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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Advancing generative large language models toward discriminative performance in protein function prediction.

Ying Lv1, Yifan Xu2, Gang Xu3

  • 1Shanghai AI Laboratory, Shanghai, 200030, China.

Genome Biology
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

We developed OPUS-PLLM, a new large language model (LLM) for protein function prediction. This generative model surpasses specialized models, unlocking new potential for biological LLMs.

Keywords:
Generative large language modelProtein function predictionProtein language model

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Artificial intelligence in life sciences

Background:

  • Generative large language models (LLMs) show promise but have limited application in protein function prediction.
  • Existing biological LLMs are often benchmarked against general models, overlooking gaps with specialized discriminative models.

Purpose of the Study:

  • To introduce OPUS-PLLM, a multitask generative LLM for protein function prediction.
  • To establish a novel sequence-to-function paradigm using natural language generation.

Main Methods:

  • Developed OPUS-PLLM with modality encoding, refinement, and instruction tuning.
  • Created two specialized datasets: OPUS-InstructionCorpus and OPUS-InstructionCorpus-Evol.
  • Trained the model on six protein functional annotations.

Main Results:

  • OPUS-PLLM demonstrated superior performance across five core protein function prediction tasks (18 benchmarks).
  • The model outperformed existing biological LLMs and specialized discriminative models in most evaluations.

Conclusions:

  • Highlights the significant, yet untapped, potential of LLMs for protein function prediction.
  • Provides a robust, scalable, and generalizable framework for developing biological LLMs.