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Keyword: secondary structure prediction ×Clear all

13 claims from 6 papers

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

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · Proceedings of the National Academy of Sciences · 2021

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   1. Unchecked“We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity.”
   2. Unchecked“Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction.”

2. 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. Unchecked“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.”

3. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Evaluation and improvement of multiple sequence methods for protein secondary structure prediction

   Cuff and Barton · Proteins Structure Function and Bioinformatics · 1999

   The paper builds a 396-domain dataset to compare four secondary structure predictors, tests a simple consensus method, examines 8- to 3-state reductions, and derives two new datasets.

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   1. UncheckedThe authors derive two new protein sequence datasets, CB513 and CB251, for cross-validating secondary structure prediction without artifacts from internal homology.“Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross-validation of secondary structure prediction methods without artifacts due to internal homology.”
   2. Unchecked“A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%.”
   3. Unchecked“Application of the different published 8- to 3-state reduction methods shows variation of over 3% on apparent prediction accuracy.”

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

   Modeling aspects of the language of life through transfer-learning protein sequences

   Heinzinger, Elnaggar, Wang et al. · BMC Bioinformatics · 2019

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   1. Unchecked“At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches.”
   2. Unchecked“Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s.”

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

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019

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   1. Unchecked“The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins.”
   2. Unchecked“Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.”

6. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   ProGen: Language Modeling for Protein Generation

   Madani, Bryan, Naik et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.”