RaptorX-Property: a web server for protein structure property prediction

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 Q 3 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.

Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting

In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.

Novel requirements for HAP2/GCS1-mediated gamete fusion in Tetrahymena

The ancestral gamete fusion protein, HAP2/GCS1, plays an essential role in fertilization in a broad range of taxa. To identify factors that may regulate HAP2/GCS1 activity, we screened mutants of the ciliate Tetrahymena thermophila for behaviors that mimic Δhap2/gcs1 knockout phenotypes in this species. Using this approach, we identified two new genes, GFU1 and GFU2, whose products are necessary for membrane pore formation following mating type recognition and adherence. GFU2 is predicted to be a single-pass transmembrane protein, while GFU1, though lacking obvious transmembrane domains, has the potential to interact directly with membrane phospholipids in the cytoplasm. Like Tetrahymena HAP2/GCS1, expression of GFU1 is required in both cells of a mating pair for efficient fusion to occur. To explain these bilateral requirements, we propose a model that invokes cooperativity between the fusion machinery on apposed membranes of mating cells and accounts for successful fertilization in Tetrahymena's multiple mating type system.

Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses

BACKGROUND

Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.

RESULTS

Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.

CONCLUSIONS

This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

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.

Structural and functional insights of starch processing α-amylase from hyperthermophilic archaeon Pyrococcusabyssi

The genomic screening of hyper-thermophilic Pyrococcus abyssi showed uncharacterized novel α-amylase sequences. Homology modelling analysis revealed that the α-amylase from P. abyssi consists of an N-terminal GH57 catalytic domain, α-amylase central, and C-terminal domain. Current studies emphasize in-silico structural and functional analysis, recombinant expression, characterization, structural studies through CD spectroscopy, and ligand binding studies of the novel α-amylase from P. abyssi. The soluble expression of PaAFG was observed in the E. coli Rosetta™ (DE3) pLysS strain upon incubation overnight at 18 °C in an orbital shaker. The optimum temperature and pH of the PaAFG were observed at 90 °C in 50 mM phosphate buffer pH 6. The Km value for PaAFG against wheat starch was determined as 0.20 ± 0.053 mg while the corresponding Vmax value was 25.00 ± 0.67 μmol min-1 mg-1 in the presence of 2 mM CaCl2 and 12.5 % glycerol. The temperature ramping experiments through CD spectroscopy reveal no significant change in the secondary structures and positive and negative ellipticities of the CD spectra showing the proper folding and optimal temperature of PaAFG protein. The RMSD and RMSF of the PaAFG enzyme determined through molecular dynamic simulation show the significant protein's stability and mobility. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various industrial applications.

Investigating the effect of SUMO fusion on solubility and stability of amylase-catalytic domain from Pyrococcus abyssi

Alpha amylase belonging to starch hydrolyzing enzymes has significant contributions to different industrial processes. The enzyme production through recombinant DNA technology faces certain challenges related to their expression, solubility and purification, which can be overcome through fusion tags. This study explored the influence of SUMO, a protein tag reported to enhance the solubility and stability of target proteins when fused to the N-terminal of the catalytic domain of amylase from Pyrococcus abyssi (PaAD). The insoluble expression of PaAD in E. coli was overcome when the enzyme was expressed in a fusion state (S-PaAD) and culture was cultivated at 18 °C. Moreover, the activity of S-PaAD increased by 1.5-fold as compared to that of PaAD. The ligand binding and enzyme activity assays against different substrates demonstrated that it was more active against 1 % glycogen and amylopectin. The analysis of the hydrolysates through HPLC demonstrated that the enzyme activity is mainly amylolytic, producing longer oligosaccharides as the major end product. The secondary structure analyses by temperature ramping in CD spectroscopy and MD simulation demonstrated the enzymes in the free, as well as fusion state, were stable at 90 °C. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various foods, feed, textiles, detergents, pharmaceuticals, and many industrial applications.

Prediction of drug-target binding affinity based on deep learning models

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

Metabolic Investigation and Auxiliary Enzyme Modelization of the Pyrrocidine Pathway Allow Rationalization of Paracyclophane-Decahydrofluorene Formation

Fungal paracyclophane-decahydrofluorene-containing natural products are complex polycyclic metabolites derived from similar hybrid PKS-NRPS pathways. Herein we studied the biosynthesis of pyrrocidines, one representative of this family, by gene inactivation in the producer Sarocladium zeae coupled to thorough metabolic analysis and molecular modeling of key enzymes. We characterized nine pyrrocidines and analogues as well as in mutants a variety of accumulating metabolites with new structures including rare cis-decalin, cytochalasan, and fused 6/15/5 macrocycles. This diversity highlights the extraordinary plasticity of the pyrrocidine biosynthetic gene cluster. From accumulating metabolites, we delineated the scenario of pyrrocidine biosynthesis. The ring A of the decahydrofluorene is installed by PrcB, a membrane-bound cyclizing isomerase, on a PKS-NRPS-derived pyrrolidone precursor. Docking experiments in PrcB allowed us to characterize the active site suggesting a mechanism triggered by arginine-mediated deprotonation at the terminal methyl of the substrate. Next, two integral membrane proteins, PrcD and PrcE, each predicted as a four-helix bundle, perform hydroxylation of the pyrrolidone ring and paracyclophane formation, respectively. Modelization of PrcE highlights a topological homology with vitamin K oxido-reductase and the presence of a disulfide bond. Our results suggest a previously unsuspected coupling mechanism via a transient loss of aromaticity of tyrosine residue to form the strained paracyclophane motif. Finally, the lipocalin-like protein PrcX drives the exo-cycloaddition yielding ring B and C of the decahydrofluorene to afford pyrrocidine A, which is transformed by a reductase PrcI to form pyrrocidine B. These insights will greatly facilitate the microbial production of pyrrocidine analogues by synthetic biology.

Plasmodium falciparum Alba6 exhibits DNase activity and participates in stress response

Alba domain proteins, owing to their functional plasticity, play a significant role in organisms. Here, we report an intrinsic DNase activity of PfAlba6 from Plasmodium falciparum, an etiological agent responsible for human malignant malaria. We identified that tyrosine28 plays a critical role in the Mg2+ driven 5'-3' DNase activity of PfAlba6. PfAlba6 cleaves both dsDNA as well as ssDNA. We also characterized PfAlba6-DNA interaction and observed concentration-dependent oligomerization in the presence of DNA, which is evident from size exclusion chromatography and single molecule AFM-imaging. PfAlba6 mRNA expression level is up-regulated several folds following heat stress and treatment with artemisinin, indicating a possible role in stress response. PfAlba6 has no human orthologs and is expressed in all intra-erythrocytic stages; thus, this protein can potentially be a new anti-malarial drug target.

Graphical models for identifying pore-forming proteins

Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology-based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample-efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi-Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)-a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome-wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.

Peptide based vaccine designing against endemic causing mammarenavirus using reverse vaccinology approach

The rodent-borne Arenavirus in humans has led to the emergence of regional endemic situations and has deeply emerged into pandemic-causing viruses. Arenavirus have a bisegmented ambisense RNA that produces four proteins: glycoprotein, nucleocapsid, RdRp and Z protein. The peptide-based vaccine targets the glycoprotein of the virus encountered by the immune system. Screening of B-Cell and T-Cell epitopes was done based on their immunological properties like antigenicity, allergenicity, toxicity and anti-inflammatory properties were performed. Selected epitopes were then clustered and epitopes were stitched using linker sequences. The immunological and physico-chemical properties of the vaccine construct was checked and modelled structure was validated by a 2-step MD simulation. The thermostability of the vaccine was checked followed by the immune simulation to test the immunogenicity of the vaccine upon introduction into the body over the course of the next 100 days and codon optimization was performed. Finally a 443 amino acid long peptide vaccine was designed which could provide protection against several members of the mammarenavirus family in a variety of population worldwide as denoted by the epitope conservancy and population coverage analysis. This study of designing a peptide vaccine targeting the glycoprotein of mammarenavirues may help develop novel therapeutics in near future.

TFE3-Splicing Factor Fusions Represent Functional Drivers and Druggable Targets in Translocation Renal Cell Carcinoma

TFE3 is a member of the basic helix-loop-helix leucine zipper MiT transcription factor family, and its chimeric proteins are associated with translocation renal cell carcinoma (tRCC). Despite the variety of gene fusions, most TFE3 fusion partner genes are related to spliceosome machinery. Dissecting the function of TFE3 fused to spliceosome machinery factors (TFE3-SF) could direct the development of effective therapies for this lethal disease, which is refractory to standard treatments for kidney cancer. Here, by using a combination of in silico structure prediction, transcriptome profiling, molecular characterization, and high-throughput high-content screening (HTHCS), we interrogated a number of oncogenic mechanisms of TFE3-SF fusions. TFE3-SF fusions drove the transformation of kidney cells and promoted distinct oncogenic phenotypes in a fusion partner-dependent manner, differentially altering the transcriptome and RNA splicing landscape and activating different oncogenic pathways. Inhibiting TFE3-SF dimerization reversed its oncogenic activity and represented a potential target for therapeutic intervention. Screening the FDA-approved drugs library LOPAC and a small-molecule library (Microsource) using HTHCS combined with FRET technology identified compounds that inhibit TFE3-SF dimerization. Hit compounds were validated in 2D and 3D patient-derived xenograft models expressing TFE3-SF. The antihistamine terfenadine decreased cell proliferation and reduced in vivo tumor growth of tRCC. Overall, these results unmask therapeutic strategies to target TFE3-SF dimerization for treating patients with tRCC.

SIGNIFICANCE

TFE3-splicing factor fusions possess both transcription and splicing factor functions that remodel the transcriptome and spliceosome and can be targeted with dimerization inhibitors to suppress the growth of translocation renal cell carcinoma.

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

Traversing DNA-Protein Interactions Between Mesophilic and Thermophilic Bacteria: Implications from Their Cold Shock Response

Cold shock proteins (CSPs) are small, acidic proteins which contain a conserved nucleic acid-binding domain. These perform mRNA translation acting as "RNA chaperones" when triggered by low temperatures initiating their cold shock response. CSP- RNA interactions have been predominantly studied. Our focus will be CSP-DNA interaction examination, to analyse the diverse interaction patterns such as electrostatic, hydrogen and hydrophobic bonding in both thermophilic and mesophilic bacteria. The differences in the molecular mechanism of these contrasting bacterial proteins are studied. Computational techniques such as modelling, energy refinement, simulation and docking were operated to obtain data for comparative analysis. The thermostability factors which stabilise a thermophilic bacterium and their effect on their molecular regulation is investigated. Conformational deviation, atomic residual fluctuations, binding affinity, Electrostatic energy and Solvent Accessibility energy were determined during stimulation along with their conformational study. The study revealed that mesophilic bacteria E. coli CSP have higher binding affinity to DNA than thermophilic G. stearothermophilus. This was further evident by low conformation deviation and atomic fluctuations during simulation.

Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus

BACKGROUND

The Nonstructural Protein (NSP) 4B of Zika virus of 251 amino acids from (ZIKV/Human/POLG_ZIKVF) with accession number (A0A024B7W1), Induces the production of Endoplasmic Reticulum ER-derived membrane vesicles, which are the sites of viral replication. To understand the physical basis of how proteins fold in nature and to solve the challenge of protein structure prediction, Ab-initio and comparative modeling are crucial tools.

RESULTS

The systematic in silico technique, ThreaDom, had only predicted one domain (4 - 190) of NSP4B. I-TASSER, and Alphafold were ranked as the best servers for full-length 3-D protein structure predictions of NSP4B, where the predicted models were evaluated quantitatively using benchmarked metrics including C-score (-3.43), TM-score (0.77949), RMSD (2.73), and Z-score (1.561). The functional and protein binding motifs were realized using motif databases, secondary and surface accessibility predictions combined with Post-Translational Modification Sites (PTMs) prediction. Two highly conserved protein-binding motifs (Flavi NS4B and Bacillus papRprotein), together with three (PTMs) (Casein Kinase II, Myristyl site, and ASN-Glycosylation site) were predicted utilizing the Motif scan and Scanprosite servers. These patterns and PTMs were associated with NSP4B's role in triggering the development of the viral replication complex and its participation in the localization of NS3 and NS5 on the membrane. Only one hit from Structural Classification of Protein (SCOP) matched the protein sequence at positions 10 to 397 and was categorized six-hairpin glycosidases superfamily according to CATH (Class, Architecture, Topology, and Homology). Integrating this NSP4B information with the templates' SCOP and CATH annotations achieves it easier to attribute structure-function/evolution links to both previously known and recently discovered protein structures.

Discovery of a Novel Cellobiose Dehydrogenase from Cellulomonas palmilytica EW123 and Its Sugar Acids Production

Cellobiose dehydrogenases (CDHs) are a group of enzymes belonging to the hemoflavoenzyme group, which are mostly found in fungi. They play an important role in the production of acid sugar. In this research, CDH annotated from the actinobacterium Cellulomonas palmilytica EW123 (CpCDH) was cloned and characterized. The CpCDH exhibited a domain architecture resembling class-I CDH found in Basidiomycota. The cytochrome c and flavin-containing dehydrogenase domains in CpCDH showed an extra-long evolutionary distance compared to fungal CDH. The amino acid sequence of CpCDH revealed conservative catalytic amino acids and a distinct flavin adenine dinucleotide region specific to CDH, setting it apart from closely related sequences. The physicochemical properties of CpCDH displayed optimal pH conditions similar to those of CDHs but differed in terms of optimal temperature. The CpCDH displayed excellent enzymatic activity at low temperatures (below 30°C), unlike other CDHs. Moreover, CpCDH showed the highest substrate specificity for disaccharides such as cellobiose and lactose, which contain a glucose molecule at the non-reducing end. The catalytic efficiency of CpCDH for cellobiose and lactose were 2.05 x 105 and 9.06 x 104 (M-1 s-1), respectively. The result from the Fourier-transform infrared spectroscopy (FT-IR) spectra confirmed the presence of cellobionic and lactobionic acids as the oxidative products of CpCDH. This study establishes CpCDH as a novel and attractive bacterial CDH, representing the first report of its kind in the Cellulomonas genus.

Assessing sequence variation, haplotype analysis and molecular characterisation of aspartate kinase2 (ask2) gene regulating methionine biosynthesis in diverse maize inbreds

Traditional maize grain is deficient in methionine, an essential amino acid required for proper growth and development in humans and poultry birds. Thus, development of high methionine maize (HMM) assumes great significance in alleviating malnutrition through sustainable and cost-effective approach. Of various genetic loci, aspartate kinase2 (ask2) gene plays a pivotal role in regulating methionine accumulation in maize. Here, we sequenced the entire ask2 gene of 5394 bp with 13 exons in five wild and five mutant maize inbreds to understand variation at nucleotide level. Sequence analysis revealed that an SNP in exon-13 caused thymine to adenine transversion giving rise to a favourable mutant allele associated with leucine to glutamine substitution in mutant ASK2 protein. Gene-based diversity analysis with 11 InDel markers grouped 48 diverse inbreds into three major clusters with an average genetic dissimilarity of 0.570 (range, 0.0-0.9). The average major allele frequency, gene diversity and PIC are 0.693, 0.408 and 0.341, respectively. A total of 45 haplotypes of the ask2 gene were identified among the maize inbreds. Evolutionary relationship analysis performed among 22 orthologues grouped them into five major clusters. The number of exons varied from 7 to 17, with length varying from 12 to 495 bp among orthologues. ASK2 protein with 565 amino acids was predicted to be in homo-dimeric state with lysine and tartaric acid as binding ligands. Amino acid kinase and ACT domains were found to be conserved in maize and orthologues. The study depicted the presence of enough genetic diversity in ask2 gene in maize, and development of HMM can be accelerated through introgression of favourable allele of ask2 into the parental lines of elite hybrids using molecular breeding.

Decoding the structural integrity and multifunctional role of Era protein in the survival of Mycobacterium tuberculosis H37Rv

Era, a widely known GTP binding protein found in many organisms including prokaryotes and eukaryotes and plays a significant role in many fundamental cellular processes like cell growth, differentiation and signaling. In Mycobacterium tuberculosis (Mtb) H37Rv, Era protein had been proved as a GTPase protein but its structural and functional insights are still lacking. Through comparative analysis, structural modeling, docking and using various bioinformatic tools, a detailed investigation of Era was carried out to deduce the structure, function and residues involved in the activity of the protein. Intriguingly, docking results revealed high binding affinity of Era not only with GTP but also with ATP. Myristoylation modifications and phosphorylations on Era were predicted to possibly aid in regulating Era activity and localization; and also the role of Era in translation regulation was foreseen by showing its association with 16s rRNA. Moreover, point mutation of Era residues revealed the effect of W288G and K19G in highly destabilizing the protein structure and activity. Additionally, Era protein was docked with 25 GTPase/ATPase inhibitors, where, Dynasore inhibitor showed the highest affinity for the protein's GTP binding sites and can be used for further drug trials to inhibit growth of mycobacteria.Communicated by Ramaswamy H. Sarma.

Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus

Lymphoid leukosis is a poultry neoplastic disease caused by avian leukosis virus (ALV) and is characterized by high morbidity and variable mortality rates in chicks. Currently, no effective treatment and vaccination is the only means to control it. This study exploited the immunoinformatics approaches to construct multi-epitope vaccine against ALV. ABCpred and IEDB servers were used to predict B and T lymphocytes epitopes from the viral proteins, respectively. Antigenicity, allergenicity and toxicity of the epitopes were assessed and used to construct the vaccine with suitable adjuvant and linkers. Secondary and tertiary structures of the vaccine were predicted, refined and validated. Structural errors, solubility, stability, immune simulation, dynamic simulation, docking and in silico cloning were also evaluated.The constructed vaccine was hydrophilic, antigenic and non-allergenic. Ramchandran plot showed most of the residues in the favored and additional allowed regions. ProsA server showed no errors in the vaccine structure. Immune simulation showed significant immunoglobulins and cytokines levels. Stability was enhanced by disulfide engineering and molecular dynamic simulation. Docking of the vaccine with chicken's TLR7 revealed competent binding energies.The vaccine was cloned in pET-30a(+) vector and efficiently expressed in Escherichia coli. This study provided a potent peptide vaccine that could assist in tailoring a rapid and cost-effective vaccine that helps to combat ALV. However, experimental validation is required to assess the vaccine efficiency.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

The origin and structural evolution of de novo genes in Drosophila

Recent studies reveal that de novo gene origination from previously non-genic sequences is a common mechanism for gene innovation. These young genes provide an opportunity to study the structural and functional origins of proteins. Here, we combine high-quality base-level whole-genome alignments and computational structural modeling to study the origination, evolution, and protein structures of lineage-specific de novo genes. We identify 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. Sequence composition, evolutionary rates, and expression patterns indicate possible gradual functional or adaptive shifts with their gene ages. Surprisingly, we find little overall protein structural changes in candidates from the Drosophilinae lineage. We identify several candidates with potentially well-folded protein structures. Ancestral sequence reconstruction analysis reveals that most potentially well-folded candidates are often born well-folded. Single-cell RNA-seq analysis in testis shows that although most de novo gene candidates are enriched in spermatocytes, several young candidates are biased towards the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and protein structural changes of Drosophilinae-specific de novo genes.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning

As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there are still deficiencies for effectively mining the discriminant information for enzyme function recognition in existing methods. In this study, we present MVDINET, a novel method for multi-level enzyme function prediction. First, the initial multi-view feature data is extracted by the enzyme sequence. Then, the above initial views are fed into various deep specific network modules to learn the depth-specificity information. Further, a deep view interaction network is designed to extract the interaction information. Finally, the specificity information and interaction information are fed into a multi-view adaptively weighted classification. We compressively evaluate MVDINET on benchmark datasets and demonstrate that MVDINET is superior to existing methods.

Two predicted α-helices within the prion-like domain of TIAR-1 play a crucial role in its association with stress granules in Caenorhabditis elegans

Stress granules (SGs) are sites for mRNA storage, protection, and translation repression. TIA1 and TIAR1 are two RNA-binding proteins that are key players in SGs formation in mammals. TIA1/TIAR have a prion-like domain (PrD) in their C-terminal that promotes liquid-phase separation. Lack of any TIA1/TIAR has severe consequences in mice. However, it is not clear whether the failure to form proper SGs is the cause of any of these problems. We disrupted two predicted α-helices within the prion-like domain of the Caenohabditis elegans TIA1/TIAR homolog, TIAR-1, to test whether its association with SGs is important for the nematode. We found that tiar-1 PrD mutant animals continued to form TIAR-1 condensates under stress in the C. elegans gonad. Nonetheless, TIAR-1 condensates appeared fragile and disassembled quickly after stress. Apparently, the SGs continued to associate regularly as observed with CGH-1, an SG marker. Like tiar-1-knockout nematodes, tiar-1 PrD mutant animals exhibited fertility problems and a shorter lifespan. Notwithstanding this, tiar-1 PrD mutant nematodes were no sensitive to stress. Our data demonstrate that the predicted prion-like domain of TIAR-1 is important for its association with stress granules. Moreover, this domain may also play a significant role in various TIAR-1 functions unrelated to stress, such as fertility, embryogenesis and lifespan.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Evidence of DISC1 as an arsenic binding protein and implications regarding its role as a translational activator

Disrupted-in-schizophrenia-1 (DISC1) is a scaffolding protein that plays a pivotal role in orchestrating signaling pathways involved in neurodevelopment, neural migration, and synaptogenesis. Among those, it has recently been reported that the role of DISC1 in the Akt/mTOR pathway can shift from a global translational repressor to a translational activator in response to oxidative stress induced by arsenic. In this study we provide evidence that DISC1 can directly bind arsenic via a C-terminal cysteine motif (C-X-C-X-C). A series of fluorescence-based binding assays were conducted with a truncated C-terminal domain construct of DISC1 and a series of single, double, and triple cysteine mutants. We found that arsenous acid, a trivalent arsenic derivative, specifically binds to the C-terminal cysteine motif of DISC1 with low micromolar affinity. All three cysteines of the motif are required for high-affinity binding. Electron microscopy experiments combined with in silico structural predictions reveal that the C-terminal of DISC1 forms an elongated tetrameric complex. The cysteine motif is consistently predicted to be located within a loop, fully exposed to solvent, providing a simple molecular framework to explain the high-affinity of DISC1 toward arsenous acid. This study sheds light on a novel functional facet of DISC1 as an arsenic binding protein and highlights its potential role as both a sensor and translational modulator within Akt/mTOR pathway.

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

Visceral Leishmaniasis (VL) is a neglected tropical disease of public health importance in the Indian subcontinent. Despite consistent elimination initiatives, the disease has not yet been eliminated and there is an increased risk of resurgence from active VL reservoirs including asymptomatic, post kala azar dermatitis leishmaniasis (PKDL) and HIV-VL co-infected individuals. To achieve complete elimination and sustain it in the long term, a prophylactic vaccine, which can elicit long lasting immunity, is desirable. In this study, we employed immunoinformatic tools to design a multi-subunit epitope vaccine for the Indian population by targeting antigenic secretory proteins screened from the Leishmania donovani proteome. Out of 8014 proteins, 277 secretory proteins were screened for their cellular location and proteomic evidence. Through NCBI BlastP, unique fragments of the proteins were cropped, and their antigenicity was evaluated. B-cell, HTL and CTL epitopes as well as IFN-ɣ, IL-17, and IL-10 inducers were predicted, manually mapped to the fragments and common regions were tabulated forming a peptide ensemble. The ensemble was evaluated for Class I MHC immunogenicity and toxicity. Further, immunogenic peptides were randomly selected and used to design vaccine constructs. Eight vaccine constructs were generated by linking random peptides with GS linkers. Synthetic TLR-4 agonist, RS09 was used as an adjuvant and linked with the constructs using EAAK linkers. The predicted population coverage of the constructs was ∼99.8 % in the Indian as well as South Asian populations. The most antigenic, nontoxic, non-allergic construct was chosen for the prediction of secondary and tertiary structures. The 3D structures were refined and analyzed using Ramachandran plot and Z-scores. The construct was docked with TLR-4 receptor. Molecular dynamic simulation was performed to check for the stability of the docked complex. Comparative in silico immune simulation studies showed that the predicted construct elicited humoral and cell-mediated immunity in human host comparable to that elicited by Leish-F3, which is a promising vaccine candidate for human VL.

The SARS-CoV-2 protein ORF3c is a mitochondrial modulator of innate immunity

The SARS-CoV-2 genome encodes a multitude of accessory proteins. Using comparative genomic approaches, an additional accessory protein, ORF3c, has been predicted to be encoded within the ORF3a sgmRNA. Expression of ORF3c during infection has been confirmed independently by ribosome profiling. Despite ORF3c also being present in the 2002-2003 SARS-CoV, its function has remained unexplored. Here we show that ORF3c localizes to mitochondria, where it inhibits innate immunity by restricting IFN-β production, but not NF-κB activation or JAK-STAT signaling downstream of type I IFN stimulation. We find that ORF3c is inhibitory after stimulation with cytoplasmic RNA helicases RIG-I or MDA5 or adaptor protein MAVS, but not after TRIF, TBK1 or phospho-IRF3 stimulation. ORF3c co-immunoprecipitates with the antiviral proteins MAVS and PGAM5 and induces MAVS cleavage by caspase-3. Together, these data provide insight into an uncharacterized mechanism of innate immune evasion by this important human pathogen.

Insights into RAG Evolution from the Identification of "Missing Link" Family A RAGL Transposons

A series of "molecular domestication" events are thought to have converted an invertebrate RAG-like (RAGL) transposase into the RAG1-RAG2 (RAG) recombinase, a critical enzyme for adaptive immunity in jawed vertebrates. The timing and order of these events are not well understood, in part because of a dearth of information regarding the invertebrate RAGL-A transposon family. In contrast to the abundant and divergent RAGL-B transposon family, RAGL-A most closely resembles RAG and is represented by a single orphan RAG1-like (RAG1L) gene in the genome of the hemichordate Ptychodera flava (PflRAG1L-A). Here, we provide evidence for the existence of complete RAGL-A transposons in the genomes of P. flava and several echinoderms. The predicted RAG1L-A and RAG2L-A proteins encoded by these transposons intermingle sequence features of jawed vertebrate RAG and RAGL-B transposases, leading to a prediction of DNA binding, catalytic, and transposition activities that are a hybrid of RAG and RAGL-B. Similarly, the terminal inverted repeats (TIRs) of the RAGL-A transposons combine features of both RAGL-B transposon TIRs and RAG recombination signal sequences. Unlike all previously described RAG2L proteins, RAG2L-A proteins contain an acidic hinge region, which we demonstrate is capable of efficiently inhibiting RAG-mediated transposition. Our findings provide evidence for a critical intermediate in RAG evolution and argue that certain adaptations thought to be specific to jawed vertebrates (e.g. the RAG2 acidic hinge) actually arose in invertebrates, thereby focusing attention on other adaptations as the pivotal steps in the completion of RAG domestication in jawed vertebrates.

In silico discovery of epitopes of gag and env proteins for the development of a multi-epitope vaccine candidate against Maedi Visna Virus using reverse vaccinology approach

Maedi Visna Virus (MVV) causes a chronic viral disease in sheep. Since there is no specific therapeutic drug that targets MVV, development of a vaccine against the MVV is inevitable. This study aimed to analyze the gag and env proteins as vaccine candidate proteins and to identify epitopes in these proteins. In addition, it was aimed to construct a multi-epitope vaccine candidate. According to the obtained results, the gag protein was detected to be more conserved and had a higher antigenicity value. Also, the number of alpha helix in the secondary structure was higher and transmembrane helices were not detected. Although many B cell and MHC-I/II epitopes were predicted, only 19 of them were detected to have the properties of antigenic, non-allergenic, non-toxic, soluble, and non-hemolytic. Of these epitopes, five were remarkable due to having the highest antigenicity value. However, the final multi-epitope vaccine was constructed with 19 epitopes. A strong affinity was shown between the final multi-epitope vaccine and TLR-2/4. In conclusion, the gag protein was a better antigen. However, both proteins had epitopes with high antigenicity value. Also, the final multi-epitope vaccine construct had a potential to be used as a peptide vaccine due to its immuno-informatics results.

Immunoinformatics aided designing of a next generation poly-epitope vaccine against uropathogenic Escherichia coli to combat urinary tract infections

Urinary tract infections (UTIs) are the second most prevalent bacterial infections and uropathogenic Escherichia coli (UPEC) stands among the primary causative agents of UTIs. The usage of antibiotics is the routine therapy being used in various countries to treat UTIs but becoming ineffective because of increasing antibiotic resistance among UPEC strains. Thus, there must be the development of some alternative treatment strategies such as vaccine development against UPEC. In the following study, pan-genomics along with reverse vaccinology approaches is used under the framework of bioinformatics for the identification of core putative vaccine candidates, employing 307 UPEC genomes (complete and draft), available publicly. A total of nine T-cell epitopes (derived from B-cells) of both MHC classes (I and II), were prioritized among three potential protein candidates. These epitopes were then docked together by using linkers (GPGPG and AAY) and an adjuvant (Cholera Toxin B) to form a poly-valent vaccine construct. The chimeric vaccine construct was undergone by molecular modelling, further refinement and energy minimization. We predicted positive results of the vaccine construct in immune simulations with significantly high levels of immune cells. The protein-protein docking analysis of vaccine construct with toll-like receptors predicted efficient binding, which was further validated by molecular dynamics simulation of vaccine construct with TLR-2 and TLR-4 at 120 ns, resulting in stable complexes' conformation throughout the simulation run. Overall, the vaccine construct demonstrated positive antigenic response. In future, this chimeric vaccine construct or the identified epitopes could be experimentally validated for the development of UPEC vaccines against UTIs.Communicated by Ramaswamy H. Sarma.

Recognition of immune reactive proteins as a potential multiepitope vaccine candidate of Taenia solium cysticerci through proteomic approach

Metacestode, the larva of Taenia solium, is the causative agent for neurocysticercosis (NCC), which causes epilepsy. The unavailability of a vaccine against human NCC is a major cause for its widespread prevalence across the globe. Therefore, the development of a reliable vaccine against NCC is the need of the hour. Employing a combination of proteomics and immunoinformatics, we endeavored to formulate a vaccine candidate. The immune reactive cyst fluid antigens of T. solium were identified by immune-blotting two-dimensional gels with NCC patient's sera, followed by Matrix-assisted laser desorption-ionization analysis. We performed a detailed proteomic study of these immune reactive proteins by utilizing immune-informatics tools, identified the nontoxic, nonallergic, B-cell epitopes, and collected epitopes with the least sequence homology with human and other Taenia species. These epitopes were joined through linkers to construct a multiepitope vaccine. Different physiochemical parameters such as molecular weight (23.82 kDa), instability (39.91), and aliphatic index (49.61) were calculated to ensure the stability of the linked peptides vaccine. The vaccine demonstrated stable interactions with different immune receptors like Toll-like receptor 4 and IgG confirming that it will effectively stimulate the host immune response. We anticipate that our designed B-cell linear epitope-based vaccine will show promising results in in vitro and in vivo assays. This study provides a platform that would be useful to develop other suitable vaccine candidates to prevent helminthic neglected tropical diseases in near future.

Designing and development of multi-epitope chimeric vaccine against Helicobacter pylori by exploring its entire immunogenic epitopes: an immunoinformatic approach

BACKGROUND

Helicobacter pylori is a prominent causative agent of gastric ulceration, gastric adenocarcinoma and gastric lymphoma and have been categorised as a group 1 carcinogen by WHO. The treatment of H. pylori with proton pump inhibitors and antibiotics is effective but also leads to increased antibiotic resistance, patient dissatisfaction, and chances of reinfection. Therefore, an effective vaccine remains the most suitable prophylactic option for mass administration against this infection.

RESULTS

We modelled a multi-chimera subunit vaccine candidate against H. pylori by screening its secretory/outer membrane proteins. We identified B-cell, MHC-II and IFN-γ-inducing epitopes within these proteins. The population coverage, antigenicity, physiochemical properties and secondary structure were evaluated using different in-silico tools, which showed it can be a good and effective vaccine candidate. The 3-D construct was predicted, refined, validated and docked with TLRs. Finally, we performed the molecular docking/simulation and immune simulation studies to validate the stability of interaction and in-silico cloned the epitope sequences into a pET28b(+) plasmid vector.

CONCLUSION

The multiepitope-constructed vaccine contains T- cells, B-cells along with IFN-γ inducing epitopes that have the property to generate good cell-mediated immunity and humoral response. This vaccine can protect most of the world's population. The docking study and immune simulation revealed a good binding with TLRs and cell-mediated and humoral immune responses, respectively. Overall, we attempted to design a multiepitope vaccine and expect this vaccine will show an encouraging result against H. pylori infection in in-vivo use.

Divergence of cytokinesis and dimorphism control by myosin II regulatory light chain in fission yeasts

Non-muscle myosin II activation by regulatory light chain (Rlc1Sp) phosphorylation at Ser35 is crucial for cytokinesis during respiration in the fission yeast Schizosaccharomyces pombe. We show that in the early divergent and dimorphic fission yeast S. japonicus non-phosphorylated Rlc1Sj regulates the activity of Myo2Sj and Myp2Sj heavy chains during cytokinesis. Intriguingly, Rlc1Sj-Myo2Sj nodes delay yeast to hyphae onset but are essential for mycelial development. Structure-function analysis revealed that phosphorylation-induced folding of Rlc1Sp α1 helix into an open conformation allows precise regulation of Myo2Sp during cytokinesis. Consistently, inclusion of bulky tryptophan residues in the adjacent α5 helix triggered Rlc1Sp shift and supported cytokinesis in absence of Ser35 phosphorylation. Remarkably, unphosphorylated Rlc1Sj lacking the α1 helix was competent to regulate S. pombe cytokinesis during respiration. Hence, early diversification resulted in two efficient phosphorylation-independent and -dependent modes of Rlc1 regulation of myosin II activity in fission yeasts, the latter being conserved through evolution.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Effect of cysteine mutation at Ca2+ coordinating residues to the autolysis, folding and hydrophobicity of full length and mature Rand protease: molecular dynamics simulation and essential dynamics

Rand protease is a serine protease that shared common characteristics with members of the MEROPS S8 subtilisin family. It is thermostable, highly stable in organic solvent and broad in specificity. Many structures of homologous protein solved by X-ray crystallography and NMR have been deposited to Protein Data Bank (PDB) which allowed this study to rely on structure prediction by deep learning to build three-dimensional (3D) structure of full length and mature Rand protease (flRP and mRP). In silico cysteine mutation to 7 predicted high affinity Ca2+ coordinating residues were introduced, and the mutants were subjected to molecular dynamics simulation to study its effect on flRP and mRP. MD simulation showed a marked increase in flexibility of the pro-peptide segment indicating the impact of single cysteine substitution at high affinity Ca2+ coordinating residues to autolysis of flRP. MD simulation for mRP reaffirmed the role of Ca2+ coordinating sites in providing stability to Rand protease. In addition, these residues also affect the autolysis, folding and hydrophobicity of RP. Essential dynamics observed large contribution of the first few eigenvectors of flRP, mRP and their high affinity Ca2+ coordinating residues mutants to the TMSF values which indicates that these values account for a large portion of the overall atomic fluctuations. These results have given a more comprehensive understanding on the role of cysteine substituted Ca2+ coordinating surface loop to the structure of flRP and mRP which are important in contributing to the structural stability of subtilisin.Communicated by Ramaswamy H. Sarma.

Insights into RAG evolution from the identification of "missing link" family A RAGL transposons

A series of "molecular domestication" events are thought to have converted an invertebrate RAG-like (RAGL) transposase into the RAG1-RAG2 (RAG) recombinase, a critical enzyme for adaptive immunity in jawed vertebrates. The timing and order of these events is not well understood, in part because of a dearth of information regarding the invertebrate RAGL-A transposon family. In contrast to the abundant and divergent RAGL-B transposon family, RAGL-A most closely resembles RAG and is represented by a single orphan RAG1-like (RAG1L) gene in the genome of the hemichordate Ptychodera flava (PflRAG1L-A). Here, we provide evidence for the existence of complete RAGL-A transposons in the genomes of P. flava and several echinoderms. The predicted RAG1L-A and RAG2L-A proteins encoded by these transposons intermingle sequence features of jawed vertebrate RAG and RAGL-B transposases, leading to a prediction of DNA binding, catalytic, and transposition activities that are a hybrid of RAG and RAGL-B. Similarly, the terminal inverted repeats (TIRs) of the RAGL-A transposons combine features of both RAGL-B transposon TIRs and RAG recombination signal sequences. Unlike all previously described RAG2L proteins, PflRAG2L-A and echinoderm RAG2L-A contain an acidic hinge region, which we demonstrate is capable of efficiently inhibiting RAG-mediated transposition. Our findings provide evidence for a critical intermediate in RAG evolution and argue that certain adaptations thought to be specific to jawed vertebrates (e.g., the RAG2 acidic hinge) actually arose in invertebrates, thereby focusing attention on other adaptations as the pivotal steps in the completion of RAG domestication in jawed vertebrates.

A chimeric protein-based vaccine elicits a strong IgG antibody response and confers partial protection against Shiga toxin-producing Escherichia coli in mice

Background

Shiga toxin-producing Escherichia coli (STEC) is a foodborne pathogen that causes gastrointestinal infections, ranging from acute diarrhea and dysentery to life-threatening diseases such as Hemolytic Uremic Syndrome. Currently, a vaccine to prevent STEC infection is an unmet medical need.

Results

We developed a chimeric protein-based vaccine targeting seven virulence factors of STEC, including the Stx2B subunit, Tir, Intimin, EspA, Cah, OmpT, and AggA proteins. Immunization of mice with this vaccine candidate elicited significant humoral and cellular immune responses against STEC. High levels of specific IgG antibodies were found in the serum and feces of immunized mice. However, specific IgA antibodies were not detected in either serum or feces. Furthermore, a significantly higher percentage of antigen-specific CD4+ T cells producing IFN-γ, IL-4, and IL-17 was observed in the spleens of immunized mice. Notably, the immunized mice showed decreased shedding of STEC O157:H7 and STEC O91:H21 strains and were protected against weight loss during experimental infection. Additionally, infection with the STEC O91:H21 strain resulted in kidney damage in control unimmunized mice; however, the extent of damage was slightly lower in immunized mice. Our findings suggest that IgG antibodies induced by this vaccine candidate may have a role in inhibiting bacterial adhesion and complement-mediated killing.

Conclusion

This study provides evidence that IgG responses are involved in the host defense against STEC. However, our results do not rule out that other classes of antibodies also participate in the protection against this pathogen. Additional work is needed to improve the protection conferred by our vaccine candidate and to elucidate the relevant immune responses that lead to complete protection against this pathogen.

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

SummaryMicrobial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453 560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

The origin and structural evolution of de novo genes in Drosophila

Although previously thought to be unlikely, recent studies have shown that de novo gene origination from previously non-genic sequences is a relatively common mechanism for gene innovation in many species and taxa. These young genes provide a unique set of candidates to study the structural and functional origination of proteins. However, our understanding of their protein structures and how these structures originate and evolve are still limited, due to a lack of systematic studies. Here, we combined high-quality base-level whole genome alignments, bioinformatic analysis, and computational structure modeling to study the origination, evolution, and protein structure of lineage-specific de novo genes. We identified 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. We found a gradual shift in sequence composition, evolutionary rates, and expression patterns with their gene ages, which indicates possible gradual shifts or adaptations of their functions. Surprisingly, we found little overall protein structural changes for de novo genes in the Drosophilinae lineage. Using Alphafold2, ESMFold, and molecular dynamics, we identified a number of de novo gene candidates with protein products that are potentially well-folded, many of which are more likely to contain transmembrane and signal proteins compared to other annotated protein-coding genes. Using ancestral sequence reconstruction, we found that most potentially well-folded proteins are often born folded. Interestingly, we observed one case where disordered ancestral proteins become ordered within a relatively short evolutionary time. Single-cell RNA-seq analysis in testis showed that although most de novo genes are enriched in spermatocytes, several young de novo genes are biased in the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and structural changes of Drosophilinae-specific de novo genes.

Evidence of Disrupted-in Schizophrenia 1 (DISC1) as an arsenic binding protein and implications regarding its role as a translational activator

Disrupted-in-schizophrenia-1 (DISC1) is a scaffold protein that plays a pivotal role in orchestrating signaling pathways involved in neurodevelopment, neural migration, and synaptogenesis. Among those, it has recently been reported that the role DISC1 in the Akt/mTOR pathway can shift from a global translational repressor to a translational activator in response to oxidative stress induced by arsenic. In this study we are providing evidence that DISC1 can directly bind arsenic via a C-terminal cysteine motif (C-X-C-X-C). A series of fluorescence-based binding assays were conducted with a truncated C-terminal domain construct of DISC1 and a of series of single, double, and triple cysteine mutants. We found that arsenous acid, a trivalent arsenic derivative, specifically binds to the C-terminal cysteine motif of DISC1 with low micromolar affinity. All three cysteines of the motif are required for high-affinity binding. Electron microscopy experiments combined with in silico structural predictions revealed that that the C-terminal of DISC1 forms an elongated tetrameric complex. The cysteine motif is consistently predicted to be located within a loop, fully exposed to solvent, providing a simple molecular framework to explain the high-affinity of DISC1 toward arsenous acid. This study sheds light on a novel functional facet of DISC1 as an arsenic binding protein and highlights its potential role as both a sensor and translational modulator within the Akt/mTOR pathway.

Immunoinformatics Strategy to Develop a Novel Universal Multiple Epitope-Based COVID-19 Vaccine

Currently available COVID vaccines are effective in reducing mortality and severity but do not prevent transmission of the virus or reinfection by the emerging SARS-CoV-2 variants. There is an obvious need for better and longer-lasting effective vaccines for various prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of a broad-spectrum vaccine that can be used to prevent infection by reducing both the transmission rate and re-infection. During the initial phases of SARS-CoV-2 infection, the nucleocapsid (N) protein is one of the most abundantly expressed proteins. Additionally, it has been identified as the most immunogenic protein of SARS-CoV-2. In this study, state-of-the-art bioinformatics techniques have been exploited to design novel multiple epitope vaccines using conserved regions of N proteins from prevalent strains of SARS-CoV-2 for the prediction of B- and T-cell epitopes. These epitopes were sorted based on their immunogenicity, antigenicity score, and toxicity. The most effective multi-epitope construct with possible immunogenic properties was created using epitope combinations. EAAAK, AAY, and GPGPG were used as linkers to connect epitopes. The developed vaccines have shown positive results in terms of overall population coverage and stimulation of the immune response. Potential expression of the chimeric protein construct was detected after it was cloned into the Pet28a/Cas9-cys vector for expression screening in Escherichia coli. The developed vaccine performed well in computer-based immune response simulation and covered a diverse allelic population worldwide. These computational findings are very encouraging for the further testing of our candidate vaccine, which could eventually aid in the control and prevention of SARS-CoV-2 infections globally.

Finding and engineering the newly found bacterial superoxide dismutase enzyme to increase its thermostability and decrease the immunogenicity: a computational and experimental research

Superoxide dismutase (SOD) is one of the most important antioxidant enzymes that can reduce oxidative stress in the cell environment. Nowadays, bacterial sources of enzyme are commercially applicable in the cosmetics and pharmaceutical industries, but the allergenic effect of proteins from non-human sources has been mentioned as disadvantage of these kinds of enzymes. In this study, to find the suitable bacterial SOD candidate for decreasing immunogenicity, the sequences of five thermophilic bacteria were selected as reference species. Then, linear and conformational B-cell epitopes of the SOD were analyzed by different servers. The stability and immunogenicity of mutant positions were also evaluated. The mutant gene was inserted into the pET-23a expression vector and transformed into E. Coli BL21 (DE3) for expression of the recombinant enzyme. Afterward, the expression of the mutant enzyme was evaluated by SDS-PAGE analysis and the recombinant enzyme activity was assessed. Anoxybacillus gonensis was selected as a reasonable SOD source according to BLAST search, physicochemical properties analysis, and prediction of allergenic features. Regarding our results, five residues including E84, E142, K144, G147, and M148 were predicted as candidates for mutagenesis. Finally, the K144A was chosen as the final modification due to the increase in the stability of the enzyme and decreased immunogenicity of the enzyme as well. The enzyme activity was 240 U/ml at room temperature. Alternation in K144 to alanine caused increased stability of the enzyme. In silico studies confirmed non-antigenic protein after mutation.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports that immunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomics discipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.