Biology subjects

Bahai, A.

Publications and source records attributed to Bahai, A..

3 recordsLinked to original sources

bioRxiv2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

bioRxiv2024

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. Our aim is to identify factors influencing performance variation, such as RNA family diversity, sequence length, RNA type, multiple sequence alignment (MSA) quality, and deep learning model architecture. We show that generally ML-based methods perform much better than non-ML methods on most RNA targets, although the performance difference isnt substantial when working with unseen novel or synthetic RNAs. The quality of the MSA and secondary structure prediction both play an important role and most methods arent able to predict non-Watson-Crick pairs in the RNAs. Overall, DeepFoldRNA has the best prediction followed by DRFold as the second best method. Finally, we also suggest possible mitigations to improve the quality of the prediction for future method development.

bioinformatics↗

bioRxiv2020

EpitopeVec: Linear Epitope Prediction Using DeepProtein Sequence Embeddings

MotivationB-cell epitopes (BCEs) play a pivotal role in the development of peptide vaccines, immunodiagnostic reagents, and antibody production, and thus generally in infectious disease prevention and diagnosis. Experimental methods used to determine BCEs are costly and time-consuming. It thus becomes essential to develop computational methods for the rapid identification of BCEs. Though several computational methods have been developed for this task, cross-testing of classifiers trained and tested on different datasets revealed their limitations, with accuracies of 51 to 53%. ResultsWe describe a new method called EpitopeVec, which utilizes residue properties, modified antigenicity scales, and a Protvec representation of peptides for linear BCE prediction with machine learning techniques. Evaluating on several large and small data sets, as well as cross-testing demonstrated an improvement of the state-of-the-art performances in terms of accuracy and AUC. Predictive performance depended on the type of antigen (viral, bacterial, eukaryote, etc.). In view of that, we also trained our method on a large viral dataset to create a linear viral BCE predictor. AvailablityThe software is available at https://github.com/hzi-bifo/epitope-prediction under the GPL3.0 license. Contactalice.mchardy@helmholtz-hzi.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗