Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis

Ibtehaz, Nabil; Sourav, Soumya; Bayzid, Md. Shamsuzzoha; Rahman, Mizanur

doi:10.1007/s10930-023-10096-7

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Hence, fully connected layers were selected for the input heads. The word size for skip-gram embedding was set at three amino acids based on a number of prior works ( Phloyphisut et al 2019 , Khanal et al 2020 , He et al 2021 , Ibtehaz et al 2023 ), and the fact that the input peptide lengths for this task are quite short.…”

Section: Methodsmentioning

confidence: 99%

MHCSeqNet2—improved peptide-class I MHC binding prediction for alleles with low data

Wongklaew,

Sriswasdi,

Chuangsuwanich

2023

Bioinformatics

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Motivation The binding of a peptide antigen to a class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient’s immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. Results In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. Availability and implementation The source code and installation instruction for MHCSeqNet2 is available at https://github.com/cmb-chula/MHCSeqNet2 Supplementary information Supplementary data are available at Bioinformatics online.

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