David Baker
· Undergraduate Chair; PPE Chair; ProfessorUniversity of Michigan · Philosophy
Active 1957–2024
About
Professor David Baker is a faculty member in the Department of Philosophy at the University of Michigan. He holds the positions of Undergraduate Chair, PPE Chair, and Professor. He earned his Ph.D. in Philosophy from Princeton University in 2008 and his B.S. with highest honors in Philosophy and Physics from the University of Michigan in 2003. Professor Baker's research focuses on the philosophy of physics, including the philosophy of quantum field theory, quantum mechanics, and general relativity. His work also addresses related issues in metaphysics and the philosophy of science, with particular interest in symmetry principles, laws of nature, and the metaphysics of fundamental quantities. His articles have been published in several academic journals such as Nous, Philosophy of Science, and The British Journal for the Philosophy of Science. Additionally, he has interests in moral philosophy.
Research topics
- Computer Science
- Biology
- Biochemistry
- Chemistry
- Computational biology
- Artificial Intelligence
- Genetics
- Biophysics
- Materials science
- Engineering
- Machine Learning
- Cell biology
- Nanotechnology
- Virology
- Programming language
- Algorithm
- Data Mining
- Mathematics
- Immunology
- Evolutionary biology
- Cartography
- Systems engineering
- Human–computer interaction
- World Wide Web
Selected publications
bioRxiv (Cold Spring Harbor Laboratory) · 2024
Senior authorCorresponding- Biophysics
- Chemistry
- Cell biology
The design of inducibly assembling protein nanomaterials is an outstanding challenge. Here, we describe the computational design of a protein filament formed from a monomeric subunit that binds a peptide ligand. The cryoEM structure of the micron-scale fibers is very close to the computational design model. The ligand acts as a tunable allosteric modulator: while not part of the fiber subunit-subunit interfaces, the assembly of the filament is dependent on ligand addition, with longer peptides having more extensive interaction surfaces with the monomer, promoting more rapid growth. Seeded growth and capping experiments reveal that the filaments grow primarily from one end. We show that designed nucleators that present 12 copies of the peptide ligand promote fiber assembly at concentrations where otherwise assembly occurs very slowly, likely by generating critical local concentrations of monomer in the assembly competent conformation. Following filament assembly, the peptide ligand can be exchanged with free peptide in solution fused to any functional protein of interest, opening the door to a wide variety of tunable engineered materials.
De novo design of pH-responsive self-assembling helical protein filaments
Nature Nanotechnology · 2024 · 60 citations
Senior authorCorresponding- Nanotechnology
- Biophysics
- Materials science
Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.
Unraveling the functional dark matter through global metagenomics
Nature · 2023 · 193 citations
- Computational biology
- Evolutionary biology
- Biology
. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.
De novo design of protein structure and function with RFdiffusion
Nature · 2023 · 1810 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Artificial Intelligence
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, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.
De novo design of luciferases using deep learning
Nature · 2023 · 443 citations
Senior authorCorresponding- Chemistry
- Combinatorial chemistry
- Biochemistry
) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.
Robust deep learning–based protein sequence design using ProteinMPNN
Science · 2022 · 1718 citations
Senior authorCorresponding- Computer Science
- Artificial Intelligence
- Computational biology
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.
Accurate de novo design of membrane-traversing macrocycles
Cell · 2022 · 164 citations
Senior authorCorresponding- Biology
- Biophysics
- Genetics
cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.
Design of multi-scale protein complexes by hierarchical building block fusion
Nature Communications · 2021 · 87 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Computational biology
A systematic and robust approach to generating complex protein nanomaterials would have broad utility. We develop a hierarchical approach to designing multi-component protein assemblies from two classes of modular building blocks: designed helical repeat proteins (DHRs) and helical bundle oligomers (HBs). We first rigidly fuse DHRs to HBs to generate a large library of oligomeric building blocks. We then generate assemblies with cyclic, dihedral, and point group symmetries from these building blocks using architecture guided rigid helical fusion with new software named WORMS. X-ray crystallography and cryo-electron microscopy characterization show that the hierarchical design approach can accurately generate a wide range of assemblies, including a 43 nm diameter icosahedral nanocage. The computational methods and building block sets described here provide a very general route to de novo designed protein nanomaterials.
Accurate prediction of protein structures and interactions using a three-track neural network
Science · 2021 · 5578 citations
Senior authorCorresponding- Computer Science
- Artificial Intelligence
- Computer Science
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 approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
De novo design of transmembrane β barrels
Science · 2021 · 149 citations
Senior authorCorresponding- Chemistry
- Computational biology
- Biophysics
Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a "hypothesis, design, and test" approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.
Recent grants
NIH · $1.9M · 2013
ERASynBio: BioMolecular Origami
NSF · $389k · 2014–2017
III-CXT: Collaborative Research: Integrated Modeling of Biological Nanomachines
NSF · $165k · 2007–2009
NIH · $2.3M · 2019
NIH · $9.3M · 2014
Frequent coauthors
- 170 shared
Frank DiMaio
University of Washington
- 165 shared
Asim K. Bera
- 130 shared
Lance Stewart
University of Washington
- 120 shared
G.T. Montelione
Rensselaer Polytechnic Institute
- 111 shared
Alex Kang
University of Washington
- 108 shared
David E. Kim
- 108 shared
Sergey Ovchinnikov
Harvard University Press
- 104 shared
William Sheffler
Education
- 1989
PhD, Biochemistry
University of California Berkeley
- 1984
B.A., Biology
Harvard University
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