AF2Complex: Researchers leverage deep learning to predict physical interactions of protein complexes

Date:
April 18, 2022
Source:
Georgia Institute of Technology
Summary:
Proteins are the molecular machinery that makes life possible,
and researchers have long been interested in a key trait of protein
function: their three-dimensional structure. A new study details a
computational tool able to predict the structure protein complexes
-- and lends new insights into the biomolecular mechanisms of
their function.



FULL STORY ==========================================================================
From the muscle fibers that move us to the enzymes that replicate our DNA, proteins are the molecular machinery that makes life possible.


========================================================================== Protein function heavily depends on their three-dimensional structure,
and researchers around the world have long endeavored to answer a
seemingly simple inquiry to bridge function and form: if you know the
building blocks of these molecular machines, can you predict how they
are assembled into their functional shape? This question is not so
easy to answer. With complex structures dependent on intricate physical interactions, researchers have turned to artificial neural network models
-- mathematical frameworks that convert complex patterns into numerical representations -- to predict and "see" the shape of proteins in 3D.

In a new paper published in Nature Communications, researchers at
Georgia Tech and Oak Ridge National Laboratory build upon one such model, AlphaFold 2, to not only predict the biologically active conformation
of individual proteins, but also of functional protein pairings known
as complexes.

The work could help researchers bypass lengthy experiments to study the structure and interactions of protein complexes on a large scale, said
Jeffrey Skolnick, Regents' Professor and Mary and Maisie Gibson Chair in
the School of Biological Sciences and one of the corresponding authors
of the study, adding that computational models such as these could mean
big things for the field.

If these new computational models are successful, Skolnick said, "it could fundamentally change the way biological molecular systems are studied."
Primed for Protein Prediction


========================================================================== Created by London-based artificial intelligence lab DeepMind, AlphaFold
2 is a deep learning neural network model designed to predict the three-dimensional structure of a single protein given its amino acid
sequence. Skolnick and fellow corresponding author, Mu Gao, senior
research scientist in the School of Biological Sciences, shared that
the Alphafold 2 program was highly successful in blind tests occurring
at the 14thiteration of the Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction, or CASP14,
a bi-annual competition where researchers around the globe gather to
put their computational models to the test.

"To us, what is striking about AlphaFold 2 is that it not only
makes excellent predictions on individual protein domains (the basic
structural or functional modules of a protein sequence), but it also
performs very well on protein sequences composed of multiple domains,"
Skolnick shared. And so with the ability to predict the structure of
these complicated, multi-domain proteins, the research team set out to determine if the program could go a little further.

"The physical interactions between different [protein] domains of
the same sequence are essentially the same as the interactions gluing
different proteins together," Gao explained. "It quickly became clear that relatively simple modifications to AlphaFold 2 could allow it predict the structural models of a protein complex." To explore different strategies,
Davi Nakajima An, a fourth- year undergraduate in the School of Computer Science, was recruited to join the team's effort.

Instead of plugging in the features of just one protein sequence into
AlphaFold 2 per its original design, the researchers joined the input
features of multiple protein sequences together. Combined with new
metrics to evaluate the strength of interactions among probed proteins,
their new program AF2Complex was created.

Charting New Territory To put AF2Complex to the test, the researchers
partnered with the high- performance computing center, Partnership for
an Advanced Computing Environment (PACE), at Georgia Tech, and charged
the model with predicting the structures of protein complexes it had
never seen before. The modified program was able to correctly predict the structure of over twice as many protein complexes as a more traditional
method called docking. While AF2Complex only needs protein sequences as
input, docking relies on knowing individual protein structures beforehand
to predict their combined structure based on complementary shapes.



========================================================================== "Encouraged by these promising results, we extended this idea to an
even bigger problem, which is to predict interactions among multiple arbitrarily chosen proteins, e.g., in a simple case, two arbitrary
proteins," shared Skolnick.

In addition to predicting the structure of protein complexes, AF2Complex
was charged with identifying which of over 500 pairs of proteins were
able to form a complex at all. Using newly designed metrics, AF2Complex outperformed conventional docking methods and AlphaFold 2 in identifying
which of the arbitrary pairs were known to experimentally interact.

To test AF2Complex on the proteome scale, which encompasses an organism's entire library of the proteins that can be expressed, the researchers
turned to the Summit Oak Ridge Leadership Computing Facility, the
world's second largest supercomputing center. "Thanks to this resource,
we were able to apply AF2Complex to about 7,000 pairs of proteins from
the bacteria E. coli," Gao shared.

In that test, the team's new model not only identified many pairs of
proteins known to form complexes, but it was able to provide insights
into interactions "suspected but never observed experimentally," Gao said.

Digging deeper into these interactions revealed a potential molecular
mechanism for protein complexes that are particularly important for
energy transport.

These protein complexes are known to carry hemes, essential metabolites
giving blood dark red color. Using AF2Complex's predicted structural
models, Jerry M.

Parks, a senior research and development staff scientist at Oak Ridge
National Laboratory and a collaborator in the study, was able to place
hemes at their suspected reaction sites within the structure. "These computational models now provide insights into the molecular mechanisms
for how this biomolecular system works," Gao said.

"Deep learning is changing the way one studies a biological system,"
Skolnick added. "We envision methods like AF2Complex will become
powerful tools for any biologist who would like to understand molecular mechanisms of a biosystem involving protein interactions." This work
was supported in part by the DOE Office of Science, Office of Biological
and Environmental Research (DOE DE-SC0021303) and the Division of General Medical Sciences of the National Institute Health (NIH R35GM118039).


========================================================================== Story Source: Materials provided
by Georgia_Institute_of_Technology. Original written by Audra
Davidson. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey
Skolnick. AF2Complex
predicts direct physical interactions in multimeric proteins
with deep learning. Nature Communications, 2022; 13 (1) DOI:
10.1038/s41467-022- 29394-2 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220418093952.htm

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