ModelAngelo

Summary

The video transcript features a presentation by Kiarash Jamali from the MRC Laboratory of Molecular Biology in Cambridge, UK, discussing ModelAngelo, an automated atomic model building program for cryo-electron microscopy (cryo-EM) maps. Jamali explains the significance of Pi Day and its relation to the celebration of science, before delving into the technical aspects of ModelAngelo. He outlines the three-step process of the program: C alpha atom predictions, full atomic model building using sequence information, and post-processing to refine the model. The use of a graphical network and attention algorithm, inspired by machine learning models like ChapGPT and AlphaFold, is highlighted. Jamali also addresses the application of ModelAngelo in building models without prior sequence knowledge and its potential in identifying unknown sequences from high-resolution maps. The talk concludes with a Q&A session where common issues are discussed, future improvements to ModelAngelo are mentioned, including the addition of nucleotide modeling and an integrated HMMER search, and the presenter's future targets in the field of structural biology are briefly touched upon.

Takeaways

• 🎓 **Pi Day Celebration**: The video begins with a mention of Pi Day (March 14), which is a celebration of mathematics and science, significant to the presenter's family.
• 🧬 **Introduction to Kiarash Jamali**: Kiarash Jamali, from the MRC Laboratory of Molecular Biology in Cambridge, UK, discusses ModelAngelo, a program for automated atomic model building for cryo-EM maps.
• 🛠️ **ModelAngelo Overview**: ModelAngelo is a three-step automated process for building atomic models from cryo-EM maps, involving C alpha atom predictions, full atomic model building, and post-processing.
• 🧠 **Machine Learning Approach**: ModelAngelo utilizes a graphical network with an attention algorithm, a technique used in various machine learning applications, to combine cryo-EM map data, sequence information, and initial C alpha positions.
• 📈 **C Alpha Prediction**: The process starts with predicting C alpha atoms within a 1.5 Å cube of the cryo-EM map, transforming it into a machine learning segmentation problem.
• 🔬 **Integration of Data**: The graphical network in ModelAngelo is designed to integrate cryo-EM map data, sequence data, and initial C alpha positions into a comprehensive model.
• 🧬 **Sequence Module**: A key component is the sequence module, which uses a protein language model to analyze the relationship between the C alpha representation and the input amino acid sequence.
• 🧬 **Spatial Invariant Point and Attention Module**: This module is similar to AlphaFold and focuses on integrating geometric information about nearby residues, such as alpha helices and beta sheets.
• 🔍 **Post-Processing**: Post-processing involves turning probabilities for amino acid types into an actual atomic model, using a hidden Markov model to refine sequence predictions.
• 🧬 **Sequence Search**: ModelAngelo can perform sequence searches against a genome using HMM profiles, which can be useful for identifying unknown sequences in a cryo-EM map.
• ⚙️ **ModelAngelo Usage**: The command-line interface for ModelAngelo is designed to be simple, with options to build with or without sequence information, and detailed instructions available on GitHub.

Q & A

What is the significance of Pi Day in the context of this video?

Pi Day, celebrated on March 14, is a holiday that is significant to this video because it is a celebration of mathematics and science in general. The presenter, Jason Key, mentions that it is a big holiday in his family and thematically appropriate for introducing Kiarash Jamali, who has a background in mathematics and computational approaches to structural biology.

What is ModelAngelo and what does it automate?

ModelAngelo is an automated, atomic model building program for cryo-EM maps. It is designed to automate the process of building atomic models into cryo-EM maps in an end-to-end fashion, which traditionally is done manually on a residue-by-residue basis.

How does ModelAngelo utilize the attention algorithm in its process?

ModelAngelo uses the attention algorithm as a differentiable search procedure within its graphical network. This allows the system to extract information from a library of vectors representing different pieces of input data, such as the cryo-EM map, sequence information, and initial C alpha positions. The attention algorithm helps in integrating this data together to build a full atomic model.

What is the role of the sequence module in ModelAngelo?

The sequence module in ModelAngelo processes the amino acid sequence for all the sequences expected to be seen in the map. It uses a protein language model to convert the text sequence into a series of vectors. These vectors are then used to calculate similarity to the sequence and update the representation of each residue with the added sequence information.

How does ModelAngelo handle the prediction of amino acid types for each residue?

ModelAngelo predicts the probabilities for each amino acid type for every residue. Instead of choosing the highest scoring amino acid, it uses these probabilities to inform the selection of the correct sequence, often turning this into a hidden Markov model to align with the user-provided sequences.

What is the purpose of the post-processing step in ModelAngelo?

The post-processing step in ModelAngelo is used to fix issues and refine the atomic model. It involves taking the probabilities for amino acid types and using them to determine the actual amino acid in each residue, correcting any issues with the initial C alpha positions, and refining the orientation of the atomic model.

How does ModelAngelo deal with unknown sequences in the map?

ModelAngelo can be run without a sequence module, allowing it to build models without prior knowledge of the sequences. It can then use the built model to search against a genome or a database of known sequences to identify the unknown sequences present in the cryo-EM map.

What are the common issues faced when using ModelAngelo and how can they be diagnosed?

Common issues include the map being in the wrong handedness, which can be diagnosed by checking the confidence values predicted by ModelAngelo or by flipping the hand of the map and re-running the model. Other issues include low local resolution, which can be addressed by ensuring the sequence is correctly provided in the fasta file, and low global resolution, which might require higher resolution maps for better results.

What are the improvements expected in the upcoming ModelAngelo 1.0 version?

The upcoming ModelAngelo 1.0 version will include the ability to build nucleotides, improved performance with an integrated HMMER search, optimizations for faster processing, and the use of a Nyquist of 2 Angstroms for better results with nucleotides.

What is the ideal resolution range for ModelAngelo to build high-quality models?

The ideal resolution range for ModelAngelo to build high-quality models is 3.5 Angstroms and better. The performance starts to drop off quickly after 3.5 Angstroms.

How does ModelAngelo handle symmetry in cryo-EM maps?

ModelAngelo builds everything independently, assuming no symmetry. If symmetry is present in the map, the user may need to manually apply symmetry to the built chains, choosing the best chain and duplicating it accordingly.

What are the next steps or targets for Kiarash Jamali after working on ModelAngelo?

Kiarash Jamali plans to continue improving ModelAngelo, particularly with the addition of nucleotide building capabilities. He is also involved with other projects in cryo-EM processing, with further plans to be disclosed as the projects develop.