Protein–protein interaction prediction

Applied sciences
Biological engineering
Biotechnology
Bioinformatics

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Related lectures (32)

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Protein Structure Prediction

Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.

Machine-learned force fields for molecular simulation

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

Metabolic Engineering: Systems Biology and Tools

Explores systems biology of metabolism, genome-scale models, and metabolic engineering applications.

Link Prediction: Missing Edges and Probabilistic Methods

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Predicting completion time: Activity Progress

Covers the prediction of completion time in activities based on progress data.

Protein Functions and Interactions: Sequence Analysis

Explores predicting protein structure from sequence data and inferring interaction partners through Direct Coupling Analysis and the Iterative Pairing Algorithm.

Protein Design: Enzyme and Biosensor Engineering

Explores novel enzyme and biosensor design for catalysis and ligand detection.

Protein Structure Prediction: AlphaFold Evolution and OpenFold Insights

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

Advanced Physics I: Introduction to Mechanics

Introduces the basics of physics, including mechanics and making predictions based on observations and hypotheses.

Quantifying Randomness and Information in Biological Data

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