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Understanding Proteins Through Language Models: A Revolution in Biological Research

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Dr. Jessica Nelson
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Understanding Proteins Through Language Models: A Revolution in Biological Research

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Protein and Language: A Unique Relationship

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Proteins, the integral building blocks of life, are composed of a linear chain of residues, with 20 canonical amino acids serving as the 'words' in the language of most natural proteins. Understanding the relationships between protein sequence, structure, and function is a critical focus of biological research. This understanding has been significantly enhanced by the development of Protein Language Models (PLMs). These models are trained on vast datasets of protein sequences, enabling them to learn the intricacies of protein structure and function. The ultimate goal is to foster a wide range of protein modeling and design tasks.

The Role of High-Throughput DNA Sequencing

Significant advances in high-throughput DNA sequencing technology have enabled the collection of billions of protein sequences. This wealth of data allows researchers to identify patterns underlying the evolutionary process. Structural and functional constraints on protein sequences, referred to as coevolution, have led to considerable advances in protein modeling and structure prediction.

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Impact of PLMs on Protein Property and Structure Prediction

Large pretrained Protein Language Models (PLMs) have had a profound impact on protein property and structure prediction through transfer learning. Research has shown that while most tasks benefit from pretrained models, performance does not necessarily scale with pretraining. This indicates a need for the development of more effective pretraining methods.

Protein Language Models Revolutionizing Research

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The creation of PLMs has transformed protein structure and function research. These models have enabled researchers to predict protein structures and functions with astonishing accuracy. Beyond this, PLMs have found applications in diverse areas such as drug discovery, protein engineering, and personalized medicine, leading to significant advancements in these fields.

Artificial Intelligence and Protein Structure Prediction

Recent progress in protein tertiary structure prediction has seen the rise of artificial intelligence algorithms and the deep learning method AlphaFold2. These technologies offer various methodologies, assessments, and databases in protein structure prediction. These include traditionally used protein structure prediction methods, such as template-based modeling and template-free modeling approaches, and recently developed deep learning-based methods, such as contact/distance-guided methods and end-to-end folding methods.

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Generative Models for Protein Engineering

Generative models like ChatGPT and DALL-E2 offer another dimension in protein engineering. These models are useful for protein structure and sequence generation, and their applications could significantly impact protein engineering research.

New Avenues for Therapeutic Design

Protein language models, such as GPCR BERT, have opened up exciting new avenues for the design and understanding of therapeutics. GPCR BERT was developed to understand the sequential design of G protein-coupled receptors (GPCRs), which are the target of over one third of FDA-approved pharmaceuticals. The model utilized the pretrained protein model Prot Bert and fine-tuned with prediction tasks of variations in the motifs NPxxY, CWxP, and E DRY. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs, leading to significant advancements in the understanding of therapeutics.

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