麻豆淫院

In synthetic and structural biology, advances in artificial intelligence have led to an explosion of designing new proteins with specific functions, from antibodies to blood clotting agents, by using computers to accurately predict the 3D structure of any given amino acid sequence.

But the structure of close to 30% of all proteins expressed by the human genome are challenging to predict for even the most powerful AI tools, including the .

Never settling into a fixed shape but constantly shifting around, these so-called intrinsically disordered proteins are key to countless biological functions like cross-linking molecules, sensing, or signaling, but their inherent instability makes them difficult to design from scratch.

A team at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Northwestern University have demonstrated a new machine learning method that can design intrinsically disordered proteins with tailored properties. The work opens doors to new understanding of these mysterious biomolecules and possible new insights into the origins of and treatments for diseases.

The work is published in and was co-led by SEAS graduate student Ryan Krueger and former NSF-Simons QuantBio Fellow Krishna Shrinivas, now an assistant professor at Northwestern, in collaboration with Michael Brenner, the Catalyst Professor of Applied Mathematics and Applied 麻豆淫院ics at SEAS.

Shrinivas said he became interested in studying intrinsically disordered proteins because they are out of reach of current AI-based methods, such as Google DeepMind's AlphaFold, for predicting and designing proteins with distinct conformations. Yet, such disordered proteins are important to many fundamental aspects of biology, and it is known that mutations to these proteins are linked to diseases like cancer and neurodegeneration.

One example of a disordered protein is alpha-synuclein, long implicated in Parkinson's and other diseases. To design IDPs for synthetic or therapeutic uses, Shrinivas said, "we needed to either come up with better AI models, or, we needed to come up with a way to actually take those physics models where you not only get good predictions, but you also get the physics for free."

Automatic differentiation algorithms

The paper describes a computational method powered by algorithms that can perform "automatic differentiation," or automatic computation of derivatives鈥攊nstantaneous rates of change鈥攊n order to rationally select for protein sequences with desired behaviors or properties.

The technique is a widely used tool for deep learning and training neural networks, but Brenner and his lab were among the first to recognize other potential use cases, such as optimizing physics-based molecular dynamics simulations.

With automatic differentiation, the researchers were able to train a computer to recognize how small changes in protein sequences鈥攅ven single amino acid changes鈥攁ffect the final desired properties of proteins.

They likened their method to a very powerful search engine for amino acid sequences that fit the criteria needed to perform a function鈥攕ay, one that creates loops or connectors, or can sense different things in the environment.

"We didn't want to have to take a bunch of data and train a machine learning model to design proteins," Krueger said. "We wanted to leverage existing, sufficiently accurate simulations to be able to design proteins at the level of those simulations."

The method leverages a traditional framework for training neural networks called gradient-based optimization to identify new protein sequences with efficiency and precision.

The result is that the proteins the researchers designed are "differentiable," that is, they are not best-guesses predicted by AI, but rather based in molecular dynamics simulations using real physics, that take into account how proteins actually, dynamically behave in nature.