Biology subjects

Ruengjitchatchawalya, M.

Publications and source records attributed to Ruengjitchatchawalya, M..

3 recordsLinked to original sources

bioRxiv2021

Immunodominant and neutralizing linear B cell epitopes spanning the spike and membrane proteins of Porcine Epidemic Diarrhea Virus

Porcine Epidemic Diarrhea Virus (PEDV) is the causative agent of PED, an enteric disease that causes high mortality rates in piglets. PEDV is an alphacoronavirus that has high genetic diversity. Insights into neutralizing B cell epitopes of all genetically diverse PEDV strains are of importance, particularly for designing a vaccine that can provide broad protection against PEDV. In this work, we aimed to explore the landscape of linear B cell epitopes on the spike (S) and membrane (M) proteins of global PEDV strains. All amino acid sequences of the PEDV S and M proteins were retrieved from the NCBI database and grouped. Immunoinformatics-based methods were next developed and used to identify putative linear B cell epitopes from 14 and 5 consensus sequences generated from distinct groups of the S and M proteins, respectively. ELISA testing predicted peptides with PEDV-positive sera revealed 9 novel immunodominant epitopes on the S protein. Importantly, 7 of these novel immunodominant epitopes and other subdominant epitopes were demonstrated to be neutralizing epitopes by neutralization-inhibition assay. Additionally, our study shows the first time that M protein is also the target of neutralizing antibodies as 7 neutralizing epitopes in the M protein were identified. Conservancy analysis revealed that epitopes in the S1 subunit are more variable than those in the S2 subunit and M protein. In this study, we offer the immunoinformatics approach for linear B cell epitope identification and a more complete profile of linear B cell epitopes across the PEDV S and M proteins, which may contribute to the development of a greater PEDV vaccine as well as peptide-based immunoassays.

immunology↗

bioRxiv2020

PhotoModPlus: A webserver for photosynthetic protein prediction from a genome neighborhood feature

Identification of photosynthetic proteins and their functions is essential for understanding and improving photosynthetic efficiency. We present here a new webserver called PhotoModPlus as a platform to predict photosynthetic proteins via genome neighborhood networks (GNN) and a machine learning method. GNN facilitates users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance to the query input. We also integrated a newly developed machine learning model for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO, into the webserver. The new model was developed using a multi-label classification approach and genome neighborhood features. The performance of the new model was up to 0.872 of F1 measure, which was better than the sequence-based approaches evaluated by nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server was user-friendly designed and compatible with all devices and available at http://bicep.kmutt.ac.th/photomod or http://bicep2.kmutt.ac.th/photomod.

bioinformatics↗

bioRxiv2020

Photosynthetic protein classification using genome neighborhood-based machine learning feature

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genomic context such as genome neighborhood can provide additional useful information to identify the photosynthetic proteins. We, therefore, expected that applying the computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their genomic neighbors, indicating the possibility to assign functions from their genome neighborhood profile. Therefore, we created a new method for extracting the patterns based on genome neighborhood network (GNN) and applied for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 94% in the classification of photosynthetic proteins and also showed better performance (Mathews correlation coefficient = 0.852) than other available tools including the sequence similarity search (0.497) and ML-based method (0.512). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins comparing to the other methods. Our classifier is available at http://bicep.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod

bioinformatics↗