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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Jul 19, 2025

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Structure-Informed Protein Language Models are Robust Predictors for Variant Effects.

Yuanfei Sun1, Yang Shen1,2,3

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, Texas, USA.

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|August 14, 2023
PubMed
Summary
This summary is machine-generated.

Structure-informed protein language models (SI-pLMs) improve variant effect prediction by incorporating structural context. This approach enhances accuracy without needing experimental labels, overcoming limitations of sequence-only models.

Keywords:
Variant effect predictionmultimodal machine learning.protein language modelsprotein sequencesprotein structures

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Protein Engineering

Background:

  • Predicting protein variant effects is crucial but limited by scarce experimental data.
  • Existing protein language models (pLMs) predict variant effects without labels but marginalize biological context.
  • Sequence and structure awareness in pLMs impact prediction accuracy, with tradeoffs observed in over-finetuning.

Conclusions:

  • Structure-informed pLMs effectively inject and utilize protein structural contexts for enhanced variant effect prediction.
  • SI-pLMs offer a robust and versatile approach, overcoming limitations of sequence-only models.
  • The integration of structural information improves the predictive power of protein language models.