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Related Concept Videos

Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Conservation of Protein Domains Over Different Proteins02:26

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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.
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Antibody Structure and Classes01:25

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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
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Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
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Unsupervised evolution of protein and antibody complexes with a structure-informed language model.

Varun R Shanker1,2,3, Theodora U J Bruun2,3,4, Brian L Hie3,4

  • 1Stanford Biophysics Program, Stanford University School of Medicine, Stanford, CA 94305, USA.

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Integrating protein structure into language models enhances protein design and evolution. This approach improved antibody therapies against SARS-CoV-2 variants, demonstrating a powerful method for protein engineering.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Structural biology

Background:

  • Large language models (LLMs) excel at learning protein design principles from sequence data alone.
  • Protein function, activity, and evolvability are critically determined by their three-dimensional structures, not just sequences.
  • Existing LLMs often lack the structural context necessary for comprehensive protein design.

Purpose of the Study:

  • To develop a general protein language model augmented with structural information to guide protein evolution.
  • To demonstrate the model's capability in engineering protein complexes and improving therapeutic antibodies.
  • To validate the effectiveness of structure-informed protein language models in enhancing protein function without task-specific training.

Main Methods:

  • Augmenting a general protein language model with protein structure backbone coordinates.
  • Extending the ESM-IF1 model, initially trained on single-chain structures, to engineer protein complexes.
  • Screening approximately 30 variants of two therapeutic antibodies against SARS-CoV-2.

Main Results:

  • The structure-augmented language model successfully guided protein evolution for diverse proteins.
  • The extended ESM-IF1 model enabled the engineering of protein complexes.
  • Significant improvements were observed in antibody neutralization (up to 25-fold) and affinity (up to 37-fold) against SARS-CoV-2 variants BQ.1.1 and XBB.1.5.

Conclusions:

  • Integrating structural information into protein language models is advantageous for identifying efficient protein evolution pathways.
  • This approach allows for protein engineering without the need for task-specific training data.
  • The findings pave the way for improved protein design and therapeutic development, particularly for antibodies targeting viral escape variants.