Sam Gelman

Hi, I'm Sam! I research deep learning methods for protein design. I have a strong background in computer science, machine learning, and biology, which has allowed me to excel in this field. I am passionate about using deep learning to better understand protein structure and function, and I have developed innovative algorithms and models in this area. Let's connect!

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Upcoming preprint

Mutational Effect Transfer Learning

Keep an eye out for my upcoming preprint!

I am excited to share how we are using molecular simulations and transfer learning to improve protein sequence-function modeling when there is limited experimental training data available.

Featured publication

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter. Proceedings of the National Academy of Sciences (2021).

Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein’s behavior and properties. We show that neural networks can learn the sequence–function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequence–function relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate that learned models can be applied to design new proteins with properties that exceed natural sequences.