Lopez Carranza, S. N. | bioRxiv Science

bioRxiv2026

BenchMHC: a reproducible framework for peptide-MHC presentation prediction

Predicting which peptides an MHC molecule presents is the first step in neoantigen and epitope discovery pipelines. Most published predictors are released for inference only, and each is trained on a different dataset, so the effect of a new method cannot be separated from the effect of its training data. We present BenchMHC, an open framework that reproduces, retrains and evaluates pMHC presentation models on public data only, and that records every implementation decision made in a reproduction. Our NetMHCpan-4.1 reproduction reaches 0.794 per-allele Mean Top-K against 0.790 for the official webserver, and 0.0025 Median FRANK against 0.0022. It scores 280,000 peptide/allele pairs in 27 s, about 350 times faster than the original binary. We then examine the training data. In the public NetMHCpan-4.1 eluted-ligand sets, 86 to 99% of hits are found in the human proteome, against about 12% of their decoys, so a model can separate hits from decoys using this difference rather than features related to MHC binding. Redrawing the decoys within the source protein of each hit and matching their cysteine status, with no other change, raises per-allele Mean Top-K on the regenerated v2.0.0 evaluation set from 0.734 to 0.755, and increases Median FRANK on CD8 epitopes from 0.0025 to 0.0053. The same change improves one evaluation set and degrades the other. Code (Apache-2.0) and model checkpoints (CC BY-NC-4.0): https://github.com/instadeepai/BenchMHC-public.

bioinformatics↗

bioRxiv2021

Early Computational Detection of Potential High Risk SARS-CoV-2 Variants

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose a significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects or increased transmissibility. Early detection of such high risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Here we present a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. These metrics can be combined into an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. With only the S protein nucleotide sequence as input, the EWS detects HRVs earlier and with better precision than baseline metrics such as the growth metric (which requires real-world observations) or random sampling. Notably, Omicron BA.1 was flagged by the EWS on the day its sequence was made available. Additionally, our immune escape and fitness metrics were experimentally validated using in vitro pseudovirus-based virus neutralisation test (pVNT) assays and binding assays. The EWS flagged as potentially dangerous all 16 variants (Alpha-Omicron BA.1/2/4/5) designated by the World Health Organisation (WHO) with an average lead time of more than one and a half months ahead of them being designated as such. One-Sentence SummaryA COVID-19 Early Warning System combining structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.

bioinformatics↗