Artificial intelligence system rapidly predicts how two proteins will
attach
The machine-learning model could help scientists speed the development of
new medicines

Date:
February 1, 2022
Source:
Massachusetts Institute of Technology
Summary:
A new machine learning system can predict the structure formed
when two proteins dock, in a process that's between 50 to 800
times faster than some software-based methods. This could help
scientists better understand biological processes or speed the
development of new therapies.



FULL STORY ========================================================================== Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to
battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon
is a synthetic antibody that binds with the virus' spike proteins to
prevent the virus from entering a human cell.


==========================================================================
To develop a successful synthetic antibody, researchers must understand
exactly how that attachment will happen. Proteins, with lumpy 3D
structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time- consuming.

To streamline the process, MIT researchers created a machine-learning
model that can directly predict the complex that will form when two
proteins bind together. Their technique is between 80 and 500 times
faster than state-of-the- art software methods, and often predicts
protein structures that are closer to actual structures that have been
observed experimentally.

This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and
repair; it could also speed up the process of developing new medicines.

"Deep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and
people haven't found good ways to express them. This deep-learning model
can learn these types of interactions from data," says Octavian-Eugen
Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co- lead author of the paper.

Ganea's co-lead author is Xinyuan Huang, a graduate student at ETH
Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola,
the Thomas Siebel Professor of Electrical Engineering in CSAIL and a
member of the Institute for Data, Systems, and Society. The research will
be presented at the International Conference on Learning Representations.



========================================================================== Protein attachment The model the researchers developed, called Equidock, focuses on rigid body docking -- which occurs when two proteins attach
by rotating or translating in 3D space, but their shapes don't squeeze
or bend.

The model takes the 3D structures of two proteins and converts
those structures into 3D graphs that can be processed by the neural
network. Proteins are formed from chains of amino acids, and each of
those amino acids is represented by a node in the graph.

The researchers incorporated geometric knowledge into the model, so it understands how objects can change if they are rotated or translated in
3D space. The model also has mathematical knowledge built in that ensures
the proteins always attach in the same way, no matter where they exist
in 3D space.

This is how proteins dock in the human body.

Using this information, the machine-learning system identifies atoms
of the two proteins that are most likely to interact and form chemical reactions, known as binding-pocket points. Then it uses these points to
place the two proteins together into a complex.



==========================================================================
"If we can understand from the proteins which individual parts are
likely to be these binding pocket points, then that will capture all the information we need to place the two proteins together. Assuming we can
find these two sets of points, then we can just find out how to rotate and translate the proteins so one set matches the other set," Ganea explains.

One of the biggest challenges of building this model was overcoming
the lack of training data. Because so little experimental 3D data for
proteins exist, it was especially important to incorporate geometric
knowledge into Equidock, Ganea says. Without those geometric constraints,
the model might pick up false correlations in the dataset.

Seconds vs. hours Once the model was trained, the researchers compared it
to four software methods. Equidock is able to predict the final protein
complex after only one to five seconds. All the baselines took much
longer, from between 10 minutes to an hour or more.

In quality measures, which calculate how closely the predicted protein
complex matches the actual protein complex, Equidock was often comparable
with the baselines, but it sometimes underperformed them.

"We are still lagging behind one of the baselines. Our method can still
be improved, and it can still be useful. It could be used in a very large virtual screening where we want to understand how thousands of proteins
can interact and form complexes. Our method could be used to generate an initial set of candidates very fast, and then these could be fine-tuned
with some of the more accurate, but slower, traditional methods," he says.

In addition to using this method with traditional models, the team wants
to incorporate specific atomic interactions into Equidock so it can make
more accurate predictions. For instance, sometimes atoms in proteins will attach through hydrophobic interactions, which involve water molecules.

Their technique could also be applied to the development of small,
drug-like molecules, Ganea says. These molecules bind with protein
surfaces in specific ways, so rapidly determining how that attachment
occurs could shorten the drug development timeline.

In the future, they plan to enhance Equidock so it can make predictions
for flexible protein docking. The biggest hurdle there is a lack of
data for training, so Ganea and his colleagues are working to generate synthetic data they could use to improve the model.

This work was funded, in part, by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Swiss National Science Foundation,
the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging (DOMANE)
threats program, and the DARPA Accelerated Molecular Discovery program.

Independent SE(3)-Equivariant Models for End-to-End Rigid
Protein Docking: https://openreview.net/forum?id=GQjaI9mLet ========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by Adam
Zewe. Note: Content may be edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2022/02/220201165655.htm

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