Multi-epitope Peptide Vaccine Prediction for Candida albicans targeting Pyruvate Kinase Protein; an Immunoinformatics Approach

sati, a. o. m.; abdelmoneim, a. h.; Dawoud, E.; abbas, t.; Abdelrahman, F.; Saad, k.; Abduallah, r.; musrafa, m.; Sidahmed, t.; Elhassan, R.; Hassan, M.

doi:10.1101/758920

bioRxiv · 10.1101/758920

Multi-epitope Peptide Vaccine Prediction for Candida albicans targeting Pyruvate Kinase Protein; an Immunoinformatics Approach

sati, a. o. m.·abdelmoneim, a. h.·Dawoud, E.·abbas, t.·Abdelrahman, F.·Saad, k.·Abduallah, r.·musrafa, m.·Sidahmed, t.·Elhassan, R.·Hassan, M.

View original source ↗Export metadata ↓

Abstract

The fungus Candida albicans is an opportunistic pathogen that causes a wide range of infections. Its the primary cause of candidiasis and the fourth most common cause of nosocomial infection. In addition, disseminated invasive candidiasis which is a major complication of the disease has an estimated mortality rate of 40%-60% even with the use of antifungal drugs. Over the last decades, several different anti-Candida vaccines have been suggested with different strategies for immunization against candidiasis such as, live-attenuated fungi, recombinant proteins, and glycoconjugates but none has been approved by the FDA, yet. This study aims to introduce a new possible vaccine for C. albicans through analyzing peptides of its pyruvate kinase (PK) protein as an immunogenic stimulant computationally.\n\nA total number of 28 C. albicans, pyruvate kinase proteins were obtained from NCBI on the 9th of February 2019 and were subjected to multiple sequence alignment using Bioedit for conservancy. The main analytical tool was IEDB, Chimera for homology modelling, and MOE for docking.\n\nAmong the tested peptides, fifteen promising T-cell peptides were predicted. Five peptides were more important than the others (HMIFASFIR, YRGVYPFIY, AVAAVSAAY, LRWAVSEAV, and IFASFIRTA) They show high Binding Affinity to MHC molecules, low binding energy required indicating more stable bonds, and their ideal length of nine peptides. (PTRAEVSDV) peptide is the most promising linear B-cell peptide due to its physiochemical parameters and optimal length (nine amino acids). Its highly recommended to have these five strong candidates in future in vivo and in vitro analysis studies.

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

sati, a. o. m., abdelmoneim, a. h., Dawoud, E., abbas, t., Abdelrahman, F., Saad, k., Abduallah, r., musrafa, m., Sidahmed, t., Elhassan, R., Hassan, M.. 2019-09-05. Multi-epitope Peptide Vaccine Prediction for Candida albicans targeting Pyruvate Kinase Protein; an Immunoinformatics Approach. https://doi.org/10.1101/758920

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

bioRxiv2026

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗

bioRxiv2026

Plate design confounds donor variation and model evaluation in single-cell perturbation screens

Single-cell screens of immune cells from many donors are used to study why individuals respond differently and to benchmark response models. Each donor-perturbation combination typically occupies one well, controls are grouped, and donors share one layout, so technical effects can look biological. Using a 90-cytokine screen, two compound screens, four cohorts and simulations, we show that well effects explain most replicate-control variation in abundant cell types, controls carry a shared offset, and identical control wells differ by hundreds of differentially expressed genes at a standard threshold, about as many as two receptor-negative proteins do. Negative controls, design-aware estimators and perturbation-specific evaluation change the interpretation of four recent analyses. A deep-learning model that scores below a perturbation-agnostic predictor under standard evaluation outperforms a simple baseline on perturbation-specific differences in two of three cell types (significantly in one with stricter controls), but predicts less well the component shared with inert wells.

bioinformatics↗