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Related lectures (11)

Explores protein co-evolutionary analysis, covering protein functions, folding, correlations, DCA, and structure prediction tools like AlphaFold 2.

Biological structure and function emerge from unsupervised learning

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

Quantifying Randomness and Information in Biological Data

Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.

Metastable Materials: New Perspectives on Discovery

Explores challenges in identifying useful metastable materials and discusses concepts like structure predictions, ensemble probabilities, and mapping algorithms.

Machine-learned force fields for molecular simulation

Explores machine-learned force fields for accurate molecular simulations and the solution of the electronic Schrödinger equation.

Protein Structure Prediction: Alphafold2

Explores the revolutionary Alphafold2 for protein structure prediction and its impact on bioinformatics and protein folding biology.

Proteins: Engineering and Materials

Explores protein engineering, elastin-based materials, lab protein production, AI prediction of folding, and non-natural amino acids.

Protein Contact Prediction: Pseudolikelihoods and Potts Models

Covers protein contact prediction using Potts models and pseudolikelihood methods, comparing different approaches for contact prediction in proteins.

Protein Structure Prediction: AlphaFold Evolution and OpenFold Insights

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

Protein Structure Prediction

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

Property Testing: Uniform Distribution

Covers property testing for uniform distributions and the importance of reliable predictions.

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