# Training self-supervised peptide sequence models on artificially chopped proteins

arXiv: https://arxiv.org/abs/2211.06428 · HTML: https://mlsanity.com/paper/2211.06428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [Learning protein sequence embeddings using information from structure](https://mlsanity.com/paper/1902.08661/) (2019)
- [What is a meaningful representation of protein sequences?](https://mlsanity.com/paper/2012.02679/) (2020)
- [On Losses for Modern Language Models](https://mlsanity.com/paper/2010.01694/) (2020)
- [Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model](https://mlsanity.com/paper/2110.15527/) (2021)
- [Symmetric Regularization based BERT for Pair-wise Semantic Reasoning](https://mlsanity.com/paper/1909.03405/) (2019)

## Abstract (arXiv)

Representation learning for proteins has primarily focused on the global understanding of protein sequences regardless of their length. However, shorter proteins (known as peptides) take on distinct structures and functions compared to their longer counterparts. Unfortunately, there are not as many naturally occurring peptides available to be sequenced and therefore less peptide-specific data to train with. In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins". We evaluate the representation potential of models trained with chopped proteins versus natural peptides and find that training language models with chopped proteins results in more generalized embeddings for short protein sequences. These peptide-specific models also retain information about the original protein they were derived from better than language models trained on full-length proteins. We compare masked language model training objectives to three novel peptide-specific training objectives: next-peptide prediction, contrastive peptide selection and evolution-weighted MLM. We demonstrate improved zero-shot learning performance for a deep mutational scan peptides benchmark.