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Given its global morbidity and mortality rates, malaria continues to be a major public health concern. Despite significant progress in the fight against malaria, efforts to control and eradicate the disease globally are in jeopardy due to lack of a universal vaccine. The conserved short peptide sequences found in Domain I of Plasmodium falciparum apical membrane antigen 1 (PfAMA1), which are exposed on the parasite cell surface and in charge of Plasmodium falciparum invasion of host cells, make PfAMA1 a promising vaccine candidate antigen. The precise amino acids that make up these conserved short peptides are still unknown, and it is still difficult to pinpoint the molecular processes by which PfAMA1 interacts with the human host cell during invasion. The creation of a universal malaria vaccine based on the AMA1 antigen is challenging due to these knowledge limitations. This study used genome mining techniques to look for these particular short peptides in PfAMA1. Thirty individuals with Plasmodium falciparum malaria had blood samples taken using Whatman's filter papers. DNA from the parasite was taken out using the Chelex technique. Domain I of the Plasmodium falciparum AMA1 gene was amplified using nested polymerase chain reactions, and the amplified products were removed, purified, and sequenced. The DNA sequence generated was converted into the matching amino acid sequence using bioinformatic techniques. These amino acid sequences were utilized to search for antigenic epitopes, therapeutic targets, and conserved short peptides in Domain I of PfAMA1. The results of this investigation shed important light on the molecular mechanisms behind Plasmodium invasion of host cells, a potential PfAMA1 vaccine antigen sequence, and prospective malaria treatment options in the future. Our work offers fresh information on malaria medication and vaccine research that has not been previously discussed. [ABSTRACT FROM AUTHOR]

The localization of a protein's submitochondrial structure is important for therapeutic design of associated disorders caused by mitochondrial abnormalities because many human diseases are directly tied to mitochondria. When Lon protease expression changes, glycolysis replaces respiratory metabolism in the cell, which is a common occurrence in cancer cells. The fact that protein formation is a dynamic research object makes it impossible to reproduce the unique living environment of proteins in an experimental setting, which surely makes it more challenging to determine protein function through experiments. This research suggests a model of Lon protease-based mitochondrial protection under myocardial ischemia based on ML (machine learning). To ensure the balance of all submitochondrial proteins, the data set is processed using a random oversampling method, each overlapping fixed-length subsequence that is created from the protein sequence functions as a channel in the convolution layer. The results demonstrate that applying the oversampling strategy increases the ROC value by 17.6%-21.3%. Our prediction method is successful as evidenced by the fact that ML prediction outperforms the predictions of other conventional classifiers. [ABSTRACT FROM AUTHOR]

Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics. However, since traditional experimental methods are both time-consuming and costly, it is difficult to analyze and predict the massive amount of PPI data quickly and accurately. To address these issues, many computational systems employing machine learning knowledge were widely applied to PPIs prediction, thereby improving the overall recognition rate. In this paper, a novel and efficient computational technology is presented to implement a protein interaction prediction system using only protein sequence information. First, the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) was employed to generate a position-specific scoring matrix (PSSM) containing protein evolutionary information from the initial protein sequence. Second, we used a novel data processing feature representation scheme, MatFLDA, to extract the essential information of PSSM for protein sequences and obtained five training and five testing datasets by adopting a five-fold cross-validation method. Finally, the random fern (RFs) classifier was employed to infer the interactions among proteins, and a model called MatFLDA_RFs was developed. The proposed MatFLDA_RFs model achieved good prediction performance with 95.03% average accuracy on Yeast dataset and 85.35% average accuracy on H. pylori dataset, which effectively outperformed other existing computational methods. The experimental results indicate that the proposed method is capable of yielding better prediction results of PPIs, which provides an effective tool for the detection of new PPIs and the in-depth study of proteomics. Finally, we also developed a web server for the proposed model to predict protein-protein interactions, which is freely accessible online at http://120.77.11.78:5001/webserver/MatFLDA_RFs. [ABSTRACT FROM AUTHOR]

The prediction of protein subcellular localization not only is important for the study of protein structure and function but also can facilitate the design and development of new drugs. In recent years, feature extraction methods based on protein evolution information have attracted much attention and made good progress. Based on the protein position-specific score matrix (PSSM) obtained by PSI-BLAST, PSSM-GSD method is proposed according to the data distribution characteristics. In order to reflect the protein sequence information as much as possible, AAO method, PSSM-AAO method, and PSSM-GSD method are fused together. Then, conditional entropy-based classifier chain algorithm and support vector machine are used to locate multilabel proteins. Finally, we test Gpos-mPLoc and Gneg-mPLoc datasets, considering the severe imbalance of data, and select SMOTE algorithm to expand a few sample; the experiment shows that the AAO + PSSM ∗ method in the paper achieved 83.1% and 86.8% overall accuracy, respectively. After experimental comparison of different methods, AAO + PSSM ∗ has good performance and can effectively predict protein subcellular location. [ABSTRACT FROM AUTHOR]

Background. Serine proteinase inhibitors, clade B, member 3 (SerpinB3) and B4 are highly similar in amino acid sequences and associated with inflammation regulation. We investigated SerpinB3 and B4 expression and their roles in chronic rhinosinusitis with nasal polyps (CRSwNP). Methods. The expression of SerpinB3 and B4 in nasal mucosa tissues, brush cells, and secretions from CRSwNP patients was measured, and their regulation by inflammatory cytokines were investigated. Their functions were also analyzed using air–liquid interface (ALI)-cultured primary human nasal epithelial cells (HNECs) and transcriptomic analysis. Results. Both SerpinB3 and B4 expression was higher in nasal mucosa, brush cells, and secretions from eosinophilic (E) CRSwNP and nonECRSwNP patients than in healthy controls. Immunofluorescence staining indicated that SerpinB3 and B4 were primarily expressed in epithelial cells and their expression was higher in CRSwNP patients. SerpinB3 and B4 expression was upregulated by interleukin-4 (IL-4), IL-5, IL-6, and IL-17a. Transcriptomic analysis identified differentially expressed genes (DEGs) in response to recombinant SerpinB3 and B4 stimulation. Both the DEGs of SerpinB3 and B4 were associated with disease genes of nasal polyps and inflammation in DisGeNET database. Pathway enrichment indicated that downregulated DEGs of SerpinB3 and B4 were both enriched in cytokine–cytokine receptor interactions, with CXCL8 as the hub gene in the protein–protein interaction networks. Furthermore, CXCL8/IL-8 expression was downregulated by recombinant SerpinB3 and B4 protein in ALI-cultured HNECs, and upregulated when knockdown of SerpinB3/B4. Conclusion. SerpinB3/B4 expression is upregulated in nasal mucosa of CRSwNP patients. SerpinB3/B4 may play an anti-inflammatory role in CRSwNP by inhibiting the expression of epithelial cell-derived CXCL8/IL-8. [ABSTRACT FROM AUTHOR]

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm's effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns. [ABSTRACT FROM AUTHOR]

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. We utilized a dataset comprising 2000 protein sequences, including 1000 SARS-CoV-2 sequences and 1000 non-SARS-CoV-2 sequences. Feature extraction provided 27 essential features representing the primary structural data, achieved through the Discere package. To optimize performance, we employed machine learning classification algorithms such as K-nearest neighbor (KNN), XGBoost, and Naïve Bayes, along with feature selection techniques like genetic algorithm (GA), LASSO, and support vector machine recursive feature elimination (SVM-RFE). The SVM-RFE+KNN model exhibited exceptional performance, achieving a classification accuracy of 99.30%, specificity of 99.52%, and sensitivity of 99.55%. These results demonstrate the model's efficacy in accurately classifying coronavirus protein sequences. Our research successfully developed a robust classification model capable of early detection and prediction of protein sequences in SARS-CoV-2 and other coronaviruses. This advancement holds great promise in facilitating the development of targeted treatments and preventive strategies for combating future viral outbreaks. [ABSTRACT FROM AUTHOR]

In this study, the complete cDNA sequence (1552 bp) of the cold-inducible RNA binding protein gene (cirbp gene) was successfully cloned from the liver in Acanthopagrus schlegelii (initial weight: 15.0 ± 2.3 g). Results showed that Ascirbp (cirbp gene from A. schlegelii) gene has 24 phosphorylation sites, no signal peptide, and no transmembrane helix structure. AsCIRBP, with a molecular weight of 18.84 ku and an isoelectric point of 9.04 was a stable protein that encodes 182 amino acids. Subcellular localization analysis of this protein showed that it was located in the nucleus. Sequence alignment results showed that the AsCIRBP amino acid sequences of various fishes including black porgy were highly conserved, especially the RNA recognition motif (RRM). Those results of real-time quantitative PCR (qRT-PCR) demonstrated that Ascirbp gene was specifically expressed in the liver tissue of black porgy and its expression was significantly increased under cold stress or cold acclimation. The RNA interference experiment results showed that Ascirbp-dsRNA could suppress the expression of Ascirbp gene in the liver of black porgy through intraperitoneal injection. After silencing the expression of Ascirbp gene, RNAi groups were more severely damaged in the structure of the liver tissue and more prone to apoptosis under cold stress than control groups. The results of the study on the linkage between Ascirbp gene expression and mitochondrial apoptosis pathways showed that changes in the expression of the Ascirbp gene had a significant effect on the expression of key genes of apoptosis. The most striking result from silencing the expression of the Ascirbp gene was that expressions of the bcl-2 and apaf-1 gene in the liver of black porgy decreased significantly, which can block the normal apoptotic process. After the disruption of the normal apoptotic process, the expressions of p53, bax, cyto-c, caspase-9, caspase-3, diablo, and caspase-1 gene were significantly affected. These results suggest that Ascirbp gene can inhibit apoptosis and protect tissue structure in the liver tissue of black porgy at low temperatures. [ABSTRACT FROM AUTHOR]

Goose astrovirus genotype 2 (GoAstV2), as a contagious pathogen of fatal visceral gout in goslings, has been widely distributed in major goose-producing regions in China since 2017, leading to significant economic losses to the Chinese goose industry. In this study, a novel goose astrovirus (GoAstV-SDHZ) was isolated and identified from laying hens with nephritis for the first time. Phylogenetic analysis of the sequenced genome of ORF2 gene revealed that the GoAstV-SDHZ strain clustered into Group 2 GoAstVs and shared the highest identity with the other representative GoAstV2 in the nucleotide (ORF1a: 97.2–99.7%; ORF1b: 98.4–99.8%; ORF2: 97.2–99.9%) and amino acid sequence (ORF1a: 98.2–100%; ORF1b: 98.8–99.6%; ORF2: 97.4–99.6%). In summary, this study provides the first evidence of the GoAstV2 infection in Chinese laying hens, which raises potential threat to the poultry industry in China. [ABSTRACT FROM AUTHOR]

Black‐bone silky fowl (Gallus gallus domesticus Brisson) is considered to have strengthening effect on the body and immunomodulatory effects. The black‐bone silky fowl peptide (BSFP) was produced by enzymatic digestion of the whole black‐bone silky fowl (including the head and claws) after removal of the viscera. Afterwards, the four of the characteristic peptides Glu‐Phe (EF), Glu‐Glu‐Leu (EEL), Glu‐His‐Pro‐Thr (EHPT), Ala‐Gly‐Gly‐His (AGGF) of the BSFP were identified by HPLC‐MS/MS. The preventive effects of BSFP and the four characteristic peptides on antioxidant and immunomodulation were investigated. The antioxidant capacity was assessed by in vitro HepG2 intracellular reactive oxygen species (ROS). The immunomodulatory experiments were conducted by measuring the effects of the BSFP and four peptides on the proliferation of splenocytes, T and B lymphocytes cells, the CD4+/CD8+ T lymphocytes ratio, and the phagocytic capacity of macrophages and the nitric oxide (NO) content of macrophages. The four peptides of BSFP showed strong antioxidant capacity, with the most potent peptide for intracellular ROS being AGGF, with 56% inhibition. AGGF, EF, and BSFP showed highly positive effects on splenocyte proliferation and when Concanavalin A (ConA) was used as a stimulus for T lymphocytes and lipopolysaccharide (LPS) as a stimulus for B lymphocytes, the peptides stimulated cell proliferation in a dose‐dependent manner. Of these, EF, AGGF, and BSFP promoted the proliferation of T lymphocytes; EF, EHPT, and BSFP significantly promoted the proliferation of B lymphocytes. EHPT and BSFP increased the CD4+/CD8+ ratio of T cells. Needle aspiration of neutral red was significantly promoted by macrophages treated with peptides other than EF. In addition, EEL, EHPT, AGGF, and BSFP had a promotive effect on NO production in phagocytes. The results indicate that BSFP is a peptide product with good immunomodulatory functions, four peptides identified from BSFP show outstanding effects in terms of antioxidant properties and immunomodulation. Practical applications: In this study, the amino acid composition and relative molecular masses of the black‐bone silky fowl peptide were analyzed, while the four peptides with significant effects on antioxidant and immunomodulatory properties in black‐bone silky fowl peptide were identified by HPLC‐MS/MS technique. Positive effects of black‐bone silky fowl peptide and its four peptides on antioxidant capacity and immunomodulatory ability as revealed by cell experiments. The results of this experiment provide a preliminary theoretical basis for the development of new functional foods using black‐bone silky fowl peptide and their characteristic peptides. [ABSTRACT FROM AUTHOR]

Pheretima vulgaris has been prescribed for the treatment of cardiovascular diseases in China for several hundred years in the form of dried powder in the clinic. However, the peptides with the potential antithrombotic activity of this source have never been reported. The total active proteins from Pheretima vulgaris were hydrolyzed by eight different commercial proteases and the alcalase hydrolysate showed the strongest thrombolytic activity. Four original thrombolytic peptides were isolated and characterized using bioactivity‐directed fractionation of the active hydrolysate. The amino acid sequences were identified as HEPLPEP (m/z 818.40076), EYPLPEP (m/z 844.39648), LGEPSVP (m/z 698.39648), and LLAPP (m/z 510.28043) by nanoLC‐ESI‐Orbitrap mass spectrometry with PEAKS software. HEPLPEP and EYPLPEP, containing the common ‐PLPEP residue, showed superior thrombolytic activity in plasmin assay and fibrinogen‐thrombin time assay. This research confirmed that Pheretima vulgaris was a potential source of active peptides with thrombolytic activities and provided novel candidates for the thrombolytic agents. Practical applications: Thrombosis has become the leading cause of mortality as it was the common underlying pathology of cardiovascular diseases, such as ischemic heart disease, and stroke. The demand for thrombolytics has increased gradually as the incidence trends of thrombosis‐related diseases raise with the aging of the population. Four novel thrombolytic peptides were characterized from Pheretima vulgaris proteins hydrolysates, among which HEPLPEP and EYPLPEP could prevent the formation of thrombus and degrade existing thrombus in vitro. These peptides are promising to be meritorious templates for developing thrombolytic agents. The structure–function relationship of peptides resulting from the presence of specific residues in these sequences may contribute to extending the knowledge about their thrombolytic activity, which may be useful in designing novel thrombolytic agents. The present research based on a bioactivity‐directed isolation strategy could also be applied to other animal‐derived traditional Chinese medicines with proteins or peptides as their function basis. [ABSTRACT FROM AUTHOR]

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named "DNAPred_Prot", which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins. [ABSTRACT FROM AUTHOR]

Background. Malaria is still a public health problem in Saudi Arabia specifically in the Jazan region. Plasmodium falciparum knob-associated histidine-rich proteins (PfKAHRPs) play an important role in cerebral malaria pathophysiology as well as pathogenesis of P. falciparum infections. The repeat region of PfKAHRP C-terminal interaction domain has been found to bind to the infected red blood cells and the vascular endothelium. Thus, this study aimed to assess the allelic variations, genetic diversity, and natural selection acting at the C-terminal PfKAHRP between parasite isolates from Saudi Arabia. Materials and Methods. The PfKHARP C-terminal interaction domain was successfully PCR-amplified and sequence data from 441 clinical isolates from Saudi Arabia were obtained. The DnaSP v5.10 software was used to determine the genetic diversity, polymorphism, haplotype, and natural selection. Haplotype network analysis was constructed by using the median-joining method in the NETWORK version 5.0.0.1 software. Results. Alignment and analysis of 441 C-terminal PfKAHRP-deduced amino acid sequences identified 5 genotypes (I–V) based on the decapeptide repeat arrangements (TKEASTSKEA, TKEASTSKGA, TKEASTTEGA, and TKEASTSKRA). Among the repeat types, Type I (49.43%, 218/441) was the most abundant in Saudi Arabia, followed by Type II (48.29%, 213/441). Overall, the nucleotide diversity in the PfKHARP C-terminal region was found to be low in Saudi Arabia (π = 0.00142); however, natural selection tests indicated positive selection (dN-dS = 1.64, P < 0.05) which was due to the variations within the repeat motifs. Genealogical relationship haplotype network of PfKAHRP from 4 different countries (i.e., Saudi Arabia, Iran, Burundi, and India) revealed 1 major shared haplotype cluster (H_1) with samples representative from all 4 countries (Saudi Arabia; n = 441, Burundi; n = 4, Iran; n = 13, and India; n = 1). Conclusion. Since this is the first study to report on genetic diversity of C-terminal PfKAHRP interaction domain and the repeat motifs from clinical samples in Saudi Arabia, it will contribute towards the rational design of antiadhesion drug therapies for P. falciparum malaria. [ABSTRACT FROM AUTHOR]

The reversible black shell colour change in the clam Cyclina sinensis functions as an adaptation to different environmental conditions (within or outside of muddy sediment) due to changes in the melanin redox state in the shell. The microphthalmia‐associated transcription factor (MITF) is a melanocyte‐specific transcription factor that is involved in the differentiation, proliferation and survival of melanocytes. In this study, the full‐length cDNA sequence of the mitf‐like gene was obtained from C. sinensis using RACE PCR. The mitf‐like cDNA is 1725 bp in length, containing a 47 bp 5′ untranslated region (UTR), a 1398 bp open reading frame and a 280 bp 5′ untranslated region. The mitf‐like gene contains seven exons separated by six introns, and it encodes a putative protein of 465 amino acids with a highly conserved domain of MITF containing a basic helix‐loop‐helix leucine zipper (bHLH‐LZ). The phylogenetic analysis of amino acid sequences using neighbour‐joining and maximum likelihood methods revealed that C. sinensis is closely related to Meretrix petechialis. Additionally, the mRNA expression of mitf‐like and melanin was high in the outer margin of the mantle, suggesting that the mitf‐like might play an important role in melanin synthesis in the mantle by regulating the cell cycle and survival of melanocytes; however, its role in C. sinensis shell melanin deposition requires further investigation. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the protein sequence through the selected properties. The shorter windows reflect the characteristics of the central residue, and the longer windows reflect the characteristics of the surroundings around the central residue. Moreover, to highlight the specificity of sequence and evolutionary properties, they are preprocessed, respectively. After that, the preprocessed properties are combined into feature matrices as the input of the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict IDPs effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks. [ABSTRACT FROM AUTHOR]