Exploring Efficient Approaches to Sequence Alignment

This chapter examines the complexities of multiple sequence alignment (MSA) and its importance in identifying conserved biological regions. It also addresses the computational challenges of MSA and explores innovative solutions like embedding spaces and indexed vector databases to optimize alignment efficiency.

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Nathan welcomes back computational biochemist Amelie Schreiber for a fascinating update on AI's revolutionary impact in biology. In this episode of The Cognitive Revolution, we explore recent breakthroughs including AlphaFold3, ESM3, and new diffusion models transforming protein engineering and drug discovery. Join us for an insightful discussion about how AI is reshaping our understanding of molecular biology and making complex protein engineering tasks more accessible than ever before.

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CHAPTERS:

(00:00:00) Teaser

(00:00:46) About the Episode

(00:04:30) AI for Biology

(00:07:14) David Baker's Impact

(00:11:49) AlphaFold 3 & ESM3

(00:16:40) Protein Interaction Prediction (Part 1)

(00:16:44) Sponsors: Shopify | SelectQuote

(00:19:18) Protein Interaction Prediction (Part 2)

(00:31:12) MSAs & Embeddings (Part 1)

(00:32:32) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++

(00:34:49) MSAs & Embeddings (Part 2)