Secondary Structure Prediction Research Articles

1H, 15N and 13C resonance backbone and side-chain assignments and secondary structure determination of the BRCT domain of Mtb LigA.

The BRCA1 carboxyl-terminal (BRCT) domain, an evolutionarily conserved structural motif, is ubiquitous in a multitude of proteins spanning prokaryotic and eukaryotic organisms. In Mycobacterium tuberculosis (Mtb), BRCT domain plays a pivotal role in the catalytic activity of the NAD+-dependent DNA ligase (LigA). LigA is pivotal in DNA replication, catalyzing the formation of phosphodiester bonds in Okazaki fragments and repairing single-strand breaks in damaged DNA, essential for the survival of Mtb. Structural and functional aspects of LigA unveil its character as a highly modular protein, undergoing substantial conformational changes during its catalytic cycle. Although the BRCT domain of Mtb LigA plays an essential role in DNA binding and protein-protein interactions, the precise mechanism of action remains poorly understood. Unravelling the structure of the BRCT domain holds the promise of advancing our understanding of this pivotal domain. Additionally, it will facilitate further exploration of the protein-protein interactions and enhance our understanding of inter domain interactions within LigA, specifically between BRCT and the Adenylation domain. In this study, we demonstrate the overexpression of the BRCT domain of Mtb LigA and conduct its analysis using solution NMR spectroscopy, revealing a well-folded structure and we present the nearly complete chemical shift assignments of both backbone and sidechains. In addition, a secondary structure prediction by TALOS N predicts BRCT consisting of 3 α-helices and 4 β-sheets, closely resembling the typical structural topology of most BRCT domains.

In Silico approach: Design an Optimized shRNA against RUNX1 Gene to Target HIV.

Human Immunodeficiency Virus (HIV) is a retrovirus with single-stranded RNA that leads to the challenging disease of acquired immunodeficiency syndrome (AIDS). Combination antiretroviral therapy (cART) can prevent the progression of the disease, but it is not capable of long-term HIV elimination. One of the significant obstacles to treating HIV-1-infected individuals is the creation of latent cell reservoirs early in the infection. Gene-based therapies that utilize RNA interference (RNAi) to silence host or viral gene expression are considered promising therapeutic approaches. It has been demonstrated that RUNX1, a T-cell-specific transcription factor, may significantly affect HIV replication and infection. According to accumulating evidence on the role of interfering RNA techniques in inhibiting gene expression and considering the role of RUNX1 in the replication of HIV-1. In this study, we aim to design shRNAs against RUNX1 that can target the replication of HIV-1. </P> Methods: Several computational methods, including target alignment, similarity search, and secondary structure prediction, have been employed in the design of shRNA against RUNX1. </P> Results: Seven shRNA molecules with the highest efficiency were designed and validated using computational methods to silence the RUNX1 gene. </P> Conclusions: In the present study, we designed shRNA against RUNX1, which can target latent cells infected with HIV. Suppression of RUNX1 by shRNA reactivates HIV in the latent cells and subsequently potentiates the immune response toward identifying accurate virus-infected cells. This process may lead to an effective and efficient reduction of the volume of cell reservoirs infected with HIV.

Characterization of the First Secreted Sorting Nexin Identified in the Leishmania Protists.

Proteins of the sorting nexin (SNX) family present a modular structural architecture with a phox homology (PX) phosphoinositide (PI)-binding domain and additional PX structural domains, conferring to them a wide variety of vital eukaryotic cell's functions, from signal transduction to membrane deformation and cargo binding. Although SNXs are well studied in human and yeasts, they are poorly investigated in protists. Herein, is presented the characterization of the first SNX identified in Leishmania protozoan parasites encoded by the LdBPK_352470 gene. In silico secondary and tertiary structure prediction revealed a PX domain on the N-terminal half and a Bin/amphiphysin/Rvs (BAR) domain on the C-terminal half of this protein, with these features classifying it in the SNX-BAR subfamily of SNXs. We named the LdBPK_352470.1 gene product LdSNXi, as it is the first SNX identified in Leishmania (L.) donovani. Its expression was confirmed in L. donovani promastigotes under different cell cycle phases, and it was shown to be secreted in the extracellular medium. Using an in vitro lipid binding assay, it was demonstrated that recombinant (r) LdSNXi (rGST-LdSNXi) tagged with glutathione-S-transferase (GST) binds to the PtdIns3P and PtdIns4P PIs. Using a specific a-LdSNXi antibody and immunofluorescence confocal microscopy, the intracellular localization of endogenous LdSNXi was analyzed in L. donovani promastigotes and axenic amastigotes. Additionally, rLdSNXi tagged with enhanced green fluorescent protein (rLdSNXi-EGFP) was heterologously expressed in transfected HeLa cells and its localization was examined. All observed localizations suggest functions compatible with the postulated SNX identity of LdSNXi. Sequence, structure, and evolutionary analysis revealed high homology between LdSNXi and the human SNX2, while the investigation of protein-protein interactions based on STRING (v.11.5) predicted putative molecular partners of LdSNXi in Leishmania.

6‐Strand to Stable 10/12 Helix Conformational Switch by Incorporating Flexible β‐hGly in the Homooligomers of Camphor Derived β‐Amino Acid: NMR and X‐Ray Crystallographic Evidence

AbstractRational design of unnatural amino acid building blocks capable of stabilizing predictable secondary structures similar to protein fragments is pivotal for foldamer chemistry/catalysis. Here, we introduce novel β‐amino acid building blocks: [1S,2R,4R]exoCDA and [1S,2S,4R]endoCDA, derived from the abundantly available R(+)‐camphor, which is traditionally known for its medicinal value. Further, we demonstrate that the homooligomers of exoCDA adopt 6‐strand conformation, which switches to a robust 10/12‐helix simply by inserting flexible β‐hGly spacer at alternate positions (1 : 1 β‐hGly/exoCDA heterooligomers), as evident by DFT‐calculations, solution‐state NMR spectroscopy and X‐ray crystallography. To the best of our knowledge, this is the first example of crystalline‐state structure of left‐handed 10/12‐mixed helix, that is free from the conventional approach of employing β‐amino acids of either alternate chirality or alternate β2/β3 substitutions, to access the 10/12‐helix. The results also show that the homooligomers of heterochiral exoCDA don′t adopt helical fold, instead exhibit banana‐shaped strands, whereas the homodimers of the other diastereomer endoCDA, nucleate 8‐membered turns. Furthermore, the homo‐exoCDA and hetero‐[β‐hGly‐exoCDA] oligomers are found to exhibit self‐association properties with distinct morphological features. Overall, the results offer new possibilties of constructing discrete stable secondary and tertiary structures based on CDAs, which can accommodate flexible residues with desired side‐chain substitutions.

6-Strand to Stable 10/12 Helix Conformational Switch by Incorporating Flexible β-hGly in the Homooligomers of Camphor Derived β-Amino Acid: NMR and X-Ray Crystallographic Evidence.

Rational design of unnatural amino acid building blocks capable of stabilizing predictable secondary structures similar to protein fragments is pivotal for foldamer chemistry/catalysis. Here, we introduce novel β-amino acid building blocks: [1S,2R,4R]exoCDA and [1S,2S,4R]endoCDA, derived from the abundantly available R(+)-camphor, which is traditionally known for its medicinal value. Further, we demonstrate that the homooligomers of exoCDA adopt 6-strand conformation, which switches to a robust 10/12-helix simply by inserting flexible β-hGly spacer at alternate positions (1 : 1 β-hGly/exoCDA heterooligomers), as evident by DFT-calculations, solution-state NMR spectroscopy and X-ray crystallography. To the best of our knowledge, this is the first example of crystalline-state structure of left-handed 10/12-mixed helix, that is free from the conventional approach of employing β-amino acids of either alternate chirality or alternate β2/β3 substitutions, to access the 10/12-helix. The results also show that the homooligomers of heterochiral exoCDA don't adopt helical fold, instead exhibit banana-shaped strands, whereas the homodimers of the other diastereomer endoCDA, nucleate 8-membered turns. Furthermore, the homo-exoCDA and hetero-[β-hGly-exoCDA] oligomers are found to exhibit self-association properties with distinct morphological features. Overall, the results offer new possibilties of constructing discrete stable secondary and tertiary structures based on CDAs, which can accommodate flexible residues with desired side-chain substitutions.

Designing of multi-Epitope vaccine against spike glycoprotein of SARS-CoV-2 using immunoinformatics approach

BackgroundCOVID-19 caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome) has created an alarming situation worldwide. The surface (S) glycoprotein of SARS-CoV-2, encoded by the genome of SARS-CoV-2, plays important role in attachment, fusion as well as entry into the host cell. The spike glycoprotein plays important role in not only infection but pathogenesis and adaptive immunity, the S glycoprotein is considered as the main target for the development of effective and durable vaccine against SARS-CoV-2.Present study aims to compare the SARS-CoV-2 spike sequence obtained from first Wuhan virus with those of Asian SARS-CoV-2 isolates. ResultA total of 1165 mutations from 657 sequences of Asia submitted in the month of November 2020 to February 2021 were detected. Further, secondary structure prediction followed by protein modeling analysis was performed which revealed, these mutations, considerably altered the stability of Spike protein. Additionally, Physiochemical properties, toxicity, allergenicity and stability of spike glycoprotein were estimated to demonstrate the specificity of the epitope candidates. Further, we identified a total of 34B-cell and 10 T-cell immune epitopes. Among the predicted epitopes, 26B-cell and 9 T-cell epitopes showed non-allergenic, non-toxic and highly antigenic properties. ConclusionTaken together, our study showed spike of SARS-CoV-2 can be a potentially good candidate for the development of vaccine to curb SARS-CoV-2 infections.

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

1H, 13C and 15N backbone and side-chain resonance assignments of the human oncogenic protein NCYM.

NCYM is a cis-antisense gene of MYCN oncogene and encodes an oncogenic protein that stabilizes MYCN via inhibition of GSK3b. High NCYM expression levels are associated with poor clinical outcomes in human neuroblastomas, and NCYM overexpression promotes distant metastasis in animal models of neuroblastoma. Using vacuum-ultraviolet circular dichroism and small-angle X-ray scattering, we previously showed that NCYM has high flexibility with partially folded structures; however, further structural characterization is required for the design of anti-cancer agents targeting NCYM. Here we report the 1H, 15N and 13C nuclear magnetic resonance assignments of NCYM. Secondary structure prediction using Secondary Chemical Shifts and TALOS-N analysis demonstrates that the structure of NCYM is essentially disordered, even though residues in the central region of the peptide clearly present a propensity to adopt a dynamic helical structure. This preliminary study provides foundations for further analysis of interaction between NCYM and potential partners.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Using protein language models for protein interaction hot spot prediction with limited data.

Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots. Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting. This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.

Characterization of an Aptamer Targeting Neu5Gc, as an Endogenous Pathogenic Factor Derived from Red Meat.

N-glycolylneuraminic acid (Neu5Gc), a sialic acid predominantly found in the non-neurohumoral fluids of hind-mouthed animals, is incapable of synthesizing Neu5Gc due to a deletion in the CMAH exon of the gene encoding human CMP-Neu5Gc hydroxylase. But consumption of animal-derived foods that contain Neu5Gc, such as red meat, can instigate an immune response in humans, as Neu5Gc is recognized as a foreign substance by the human immune system. This recognition leads to the production of anti-Neu5Gc antibodies, subsequently resulting in chronic inflammation. When Neu5Gc is consumed excessively or frequently, it may contribute to the development of heart disease and cancer. This makes Neu5Gc, an endogenous pathogenic factor derived from red meat, a new hot topic in red meat safety research. In this study, aptamers obtained by the magnetic bead SELEX technique were subjected to homology and secondary structure prediction analysis as well as affinity determination. The result indicated that the aptamer 2B.N2A9 exhibited a robust binding affinity, with an affinity constant (Ka) of 1.87 × 108 L/mol. This aptamer demonstrated optimal binding specificity within a pH range of 5.4 to 7.4. Molecular docking analysis further revealed that aptamer 2B.N2A9 formed stable binding interactions with the target Neu5Gc at specific sites, namely G-14, C-15, G-13, G-58, G-60, and C-59. An Enzyme-Linked Oligonucleotide Sorbent Assay (ELOSA) methodology was established to detect the endogenous pathogenic factor Neu5Gc present in red meat. This method demonstrated a limit of detection (LOD) of 0.71 ng/mL, along with an average recovery rate of 92.23%. The aptamer obtained in this study exhibited favorable binding properties to Neu5Gc. The assay was relatively convenient and demonstrated good sensitivity. Further investigation into the distribution of Neu5Gc in various red meats is of public health significance and scientific potential. A practical detection method should be provided to guide red meat diets and ensure the nutrition and safety of meat products.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Structural Impact of the Interaction of the Influenza A Virus Nucleoprotein with Genomic RNA Segments.

Influenza A viruses (IAVs) possess a segmented genome consisting of eight viral RNAs (vRNAs) associated with multiple copies of viral nucleoprotein (NP) and a viral polymerase complex. Despite the crucial role of RNA structure in IAV replication, the impact of NP binding on vRNA structure is not well understood. In this study, we employed SHAPE chemical probing to compare the structure of NS and M vRNAs of WSN IAV in various states: before the addition of NP, in complex with NP, and after the removal of NP. Comparison of the RNA structures before the addition of NP and after its removal reveals that NP, while introducing limited changes, remodels local structures in both vRNAs and long-range interactions in the NS vRNA, suggesting a potentially biologically relevant RNA chaperone activity. In contrast, NP significantly alters the structure of vRNAs in vRNA/NP complexes, though incorporating experimental data into RNA secondary structure prediction proved challenging. Finally, our results suggest that NP not only binds single-stranded RNA but also helices with interruptions, such as bulges or small internal loops, with a preference for G-poor and C/U-rich regions.

Sequence analysis and secondary structure prediction of autosomal STR alleles using next generation sequencing (NGS) data

The inception of NGS technology in forensic DNA analysis explores the sequence-based alleles in STR markers. This allows the in-depth analysis of observed STR allelic sequences to understand their nature, stability, inheritance, and possible generation of artifacts during their analysis. In the current study, 100 allelic sequences at 20 STR markers generated using the NGS technique were analyzed for their sequence topographies and prediction of secondary structures. The G + C content of the alleles observed in 20 STR markers used in this study varied from 58.65 ± 1.367% (D2S1338) to 7.62 ± 0.844% (D12ATA63). The average exact mass of one stand and for the opposite stand mass was found to be highest in FGA (25,963.248 ± 1623.271; 27,501.720 ± 1712.691), whereas, D4S2408 generated the lowest exact mass of one stand and for the opposite stand mass (11,136.354 ± 1521.757; 11,582.021 ± 1585.486). As expected, none of the STR markers showed the presence of open reading frames, Codons, and CRISPR sequences. Three STR markers viz. D2S1338, TH01, and D5S2800 showed the presence of restriction sites for Cac8I, TspGWI, TspDTI, AccI, and Hpy8I enzymes. Phylogenetic analysis reveals the close association of alleles between the D12ATA63 and D19S433 markers. Stable pseudoknots were predicted at alleles of D2S1338 showing an average energy of −0.76 with the highest number of nucleotides present in the pseudoknots i.e., 21.33, suggesting this marker is more prone to generate amplification artifacts.

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^3)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

Prediction of RNA Secondary Structure Using Butterfly Optimization Algorithm

AbstractRibonucleic acid (RNA) structure is vital to its ability to function within the cell. The ability to predict RNA structure is essential to implementing new medications and understanding genetic illnesses. It is also important in synthetic and computational biology. All these functions are directly related to its secondary structure. Also prediction of RNA secondary structure process is the most significant step to determining the tertiary structure of RNA. On account of this, prediction of secondary structure of RNA is the crying topic in bioinformatics. In this research, we present the swarm-based metaheuristic Butterfly Optimization Algorithm (BOA) method for predicting the secondary structure of RNA. The main feather of the BOA is that it can conduct both local and global search simultaneously. According to the problem perspective, we have redesigned the operators of BOA to perform global and local search operations in different ways. We have followed a thermodynamic model for the selection of the stable secondary structure with minimum Gibbs free energy. Predicting the minimum free energy value we also developed an “Optimize” function to search the new optimize structure. This function increases the prediction efficiency, creating new stable structure and also decreases the time complexity of global searching procedure. We have used a public dataset to perform the prediction operation. To accuse our prediction efficiency, we have compared our outcomes to existing popular algorithms. The result shows that the proposed approach can predict secondary RNA structure better than other state-of-the-art algorithms.

NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m6A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

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

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.

Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in Protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%. Communicated by Ramaswamy H. Sarma

Limits of experimental evidence in RNA secondary structure prediction.

Limits of experimental evidence in RNA secondary structure prediction.