The shape-based model sheds light on simplified protein binding


Three dimers, protein structures consisting of two bound proteins, from the Dockground database. The interfaces at which the proteins meet are shown as the darkened regions. Credit: ORNL
Three dimers, protein structures consisting of two bound proteins, from the Dockground database. The interfaces at which the proteins meet are shown as the darkened regions. Credit: ORNL
 

Can something as simple as shape fully determine whether or not proteins will bind together? Scientists are commissioning supercomputers to find out. A team led by Sharon Glotzer, distinguished professor and department chair of chemical engineering at the University of Michigan (UM), used the 200-petaflop Summit supercomputer at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) to model lock-and-key interactions between proteins to study their binding behaviors. The results, published in Soft Matter, revealed that some proteins do, in fact, bind based on shape alone.


Tens Glaser, computational scientist in the Advanced Computing for Chemistry and Materials group at the Oak Ridge Leadership Computing Facility (OLCF) said, we've demonstrated that something as simple as the shape is able to predict protein interactions that are sometimes really complex. This first demonstration has led us to believe that shape has been an unappreciated ingredient in many protein assembly processes.


The results could have numerous applications in biological research. For example, the approach might be used to screen drugs for disease or provide scientists with information about how to use proteins as building blocks to design new biological materials.


Dr. Stephanie McElhinny, program manager at the US Army Combat Capabilities Development Command's Army Research Laboratory, referring to the favorable spatial relationship between two compatibly shaped proteins said, this exciting study demonstrates the power of shape complementarity in the prediction of protein-protein interfaces. Computational models that accurately predict these interfaces will support the future design of advanced protein-based materials with active and responsive properties, such as light-harvesting protein-based plastics that could function as an artificial leaf for power generation.


For proteins to successfully bind to one another, one of them acts as a ligand, a molecule that attaches to a target protein, and one of them acts as a receptor, the molecule that receives the ligand. This process involves complex chemical interactions, in which molecules share bonds and change their configurations upon binding.


Glotzer's team wanted to see whether they could predict this molecular binding based on shape alone, ignoring the interactions between proteins. From a database of more than 6,000 protein pairs, the team tested 46 pairs that are known to bind to one another and simulated their assembly on Summit. The team performed the simulations under the INCITE (Innovative and Novel Computational Impact on Theory and Experiment) program.


Like multiple tennis balls being thrown at a single target, the simulations modeled multiple ligands being tossed at a single, fixed target receptor. Out of the 46 pairs tested, they found 6 pairs that performed well more than 50 percent of the time they successfully assembled based purely on their complementary shapes.


Fengyi Gao, Ph.D. candidate at UM said, we looked at the interfaces where the proteins bound together to see how similar they were to their real-life interfaces, and then we determined the cutoff to see how many pairs were good predictors of the real interfaces. We found that 13 percent of these protein pairs could bind based on shape alone.


The team then built a machine-learning model that could determine which proteins are able to assemble solely based on shape. Combining their initial model with such machine-learning tools will help them understand what information is needed for protein pairs that cannot assemble based on shape complementarity alone.


To model multiple reversible binding processes of 46 protein pairs under different parameters, they needed two days of computational time and more than 3,000 GPUs an amount that only a supercomputer like the OLCF's Summit could provide. The OLCF is a DOE Office of Science User Facility at ORNL.


As part of the HOOMD-blue computational code that was used to run the simulations, Glaser, who was previously an assistant research scientist in Glotzer's group at UM, developed an algorithm that simulated the proteins in the presence of many small particles. But Glaser found a way to model only the motion of the proteins the team was interested in, avoiding unnecessary and expensive calculations for the solvent molecules around them.


Glaser said, I ran the code in parallel so that many different parameters, iterations of the same system, and different proteins could be distributed across the GPUs. This allowed us to easily make use of Summit's parallel computing capabilities.


Using Summit, the team captured six protein pairs that bound based only on shape complementarity, with one of them achieving binding more than 94 percent of the time.


Glaser said, it was quite surprising to us that such a simplified model could correctly select just that one pose that they assume out of the many hundreds or more poses that compete. We were expecting that much more would be necessary to reproduce the real binding pose for these protein pairs.


The team plans to study more proteins that can also bind based on shape or form even higher-order structures. The team's current study explored only protein dimers, which consist of two proteins bound together, but the team wants to know the limitation for how protein shapes can evolve to form hierarchical protein structures.


Fengyi Gao, Ph.D. candidate at UM said, before we did this study, I actually didn't expect proteins could form dimers based on shape alone. But now, we've found that this works, and we can study more complex structures or even combine this with other approaches, like machine learning, to see which features we need to enable the correct binding.


The team hopes they can eventually predict the binding of protein-protein interfaces in protein clusters or protein crystallization structures.


Gao said, we think we can adopt this approach to something like drug screening in the future. In addition to that, we hope that this shape-based model can serve as a basis of studying protein assembly in general.


Journal Information: Fengyi Gao et al, The role of complementary shape in protein dimerization, Soft Matter (2021). DOI: 10.1039/D1SM00468A

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