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Status: Unchecked ×Keyword: protein localization ×Clear all

6 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

   The authors trained six language models on huge protein sequence sets and showed their embeddings, used alone, could predict protein structure and location, with the best beating methods that need sequence alignments.

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   1. UncheckedSimplifying protein language model embeddings from unlabelled sequences showed they captured some biophysical features of proteins.“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. UncheckedProtein language model embeddings alone, used as input, predicted secondary structure, cell location and membrane status with the stated accuracies.“We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=…”
   3. UncheckedThe best ProtTrans embeddings (ProtT5) predicted protein secondary structure better than the prior best method without alignments or evolutionary information.“For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

   Thumuluri, Armenteros, Johansen, Nielsen and Winther · Nucleic Acids Research · 2022

   The authors update the DeepLoc tool to predict multiple subcellular locations per protein, using a protein language model, with better performance and interpretability, and release it as a webserver.

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   1. UncheckedDeepLoc 2.0, which uses a pre-trained protein language model, reports state-of-the-art performance in predicting where proteins sit in cells.“We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model.”
   2. UncheckedIn DeepLoc 2.0, the model's attention output along a protein sequence lines up well with where sorting signals are located.“We find that the attention output correlates well with the position of sorting signals.”

3. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2020

   The authors trained six language models on huge protein sequence sets and found their embeddings helped predict structure and localization, with ProtT5 beating the state of the art on per-residue tasks without evolutionary information.

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   1. UncheckedReducing the dimensions of raw protein language-model embeddings showed they captured some biophysical features of protein sequences, without labels.“Dimensionality reduction revealed that the raw protein LM- embeddings from unlabeled data captured some biophysical features of protein sequences.”