Computational techniques explore ‘the dark side of amyloid aggregation in the brain’

As doctors and families know very well, although Alzheimer’s disease has been studied intensively for decades, too much is still unknown about the molecular processes in the brain that cause it. Now University of Massachusetts Amherst researchers say new insights from analytical theory and molecular simulation techniques offer a better understanding of amyloid fibril growth and brain pathology.

As lead author Jianhan Chen points out, the “amyloid hypothesis” was promising: Amyloid protein fibrils are a central feature in Alzheimer’s, Parkinson’s disease, and other neurodegenerative diseases. “But the process is really difficult to study,” he says. “For many years people thought that fibril might be the damaging factor in the brain. But after billions of dollars of investment failed to administer an Alzheimer’s drug, that thought is really questionable. We now believe that fibril It is not the toxic species, but it is the previous forms, soluble oligomers or protofibrils. That is what we wanted to study. “

Chen and first author Zhiguang Jia, a research scientist at Chen’s computational biophysics laboratory, explored how building block peptides form fibrils. “We are truly proud of this work because, to the best of our knowledge, we have described for the first time the comprehensive process of how fibril growth can occur. We illustrate that the effects of disease-causing mutations often stem from the cumulative effects of many disturbances. Small. A full description is absolutely critical to generating reliable and testable hypotheses, “he adds. The details of its multi-scale approach with many atomistic simulations are found in Procedures of the National Academy of Sciences.

Chen adds: “The process is slow and very complex. All non-productive pathways are generally hidden and have never been exhaustively and quantitatively described. It is like the dark side of the moon.”

Chen says his model is “parameter-free and purely physics-based, with no adjustments or assumptions. We provide a full description of the process and the physics just comes naturally. It’s really satisfying; we feel like it’s a real breakthrough.” .

He and Jia first focused on how peptides behave in disordered solution. Chen notes that the process begins with peptides in a partially deployed conformation. They describe both productive and non-productive aggregation processes, and point out that non-productive ones can take a long time to disconnect from interactions. “It’s like walking in the woods without a path,” says Chen. “It’s like a maze. And if a peptide takes the wrong path, it starts over and tries again many, many times.”

A key idea was to account for these many non-productive pathways, too many possibilities, that slow down movement and cause a “kinetic bottleneck,” he says. Another important idea, Chen points out, is that the “energy picture,” as biophysicists call it, is crucial. With “regular” structured proteins, despite their great complexity, they fold quickly because the underlying energy landscape is well structured to support fast and efficient folding.

By contrast, fibril growth occurs in a “really flat” energy landscape, he adds. “There are many, many errors before falling into the hole that will lead to fibril formation.” Biochemists call it “unguided search,” he says, adding that “awkward” is a good way to describe it.

Modeling and characterizing such unguided systems is extremely difficult, the biophysicist notes. “To use a simulation to predict the process, you need a complete description of the entire maze, or you can never understand it, and this is almost impossible. To fully describe the search space, you must compromise the resolution of peptide modeling. When you limit the resolution of the model, you won’t be able to faithfully capture the impacts of disease mutations, for example. “

He says that these conflicting requirements (resolution and integrity) must be met at the same time. “Our approach is the first to satisfy both. This is one of our technical advances,” says Chen.

Chen says a key inspiration for the multiscale algorithm came from the theoretical work of Jeremy Schmit, a contributor and co-author of the article from Kansas State University. Together, we show how to achieve a description of the peptide search process at the atomic level. We demonstrate our approach by looking at how mutations in the beta amyloid peptide affect the growth of fibrils. Our results show that we can reproduce what is known about these mutants, plus the peculiar non-additivity of the mutations, which is observed experimentally. This means that two positions can mutate and either of them will cause the growth of fibrils to accelerate, but if both are mutated, the growth of fibrils will slowly go away ”