpath2_submission.

AlphaFind: discover structure similarity across the proteome in AlphaFold DB

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid UniProt ID, Protein Data Bank ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz.

Insights into docking in megasynthases from the investigation of the toblerol trans-AT polyketide synthase: many α-helical means to an end

The fidelity of biosynthesis by modular polyketide synthases (PKSs) depends on specific moderate affinity interactions between successive polypeptide subunits mediated by docking domains (DDs). These sequence elements are notably portable, allowing their transplantation into alternative biosynthetic and metabolic contexts. Herein, we use integrative structural biology to characterize a pair of DDs from the toblerol trans-AT PKS. Both are intrinsically disordered regions (IDRs) that fold into a 3 α-helix docking complex of unprecedented topology. The C-terminal docking domain (CDD) resembles the 4 α-helix type (4HB) CDDs, which shows that the same type of DD can be redeployed to form complexes of distinct geometry. By carefully re-examining known DD structures, we further extend this observation to type 2 docking domains, establishing previously unsuspected structural relations between DD types. Taken together, these data illustrate the plasticity of α-helical DDs, which allow the formation of a diverse topological spectrum of docked complexes. The newly identified DDs should also find utility in modular PKS genetic engineering.

The landscape of RNA 3D structure modeling with transformer networks

Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.

FURNA: A database for functional annotations of RNA structures

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by noncoding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand-binding sites, RNA families, protein-binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.

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.

Cotton plants overexpressing the Bacillus thuringiensis Cry23Aa and Cry37Aa binary-like toxins exhibit high resistance to the cotton boll weevil (Anthonomus grandis)

The cotton boll weevil (CBW, Anthonomus grandis) stands as one of the most significant threats to cotton crops (Gossypium hirsutum). Despite substantial efforts, the development of a commercially viable transgenic cotton event for effective open-field control of CBW has remained elusive. This study describes a detailed characterization of the insecticidal toxins Cry23Aa and Cry37Aa against CBW. Our findings reveal that CBW larvae fed on artificial diets supplemented exclusively with Cry23Aa decreased larval survival by roughly by 69%, while supplementation with Cry37Aa alone displayed no statistical difference compared to the control. However, the combined provision of both toxins in the artificial diet led to mortality rates approaching 100% among CBW larvae (LC50 equal to 0.26 PPM). Additionally, we engineered transgenic cotton plants by introducing cry23Aa and cry37Aa genes under control of the flower bud-specific pGhFS4 and pGhFS1 promoters, respectively. Seven transgenic cotton events expressing high levels of Cry23Aa and Cry37Aa toxins in flower buds were selected for greenhouse bioassays, and the mortality rate of CBW larvae feeding on their T0 and T1 generations ranged from 75% to 100%. Our in silico analyses unveiled that Cry23Aa displays all the hallmark characteristics of β-pore-forming toxins (β-PFTs) that bind to sugar moieties in glycoproteins. Intriguingly, we also discovered a distinctive zinc-binding site within Cry23Aa, which appears to be involved in protein-protein interactions. Finally, we discuss the major structural features of Cry23Aa that likely play a role in the toxin's mechanism of action. In view of the low LC50 for CBW larvae and the significant accumulation of these toxins in the flower buds of both T0 and T1 plants, we anticipate that through successive generations of these transgenic lines, cotton plants engineered to overexpress cry23Aa and cry37Aa hold promise for effectively managing CBW infestations in cotton crops.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models

MOTIVATION

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation.

RESULTS

In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/ml4bio/RiboDiffusion.

The inorganic pyrophosphatases of microorganisms: a structural and functional review

Pyrophosphatases (PPases) are enzymes that catalyze the hydrolysis of pyrophosphate (PPi), a byproduct of the synthesis and degradation of diverse biomolecules. The accumulation of PPi in the cell can result in cell death. Although the substrate is the same, there are variations in the catalysis and features of these enzymes. Two enzyme forms have been identified in bacteria: cytoplasmic or soluble pyrophosphatases and membrane-bound pyrophosphatases, which play major roles in cell bioenergetics. In eukaryotic cells, cytoplasmic enzymes are the predominant form of PPases (c-PPases), while membrane enzymes (m-PPases) are found only in protists and plants. The study of bacterial cytoplasmic and membrane-bound pyrophosphatases has slowed in recent years. These enzymes are central to cell metabolism and physiology since phospholipid and nucleic acid synthesis release important amounts of PPi that must be removed to allow biosynthesis to continue. In this review, two aims were pursued: first, to provide insight into the structural features of PPases known to date and that are well characterized, and to provide examples of enzymes with novel features. Second, the scientific community should continue studying these enzymes because they have many biotechnological applications. Additionally, in this review, we provide evidence that there are m-PPases present in fungi; to date, no examples have been characterized. Therefore, the diversity of PPase enzymes is still a fruitful field of research. Additionally, we focused on the roles of H+/Na+ pumps and m-PPases in cell bioenergetics. Finally, we provide some examples of the applications of these enzymes in molecular biology and biotechnology, especially in plants. This review is valuable for professionals in the biochemistry field of protein structure-function relationships and experts in other fields, such as chemistry, nanotechnology, and plant sciences.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

Structural investigation of Fun168A unraveling the recognition mechanism of endo-1,3-fucanase towards sulfated fucan

BACKGROUND

Sulfated fucan has gained interest due to its various physiological activities. Endo-1,3-fucanases are valuable tools for investigating the structure and establishing structure-activity relationships of sulfated fucan. However, the substrate recognition mechanism of endo-1,3-fucanases towards sulfated fucan remains unclear, limiting the application of endo-1,3-fucanases in sulfated fucan research.

SCOPE AND APPROACH

This study presented the first crystal structure of endo-1,3-fucanase (Fun168A) and its complex with the tetrasaccharide product, utilizing X-ray diffraction techniques. The novel subsite specificity of Fun168A was identified through glycomics and nuclear magnetic resonance (NMR).

KEY FINDINGS AND CONCLUSIONS

The structure of Fun168A was determined at 1.92 Å. Residues D206 and E264 acted as the nucleophile and general acid/base, respectively. Notably, Fun168A strategically positioned a series of polar residues at the subsites ranging from -2 to +3, enabling interactions with the sulfate groups of sulfated fucan through salt bridges or hydrogen bonds. Based on the structure of Fun168A and its substrate recognition mechanisms, the novel subsite specificities at the -2 and +2 subsites of Fun168A were identified. Overall, this study provided insight into the structure and substrate recognition mechanism of endo-1,3-fucanase for the first time and offered a valuable tool for further research and development of sulfated fucan.

Pro-CRISPR PcrIIC1-associated Cas9 system for enhanced bacterial immunity

The CRISPR system is an adaptive immune system found in prokaryotes that defends host cells against the invasion of foreign DNA1. As part of the ongoing struggle between phages and the bacterial immune system, the CRISPR system has evolved into various types, each with distinct functionalities2. Type II Cas9 is the most extensively studied of these systems and has diverse subtypes. It remains uncertain whether members of this family can evolve additional mechanisms to counter viral invasions3,4. Here we identify 2,062 complete Cas9 loci, predict the structures of their associated proteins and reveal three structural growth trajectories for type II-C Cas9. We found that novel associated genes (NAGs) tended to be present within the loci of larger II-C Cas9s. Further investigation revealed that CbCas9 from Chryseobacterium species contains a novel β-REC2 domain, and forms a heterotetrameric complex with an NAG-encoded CRISPR-Cas-system-promoting (pro-CRISPR) protein of II-C Cas9 (PcrIIC1). The CbCas9-PcrIIC1 complex exhibits enhanced DNA binding and cleavage activity, broader compatibility for protospacer adjacent motif sequences, increased tolerance for mismatches and improved anti-phage immunity, compared with stand-alone CbCas9. Overall, our work sheds light on the diversity and 'growth evolutionary' trajectories of II-C Cas9 proteins at the structural level, and identifies many NAGs-such as PcrIIC1, which serves as a pro-CRISPR factor to enhance CRISPR-mediated immunity.

IGSF8 is an innate immune checkpoint and cancer immunotherapy target

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo. It is overexpressed and associated with low antigen presentation, low immune infiltration, and worse clinical outcomes in many tumors. An antibody that blocks IGSF8-NK receptor interaction enhances NK cell killing of malignant cells in vitro and upregulates antigen presentation, NK cell-mediated cytotoxicity, and T cell signaling in vivo. In syngeneic tumor models, anti-IGSF8 alone, or in combination with anti-PD1, inhibits tumor growth. Our results indicate that IGSF8 is an innate immune checkpoint that could be exploited as a therapeutic target.

Integrating cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution

The 3D architecture of RNAs governs their molecular interactions, chemical reactions, and biological functions. However, a large number of RNAs and their protein complexes remain poorly understood due to the limitations of conventional structural biology techniques in deciphering their complex structures and dynamic interactions. To address this limitation, we have benchmarked an integrated approach that combines cryogenic OrbiSIMS, a state-of-the-art solid-state mass spectrometry technique, with computational methods for modelling RNA structures at atomic resolution with enhanced precision. Furthermore, using 7SK RNP as a test case, we have successfully determined the full 3D structure of a native RNA in its apo, native and disease-remodelled states, which offers insights into the structural interactions and plasticity of the 7SK complex within these states. Overall, our study establishes cryo-OrbiSIMS as a valuable tool in the field of RNA structural biology as it enables the study of challenging, native RNA systems.

Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) frameshift stimulatory element (FSE) is necessary for programmed -1 ribosomal frameshifting (-1 PRF) and optimized viral efficacy. The FSE has an abundance of context-dependent alternate conformations, but two of the structures most crucial to -1 PRF are an attenuator hairpin and a three-stem H-type pseudoknot structure. A crystal structure of the pseudoknot alone features three RNA stems in a helically stacked linear structure, whereas a 6.9 Å cryo-EM structure including the upstream heptameric slippery site resulted in a bend between two stems. Our previous research alluded to an extended upstream multibranch loop that includes both the attenuator hairpin and the slippery site-a conformation not previously modeled. We aim to provide further context to the SARS-CoV-2 FSE via computational and medium resolution cryo-EM approaches, by presenting a 6.1 Å cryo-EM structure featuring a linear pseudoknot structure and a dynamic upstream multibranch loop.