Protein codes promote selective subcellular compartmentalization

Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Cells have evolved mechanisms to distribute ~10 billion protein molecules subcellular compartments where diverse proteins involved in shared functions must assemble. Here, we demonstrate that with share amino acid sequence codes guide them compartment destinations. A language model, ProtGPS, was developed predicts high performance the localization of human excluded from training set. ProtGPS successfully guided generation novel sequences selectively assemble nucleolus. identified pathological mutations change this code and lead altered proteins. Our results indicate contain not only a folding code, but also previously unrecognized governing their distribution compartments.

Language: Английский

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Biophysics-based protein language models for protein engineering

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 17, 2024

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these overlook decades of research into biophysical factors governing We propose Mutational Effect Transfer Learning (METL), a model framework that unites advanced machine learning modeling. Using the METL framework, we pretrain transformer-based neural networks simulation to capture fundamental relationships between energetics. finetune experimental sequence-function harness signals apply them when predicting properties like thermostability, catalytic activity, fluorescence. excels in challenging engineering tasks generalizing from small training sets position extrapolation, although existing methods train remain many types assays. demonstrate METL's ability design functional green fluorescent variants only 64 examples, showcasing potential biophysics-based engineering.

Language: Английский

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Challenges and compromises: Predicting unbound antibody structures with deep learning

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 90, P. 102983 - 102983

Published: Jan. 24, 2025

Language: Английский

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Teaching AI to speak protein

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 91, P. 102986 - 102986

Published: Feb. 21, 2025

Language: Английский

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Computational protein design

Nature Reviews Methods Primers, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 27, 2025

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ProtSpace: a tool for visualizing protein space

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 168940 - 168940

Published: Jan. 1, 2025

Protein language models (pLMs) generate high-dimensional representations of proteins, so called embeddings, that capture complex information stored in the set evolved sequences. Interpreting these embeddings remains an important challenge. ProtSpace provides one solution through open-source Python package visualizes protein interactively 2D and 3D. The combination embedding space with 3D structure view aids discovering functional patterns readily missed by traditional sequence analysis. We present two examples to showcase ProtSpace. First, investigations phage data sets showed distinct clusters major groups a mixed region, possibly suggesting bias today's sequences used train pLMs. Second, analysis venom proteins revealed unexpected convergent evolution between scorpion snake toxins; this challenges existing toxin family classifications added evidence refuting aculeatoxin hypothesis. is freely available as pip-installable (source code & documentation) on GitHub (https://github.com/tsenoner/protspace) web interface (https://protspace.rostlab.org). platform enables seamless collaboration portable JSON session files.

Language: Английский

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Major advances in protein function assignment by remote homolog detection with protein language models – A review

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 90, P. 102984 - 102984

Published: Jan. 27, 2025

Language: Английский

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Deep Learning for Antimicrobial Peptides: Computational Models and Databases

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of is both time-consuming and laborious. In recent years, development computational technologies (especially deep learning) has provided new opportunities for peptide prediction. Various models have been proposed predict peptide. this review, we focus on learning We first collected summarized available data resources peptides. Subsequently, existing discussed their limitations challenges. This study aims help biologists design better

Language: Английский

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Integrating protein language models and automatic biofoundry for enhanced protein evolution

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 11, 2025

Language: Английский

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Toward deep learning sequence–structure co-generation for protein design

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 91, P. 103018 - 103018

Published: Feb. 20, 2025

Language: Английский

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