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Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings. [ABSTRACT FROM AUTHOR]

This paper outlines methods used to identify novel antigens for use in the development of serological assays. Specifically, we applied these methods to a neurogenic parasitic nematode of cervids called Parelaphostrongylus tenuis. This parasite is of particular concern in both wild and domestic ungulates as it causes significant neurological signs and definitive diagnosis is only possible post-mortem, necessitating the development of serologic assays for antemortem diagnosis. Proteins extracted from P. tenuis organisms were affinity isolated using antibodies enriched from seropositive moose (Alces alces). The proteins were analyzed using mass spectrometry and liquid chromatography to obtain amino acid sequences that were then cross-referenced to open reading frames predicted from an assembled transcriptome. An antigen of interest was assessed for immunogenic epitopes and subsequently synthesized into 10-mer synthetic overlapping peptides representing these regions. These synthetic peptides were then assessed for reactivity against positive and negative moose sera and demonstrated potential use as a serological assay in diagnostic laboratories. Known negative moose sera revealed significantly lower optical density when compared to the positive samples (p < 0.05). This method serves as a pipeline for the construction of diagnostic assays of pathogens in both human and veterinary medicine. [ABSTRACT FROM AUTHOR]

Jiangxi is one of the provinces in China most seriously affected by the haemorrhagic fever with renal syndrome (HFRS) epidemic. The aim of this paper was to systematically explore the HFRS epidemic in Jiangxi from the perspective of Hantavirus (HV) prevalence in rodents and humans and virus molecular characteristics. Individual information on all HFRS cases in Jiangxi from 2005 to 2021 was extracted from the China Information System for Disease Control and Prevention. All S and M fragment sequences of the Seoul virus and Hantan virus strains uploaded by Jiangxi and its neighbouring provinces and some representative sequences from provinces in China or some countries of Southeast Asia with the highest HV prevalence were retrieved and downloaded from NCBI GenBank. Periodogram and spatial autocorrelation were adopted for temporal periodicity and spatial clustering analysis of the HFRS epidemic. Joinpoint regression was utilized to explore the changing morbidity trend patterns of HFRS. Multiple sequence alignment and amino acid variation analysis were used to explore the homology and variation of strain prevalence in Jiangxi. Based on monthly morbidity time series, the periodogram analysis showed that the prevalence of HFRS had periodicities of 6 months and 12 months. Spatial autocorrelation analysis showed that HFRS distributed in Jiangxi was not random, with a "High-High" clustering area around Gaoan County. HFRS morbidity among the 0 ~ 15-year-old and ~ 61-year-old or older populations in Jiangxi increased significantly during the period of 2008–2015. Generally, HFRS morbidity was significantly positively correlated with the index of rat with virus (IRV) (r = 0.742) in the counties surrounding Gaoan from 2005 to 2019. HTNV strains in Jiangxi were in one independent branch, while the SEOV strains in Jiangxi were relatively more diverse. Both the YW89-15 and GAW30/2021 strains shared approximately 85% nucleotide homology and approximately 97% amino acid homology with their corresponding standard strains and vaccine strains. GAW30/2021 and YW89-15 had some amino acid site variations in nucleoprotein, glycoprotein precursor and RNA-dependent polymerase with their corresponding vaccine strains Z10 (HTNV) and Z37 (SEOV). The HFRS epidemic in Jiangxi has obvious temporal periodicity and spatial clustering, and the significant increase in the non-Immunization Expanded Program (EPI) targeted population (children and elderly) suggests that HFRS vaccination in this population needs to be considered. Although applying the EPI played a certain role in curbing the incidence of HFRS in Jiangxi from the perspective of ecological epidemiology, HTNV and SEOV strains prevalent in Jiangxi have some amino acid site variations compared to their corresponding vaccine strains, suggesting that HV variation needs to be continuously monitored in the future to observe vaccine protective efficiency. [ABSTRACT FROM AUTHOR]

Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the protein sequence remain challenging. To deal with these challenges, state-of-the-art inter-residue distance prediction algorithms have used large sets of coevolutionary and non-coevolutionary features. In this paper, we argue that the more the types of features used, the more the kinds of noises introduced and then the deep learning model has to overcome the noises to improve the accuracy of the predictions. Also, multiple features capturing similar underlying characteristics might not necessarily have significantly better cumulative effect. So we scrutinise the feature space to reduce the types of features to be used, but at the same time, we strive to improve the prediction accuracy. Consequently, for inter-residue real distance prediction, in this paper, we propose a deep learning model named scrutinised distance predictor (SDP), which uses only 2 coevolutionary and 3 non-coevolutionary features. On several sets of benchmark proteins, our proposed SDP method improves mean Local Distance Different Test (LDDT) scores at least by 10% over existing state-of-the-art methods. The SDP program along with its data is available from the website https://gitlab.com/mahnewton/sdp. [ABSTRACT FROM AUTHOR]

Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction [ABSTRACT FROM AUTHOR]

Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticidal products for agricultural use, including transgenic crops. There is an urgent need to identify novel pore-forming proteins to combat development of resistance in pests to existing products, and to develop products that are effective against a broader range of pests. Existing computational methodologies to search for these proteins rely on sequence homology-based approaches. These approaches are based on similarities between protein sequences, and thus are limited in their usefulness for discovering novel proteins. In this paper, we outline a novel deep learning model trained on pore-forming proteins from the public domain. We compare different ways of encoding protein information during training, and contrast it with traditional approaches. We show that our model is capable of identifying known pore formers with no sequence similarity to the proteins used to train the model, and therefore holds promise for identifying novel pore formers. [ABSTRACT FROM AUTHOR]