Frank DiMaio

About

Frank DiMaio is an Associate Professor in Biochemistry at the University of Washington, specializing in protein structure determination and computational modeling. His research focuses on developing novel computational tools to determine high-resolution protein structures from low-resolution experimental data, drawing on information from previously solved structures. He is interested in improving methods for conformational sampling, modeling of energetics, and identifying missing physical parameters to enhance the accuracy of protein models. His work also includes the prediction and design of symmetric protein assemblies, leveraging the natural abundance of symmetry in biological systems. His research aims to understand the mechanisms behind symmetric protein assembly, improve protein crystallization techniques, and develop symmetric protein assemblies for materials design. DiMaio's contributions are centered on advancing computational approaches to understand and manipulate protein structures and assemblies.

Research topics

• Computer Science
• Biology
• Chemistry
• Biochemistry
• Artificial Intelligence
• Materials science
• Computational biology
• Nanotechnology
• Mathematics
• Biological system

Selected publications

• Accurate prediction of protein structures and interactions using a three-track neural network

  Science · 2021 · 5578 citations

  DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approac…

  DOI

• De novo design of protein structure and function with RFdiffusion

  Nature · 2023 · 1810 citations

  have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, prot…

  DOI

• Macromolecular modeling and design in Rosetta: recent methods and frameworks

  Nature Methods · 2020 · 902 citations

  DOI

• Perturbing the energy landscape for improved packing during computational protein design

  Proteins Structure Function and Bioinformatics · 2020 · 193 citations

  The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we…

  PublisherOA PDFDOI

• An artificial intelligence accelerated virtual screening platform for drug discovery

  Nature Communications · 2024-09-05 · 176 citations

  articleOpen accessSenior author

  Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the…

  PublisherOA PDFDOI

Recent grants

• Multimodal Gating Mechanisms of TRPV1 Ion Channels

  NIH · $1.8M · 2018–2022

• Protein structure determination from low-resolution experimental data

  NIH · $2.6M · 2017–2026

Frequent coauthors

• David Baker

  University of Washington

  170 shared

• Daniel P. Farrell

  University of Washington

  49 shared

• Paul D. Adams

  Joint BioEnergy Institute

  29 shared

• Hahnbeom Park

  Korea Institute of Brain Science

  26 shared

• David Veesler

  University of Washington

  25 shared

• Minkyung Baek

  24 shared

• Sergey Ovchinnikov

  Harvard University Press

  23 shared

• Wah Chiu

  Stanford University

  21 shared

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