Secondary Structure Prediction Research Articles

Using the RNAstructure Software Package to Predict Conserved RNA Structures.

The structures of many non-coding RNAs (ncRNA) are conserved by evolution to a greater extent than their sequences. By predicting the conserved structure of two or more homologous sequences, the accuracy of secondary structure prediction can be improved as compared to structure prediction for a single sequence. Here, we provide protocols for the use of four programs in the RNAstructure suite topredict conserved structures: Multilign, TurboFold, Dynalign, and PARTS. TurboFold iteratively aligns multiple homologous sequences and estimates the pairing probabilities for the conserved structure. Dynalign, PARTS, and Multilign are dynamic programming algorithms that simultaneously align sequences and identify the common secondary structure. Dynalign uses a pair of homologs and finds the lowest free energy common structure. PARTS uses a pair of homologs and estimates pairing probabilities from the base pairing probabilities estimated for each sequence. Multilign uses two or morehomologs and finds the lowest free energy common structure using multiple pairwise calculations with Dynalign. It scales linearly with the number of sequences. We outline the strengths of each program. These programs can be run through web servers, on the command line, or with graphical user interfaces. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Predicting a structure conserved in three or more sequences with the RNAstructure web server Basic Protocol 2: Predicting a structure conserved in two sequences with the RNAstructure web server Alternative Protocol 1: Predicting a structure conserved in multiple sequences in the RNAstructure graphical user interface Alternative Protocol 2: Predicting a structure conserved in two sequences with Dynalign in the RNAstructure graphical user interface Alternative Protocol 3: Running TurboFold on the command line.

ML-Based RNA Secondary Structure Prediction Methods: A Survey

Article ML-Based RNA Secondary Structure Prediction Methods: A Survey Qi Zhao 1, Jingjing Chen 1, Zheng Zhao 2, Qian Mao 3, Haoxuan Shi 1 and Xiaoya Fan 4,∗ 1 School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110000, China 2 School of Artificial Intelligence, Dalian Maritime University, Dalian 116000, China 3 Department of Food Science and Engineering, College of Light Industry, Liaoning University, Shenyang 110000, China 4 School of Software, Dalian University of Technology, Key Laboratory for Ubiquitous Network and Service Software, Dalian 116000, China ∗ Correspondence: xiaoyafan@dlut.edu.cn Received: 6 May 2024; Revised: 17 October 2024; Accepted: 22 October 2024; Published: 29 October 2024 Abstract: The secondary structure of noncoding RNAs (ncRNA) is significantly related to their functions, emphasizing the importance and value of identifying ncRNA secondary structure. Computational prediction methods have been widely used in this field. However, the performance of existing computational methods has plateaued in recent years despite various advancements. Fortunately, the emergence of machine learning, particularly deep learning, has brought new hope to this field. In this review, we present a comprehensive overview of machine learning-based methods for predicting RNA secondary structures, with a particular emphasis on deep learning approaches. Additionally, we discuss the current challenges and prospects in RNA secondary structure prediction.

ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure.

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research. We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage. To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures. Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches. This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.

Computational Analysis and Inhibition Study of Carbonic Anhydrase from Azadirachta indica Leaves

Introduction: Carbonic anhydrase (CA) has been an enzyme of great interest for its application in carbon dioxide sequestration. A total of 66 protein sequences of plant CAs were computationally analyzed and submitted to bioinformatics for motif and domain identification, multiple sequence alignment, and phylogenetic analysis. Method: The current work aims to extend knowledge among researchers in better understanding the structure of AZDI CA by analyzing its physicochemical properties, secondary structure prediction, 3D modeling of protein sequence, and its validation using a variety of conventional computational methods. Results: The accuracy of the predicted 3D structure checked by the Ramachandran plot generated by PROCHECK showed that for the CA protein sequence 93.1%, was observed in the most favored regions, VERIFY 3D 69.44% and ERRAT 98.09%. AZDI CA was docked with various anions and sulphonamide derivatives to study their inhibitory effect and calculate binding energy by using Autodock 4.2. Ethoxzolamide is found to be a better inhibitor of AZDI CA with binding energy -8.71 kCal/mol and KI value 0.0004 mM. Further MD simulation of the docked complex of ethoxzolamide with AZDI CA was done using SCHRODINGER DESMOND software. Conclusion: The outcomes of this research will have a significant influence on biochemistry, biotechnology, and potential applications of AZDI CA and similar plant CAs in determining potent inhibitors for drug targets in treating various diseases.

Wfold: A new method for predicting RNA secondary structure with deep learning

Precise estimations of RNA secondary structures have the potential to reveal the various roles that non-coding RNAs play in regulating cellular activity. However, the mainstay of traditional RNA secondary structure prediction methods relies on thermos-dynamic models via free energy minimization, a laborious process that requires a lot of prior knowledge. Here, RNA secondary structure prediction using Wfold, an end-to-end deep learning-based approach, is suggested. Wfold is trained directly on annotated data and base-pairing criteria. It makes use of an image-like representation of RNA sequences, which an enhanced U-net incorporated with a transformer encoder can process effectively. Wfold eventually increases the accuracy of RNA secondary structure prediction by combining the benefits of self-attention mechanism's mining of long-range information with U-net's ability to gather local information. We compare Wfold's performance using RNA datasets that are within and across families. When trained and evaluated on different RNA families, it achieves a similar performance as the traditional methods, but dramatically outperforms the state-of-the-art methods on within-family datasets. Moreover, Wfold can also reliably forecast pseudoknots. The findings imply that Wfold may be useful for improving sequence alignment, functional annotations, and RNA structure modeling.

Identification of a cooperative effect between amino acids 169 and 174 in the rotavirus NSP4 double-layered particle-binding domain.

Segmented RNA viruses are capable of exchanging genome segments via reassortment as a means of immune evasion and to maintain viral fitness. Reassortments of single-genome segments are common among group A rotaviruses. Multiple instances of co-reassortment of two genome segments, GS6(VP6) and GS10(NSP4), have been documented in surveillance. Specifically, a division between NSP4 genotypes has been observed in the NSP4 double-layered particle (DLP)-binding domain. A previously hypothesized mechanism for this co-reassortment has been suggested to be the interaction between VP6 and NSP4 during DLP transport from viroplasms for particle maturation. In this study, we used sequence analysis, RNA secondary structure prediction, molecular dynamics and reverse genetics to form a hypothesis regarding the role of the NSP4 DLP-binding domain. Sequence analysis showed that the polarity of NSP4 DLP-binding domain amino acids 169 and 174 is clearly divided between E1 and E2 NSP4 genotypes. Viruses with E1 NSP4s had 169A/I or 169S/T with 174S. E2 NSP4s had 169R/K and 174A. RNA secondary structure prediction showed that mutation in both 545 (aa169) and 561 (aa174) causes global structure remodelling. Molecular dynamics showed that the NSP4/VP6 interaction stability is increased by mutating both aa positions 169 and 174. Using reverse genetics, we showed that an R169I mutation alone does not prevent rescue. Conversely, 174A to 174S prevented rescue, and rescue could be returned by combining 174S with 169I. When compared to rSA11 NSP4-wt, both rSA11 NSP4-R169I and rSA11 NSP4-R169I/A174S had a negligible but significant reduction in titre at specific time points. This study suggests that amino acid 174 of NSP4 may be essential in maintaining the VP6/NSP4 interaction required for DLP transport. Our results suggest that maintenance of specific polarities of amino acids at positions 169 and 174 may be required for the fitness of rotavirus field strains.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Impact of Multi-Factor Features on Protein Secondary Structure Prediction.

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid sequences, position-specific score matrices (PSSM), amino acid properties, and secondary structure trend factors, to improve prediction accuracy is an important technical route for it. However, a comprehensive evaluation of the impact of these factor features in secondary structure prediction is lacking in the current work. This study quantitatively analyzes the impact of several major factors on secondary structure prediction models using a more explanatory four-class machine learning approach. The applicability of each factor in the different types of methods, the extent to which the different methods work on each factor, and the evaluation of the effect of multi-factor combinations are explored in detail. Through experiments and analyses, it was found that PSSM performs best in methods with strong high-dimensional features and complex feature extraction capabilities, while amino acid sequences, although performing poorly overall, perform relatively well in methods with strong linear processing capabilities. Also, the combination of amino acid properties and trend factors significantly improved the prediction performance. This study provides empirical evidence for future researchers to optimize multi-factor feature combinations and apply them to protein secondary structure prediction models, which is beneficial in further optimizing the use of these factors to enhance the performance of protein secondary structure prediction models.

Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements.

1H, 13C and 15N backbone resonance assignment of the calcium-activated EndoU endoribonuclease.

The catalytic domain of the calcium-dependent endoribonuclease EndoU from Homo sapiens was expressed in E. coli with 13C and 15N labeling. A nearly complete assignment of backbone 1H, 15N, and 13C resonances was obtained, as well as a secondary structure prediction based on the assigned chemical shifts. The predicted secondary structures were almost identical to the published crystal structure of calcium-activated EndoU. This is the first NMR study of an eukaryotic member of the EndoU-like superfamily of ribonucleases.

Design and computational analysis of a novel Leptulipin-p28 fusion protein as a multitarget anticancer therapy in breast cancer.

The search for novel therapeutic agents to treat breast cancer has compelled the development of fusion proteins that synergize the functional benefits of different bioactive peptides. Leptulipin, derived from scorpion venom, exhibits antitumor properties. On the other hand, p28, a peptide from the bacterial protein azurin, enhances cell penetration. The current study investigated the design and computational evaluation of a Leptulipin-p28 fusion protein for breast cancer treatment. The amino acid sequences of Leptulipin and p28 were joined via a rigid linker to maintain structural and functional integrity. Secondary and tertiary structure predictions were performed using online servers of GOR-IV and I-TASSER. Physicochemical properties and solubility were analyzed using ProtParam and Protein-Sol. Validation and quality assessment of the fusion protein were confirmed through Rampage and ERRAT2. Finally, the fusion protein was docked with 2 receptors (VEGFR and Cadherin) and docked complexes were simulated on GROMACS. The Leptulipin-p28 fusion protein exhibited a stable structure exhibiting a high quality score of 92 on ERRAT and Ramachandran plot analysis highlighting 76.3% of residues in the favorable region. Docking studies with VEGFR and Cadherin receptors followed by 100ns simulations on GROMACS showed stable complex formation. Molecular dynamics simulations confirmed the stability and robust interaction of the fusion protein-receptor complexes over time. The computational analysis indicates that the Leptulipin-p28 fusion protein holds promise as a multitarget therapeutic agent in breast cancer. The current findings warrant further investigation through in vitro and in vivo studies to validate the current outcomes.

Protein Secondary Structure Prediction Using Convolutional Bidirectional GRU

In this paper, a protein secondary structure prediction method based on convolutional bidirectional GRU Model (CBi-GRU model) is adopted, which combines the advantages of sliding window in extracting local features of data. The use of CNN and Bi-GRU in the construction of the model improves the feature expression and data utilization, and improves the performance of the model. Protein data from FoxChase Institute were used, and high quality, complete and representative CullPDB dataset, CB513, CASP10 and CASP11 datasets were selected to train, test and validate the model. The results show that the proposed method achieves good prediction performance on CASP10 and CASP11 datasets, and the prediction accuracy of Q8 is 76.2% and 76.4%, respectively. Compared with RaptorX-SS, DeepCNF, CGAN-PSSP and other methods, the Q8 evaluation indicators are improved. Compared with the latest research data, our Q8 prediction accuracy is improved by 2% and 5.1%, which shows the effectiveness and superiority of the proposed model.

Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Porcine epidemic diarrhea virus (PEDV) is a serious disease that poses a significant threat to the pig industry. This study focused on analyzing the Spike protein of PEDV, which harbors crucial antigenic determinants, in identifying dominant epitopes. Immunoinformatics tools were used to screen for B-cell, CD4+ and CD8+ predominance epitopes. These epitopes were then connected to the N-terminal of ferritin to form a self-assembled nanoparticle vaccine. Various physical and chemical properties of the candidate vaccine were analyzed, including secondary structure prediction, tertiary structure modeling, molecular docking, immune response simulation and computer cloning. The results demonstrated that the candidate vaccine was antigenic, soluble, stable, non-allergic, and formed a stable complex with the target receptor TLR-3. Immune simulation analysis showed that the candidate vaccine effectively stimulated both cellular and humoral reactions, leading to increased related cytokines production. Furthermore, efficient and stable expression of the candidate vaccine was achieved through reverse translation in the Escherichia coli K12 expression system following codon optimization and in silico cloning. The developed nanoparticle candidate vaccine in this study holds promise as an effective PEDV vaccine candidate, offering a new approach for the research, development and improvement of vaccines targeting porcine enteric diarrhea coronavirus.

Uncovering chikungunya virus-encoded miRNAs and host-specific targeted genes associated with antiviral immune responses: an integrated bioinformatics approach

Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the genus Alphavirus and is responsible for causing Chikungunya fever, a type of arboviral fever. Despite extensive research, the pathogenic mechanism of CHIKV within host cells remains unclear. In this study, an in-silico approach was used to predict that CHIKV produces micro-RNAs that target host-specific genes associated with host cellular regulatory pathways. Putative micro-RNAs of CHIKV were predicted using the miRNAFold and Vmir RNA structure web servers, and secondary structure prediction was performed using RNAfold. Host-specific target genes were then predicted, and hub genes were identified using CytoHubba and module selection through MCODE. Functional annotations of hub genes revealed their association with various pathways, including osteoclast differentiation, neuroactive ligand-receptor interaction, and mRNA surveillance. We used the freely available dataset GSE49985 to determine the level of expression of host-specific target genes and found that two genes, F-box and leucine-rich repeat protein 16 (FBXL16) and retinoic acid receptor alpha (RARA), were down-regulated, while four genes, RNA binding protein with serine-rich domain 1 (RNPS1), RNA helicase and ATPase (UPF1), neuropeptide S receptor 1 (NPSR1), and vasoactive intestinal peptide receptor 1 (VIPR1), were up-regulated. These findings provide insight into novel miRNAs and hub genes associated with CHIKV infection and suggest potential targets for therapeutic intervention. Further experimental validation of these targets could lead to the development of effective treatments for CHIKV-mediated diseases.

VHDvDB 2.0: Database and Group Comparison Server for Hepatitis Delta Virus.

The hepatitis delta virus (HDV) is a unique pathogen with significant global health implications, affecting individuals who are coinfected with the hepatitis B virus (HBV). HDV infection has profound clinical consequences, manifesting either as coinfection with HBV, resulting in acute hepatitis and potential liver failure, or as superinfection in chronic HBV cases, substantially increasing the risk of cirrhosis and hepatocellular carcinoma. Given the complex dynamics of HDV infection and the urgent need for advanced research tools, this article introduces vHDvDB 2.0, a comprehensive HDV full-length sequence database. This innovative platform integrates data preprocessing, secondary structure prediction, and epidemiological research tools. The primary goal of vHDvDB 2.0 is to consolidate HDV sequence data into a user-friendly repository, thereby facilitating access for researchers and enhancing the broader scientific understanding of HDV. The significance of this database lies in its potential to streamline HDV research by providing a centralized resource for analyzing viral sequences and exploring genotype-specific characteristics. It will also enable more in-depth research within the HDV sequence domains.

Protein Secondary Structure Prediction based on Multi-scale Convolution and Transformer

Protein Secondary Structure Prediction based on Multi-scale Convolution and Transformer

UPC2 mutations and development of azole resistance in Candida albicans hospital isolates from Lebanon

ObjectivesThis study evaluated the role of Upc2 in the development of azole resistance in Candida albicans isolates from Lebanese hospitalized patients and determined a correlation between resistance and virulence. MethodsThe UPC2 gene which codes for an ergosterol biosynthesis regulator was sequenced and analysed in two azole-resistant and one azole-susceptible C. albicans isolates. An amino acid substitution screening was carried out on Upc2 with a focus on its ligand binding domain (LBD) known to interact with ergosterol. Then, Upc2 protein secondary structure prediction and homology modelling were conducted, followed by total plasma membrane ergosterol and cell wall chitin quantifications. For virulence, mouse models of systemic infection were generated and an agar adhesion and invasion test was performed. ResultsAzole-resistant isolates harboured novel amino acid substitutions in the LBD of Upc2 and changes in protein secondary structures were observed. In addition, these isolates exhibited a significant increase in plasma membrane ergosterol content. Resistance and virulence were inversely correlated while increased cell wall chitin concentration does not seem to be linked to resistance since even though we observed an increase in chitin concentration, it was not statistically significant. ConclusionsThe azole-resistant C. albicans isolates harboured novel amino acid substitutions in the LBD of Upc2 which are speculated to induce an increase in plasma membrane ergosterol content, preventing the binding of azoles to their target, resulting in resistance.

CSSP-2.0: A refined consensus method for accurate protein secondary structure prediction

Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/

Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.

Ecological and evolutionary dynamics of CRISPR-Cas systems in Clostridium botulinum: Insights from genome mining and comparative analysis

Understanding the prevalence and distribution of CRISPR-Cas systems across different strains can illuminate the ecological and evolutionary dynamics of Clostridium botulinum populations. In this study, we conducted genome mining to characterize the CRISPR-Cas systems of C. botulinum strains. Our analysis involved retrieving complete genome sequences of these strains and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Subsequently, we performed an analysis of homology in spacer sequences from identified CRISPR arrays to investigate and characterize the range of targeted phages and plasmids. Additionally, we investigated the evolutionary trajectory of C. botulinum strains under selective pressures from foreign invasive DNA. Our findings revealed that 306 strains possessed complete CRISPR-Cas structures, comprising 58% of the studied C. botulinum strains. Secondary structure prediction of consensus repeats indicated that subtype II-C, with longer stems compared to subtypes ID and IB, tended to form more stable RNA secondary structures. Moreover, protospacer motif analysis demonstrated that strains with subtype IB CRISPR-Cas systems exhibited 5′-CGG-3′, 5′-CC-3′, and 5′-CAT-3′ motifs in the 3′ flanking regions of protospacers. The diversity observed in CRISPR-Cas systems indicated their classification into subtypes IB, ID, II-C, III-B, and III-D. Furthermore, our results showed that systems with subtype ID and III-D frequently harbored similar spacer patterns. Moreover, analysis of spacer sequences homology with phage and prophage genomes highlighted the specific activities exhibited by subtype IB and III-B against phages and plasmids, providing valuable insights into the functional specialization within these systems.