STAT115 Chapter 2.1 Protein Wave

About this video

• Video Title: STAT115 Chapter 2.1 Protein Wave
• Channel: Xiaole Shirley Liu
• Speakers: Xiaole Shirley Liu
• Duration: 00:15:31

Overview

This video provides a historical overview of bioinformatics, focusing on the "protein wave." It explains the evolution of protein sequencing, structure prediction, and the development of computational tools and databases to manage and analyze this data. The speaker highlights key milestones like Sanger's sequencing of insulin, the creation of the Protein Data Bank, the development of sequence alignment algorithms, the CASP competition for structure prediction, and the recent advancements with AI, particularly AlphaFold.

Key takeaways

• Early Bioinformatics (Protein Sequencing): The first wave of bioinformatics was driven by the ability to sequence proteins, starting with Fred Sanger's work on insulin in 1955. This led to the need for computational algorithms for pairwise sequence alignment to compare newly sequenced proteins with existing ones.
• Data Management and Databases: The development of the Protein Data Bank (PDB) in 1973 was crucial for storing solved protein structures. Later, databases for protein sequences emerged, enabling faster searching using algorithms like BLAST to infer function and structure from known similar sequences.
• Protein Structure Prediction: Predicting protein structure from sequence became a major focus. The Critical Assessment of Structure Prediction (CASP) competition, established in 1994, provided an unbiased platform to evaluate different prediction methods.
• Advancements in Prediction: Initially, predicting protein structure was challenging. However, over time, methods improved, with significant breakthroughs in the 2010s. The introduction of AI, specifically DeepMind's AlphaFold, dramatically advanced the accuracy of protein structure prediction, with AlphaFold 2 achieving near-experimental accuracy by 2020.
• Current Challenges: While single protein structure prediction is largely solved with AI, challenges remain in predicting complex molecular structures and protein-protein interactions.