Doug Tischer
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View article: Computational design of metallohydrolases
Computational design of metallohydrolases Open
View article: <i>De novo</i> design of phospho-tyrosine peptide binders
<i>De novo</i> design of phospho-tyrosine peptide binders Open
Phosphorylation on tyrosine is a key step in many signaling pathways. Despite recent progress in de novo design of protein binders, there are no current methods for designing binders that recognize phosphorylated proteins and peptides; thi…
View article: Atom level enzyme active site scaffolding using RFdiffusion2
Atom level enzyme active site scaffolding using RFdiffusion2 Open
De novo enzyme design starts from ideal active site descriptions consisting of constellations of catalytic residue functional groups around reaction transition state(s), and seeks to generate protein structures that can accurately hold the…
View article: Computational Design of Metallohydrolases
Computational Design of Metallohydrolases Open
De novo enzyme design starts from a description of an ideal active site composed of catalytic residues surrounding the reaction transition state(s), and builds a protein structure that contains this site 1–7 . Generative AI methods such as…
View article: Small-molecule binding and sensing with a designed protein family
Small-molecule binding and sensing with a designed protein family Open
Despite transformative advances in protein design with deep learning, the design of small-molecule–binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to gen…
View article: De novo design of small beta barrel proteins
De novo design of small beta barrel proteins Open
Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such str…
View article: De novo design of luciferases using deep learning
De novo design of luciferases using deep learning Open
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds 1,2 , but has been limited by a lack of suitable …
View article: Robust deep learning–based protein sequence design using ProteinMPNN
Robust deep learning–based protein sequence design using ProteinMPNN Open
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 learnin…
View article: Scaffolding protein functional sites using deep learning
Scaffolding protein functional sites using deep learning Open
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional site…
View article: Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem Open
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-l…
View article: Robust deep learning based protein sequence design using ProteinMPNN
Robust deep learning based protein sequence design using ProteinMPNN Open
While 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 ba…
View article: De novo design of small beta barrel proteins
De novo design of small beta barrel proteins Open
This dataset contains the supplementary materials for "De novo design of small beta barrel proteins" Datasets. All dataset_SX.tar.gz files extract into a directory named data_archive. Dataset S1 (dataset_S…
View article: Deep learning methods for designing proteins scaffolding functional sites
Deep learning methods for designing proteins scaffolding functional sites Open
Current approaches to de novo design of proteins harboring a desired binding or catalytic motif require pre-specification of an overall fold or secondary structure composition, and hence considerable trial and error can be required to iden…
View article: Protein sequence design by conformational landscape optimization
Protein sequence design by conformational landscape optimization Open
Significance Almost all proteins fold to their lowest free energy state, which is determined by their amino acid sequence. Computational protein design has primarily focused on finding sequences that have very low energy in the target desi…
View article: Design of proteins presenting discontinuous functional sites using deep learning
Design of proteins presenting discontinuous functional sites using deep learning Open
An outstanding challenge in protein design is the design of binders against therapeutically relevant target proteins via scaffolding the discontinuous binding interfaces present in their often large and complex binding partners. There is c…
View article: Optogenetic Tuning of Protein-protein Binding in Bilayers Using LOVTRAP
Optogenetic Tuning of Protein-protein Binding in Bilayers Using LOVTRAP Open
Modern microscopy methods are powerful tools for studying live cell signaling and biochemical reactions, enabling us to observe when and where these reactions take place from the level of a cell down to single molecules. With microscopy, e…
View article: Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling
Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling Open
T cells are thought to discriminate self from foreign peptides by converting small differences in ligand binding half-life into large changes in cell signaling. Such a kinetic proofreading model has been difficult to test directly, as exis…
View article: Author response: Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling
Author response: Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling Open
View article: Light-based tuning of ligand half-life supports kinetic proofreading model of T cell activation
Light-based tuning of ligand half-life supports kinetic proofreading model of T cell activation Open
T cells are thought to discriminate stimulatory versus non-stimulatory ligands by converting small changes in ligand binding half-life to large changes in cell activation. Such a kinetic proofreading model has been difficult to test direct…
View article: Using optogenetic tools to test the kinetic proofreading model of T cell receptor ligand discrimination
Using optogenetic tools to test the kinetic proofreading model of T cell receptor ligand discrimination Open
The kinetics and intensity of stimulation are thought to affect T cell signaling at multiple levels. For example, at the level of the receptor, ligand binding kinetics have been suggested to discriminate agonistic from non-agonist pMHCs. D…