Understanding Generative Models of Protein Sequences

Related lectures (32)

Explores the evolution of protein structure prediction with AlphaFold models and introduces OpenFold insights.

Delves into how biological structure and function are decoded through unsupervised learning of protein sequences.

Covers protein design fundamentals, including secondary structures, amino acid propensity, loops, and computational methods.

Delves into predicting protein structure through amino acid contact analysis and advanced computational methods.

Covers the structure, function, and engineering of proteins, emphasizing their biological significance and applications in biotechnology.

Delves into analyzing residue coevolution in protein families to capture native contacts and predict spatial proximity and protein interactions.

Discusses a mini-project on sequence alignment of proteins and submission guidelines.

Series explores computational protein design, covering challenges, applications, and importance in bioengineering and synthetic biology.

Explores protein folding, hydrophobic interactions, compact conformations, the HP model, co-evolution, and computational methods.

Covers the principles and challenges of computational protein design in bioengineering.

Covers the Needleman-Wunsch algorithm for optimal protein sequence alignment using dynamic programming and scoring methods.

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

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