1. Proteins
What are they and why are they important.
2. The Language of Proteins: Sequences
Analyzing the amino acid sequence of a protein, including aligning sequences, identifying patterns, and utilizing databases and resources to gain insights into protein function and evolution.
3. Protein Structure
Predicting the three-dimensional structure of a protein using computational methods, including homology modeling, comparative modeling, and de novo structure prediction.
4. Protein-Protein Interactions
Exploring how proteins interact with each other, including computational techniques for predicting and analyzing protein-protein interactions, as well as docking algorithms for simulating their binding.
5. Protein Engineering and Design
Modifying proteins to enhance their properties or design new ones, using methods like directed evolution and rational design, often guided by computational modeling and simulation.
6. Protein Function Prediction and Annotation
Inferring the function of a protein based on its sequence or structure, utilizing computational methods to predict and annotate protein functions, including predicting protein-protein interactions.
7. Data Resources
A description of resources that contain relevant protein data that can be used in ML models.
8. Machine Learning and Deep Learning for Protein Language Modeling
Leveraging machine learning and deep learning algorithms to model and analyze protein sequences and structures, enabling tasks such as sequence generation, structure prediction, and function annotation.
9. Applications of Protein Language Models
Exploring real-world applications of protein language modeling, including refining and improving protein structures, predicting protein-protein interactions, facilitating drug discovery, and aiding protein engineering and design.
10. Limitations
Addressing the limitations associated with protein language modeling, potential biases or shortcomings of computational methods.