Ivan Anishchenko
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View article: Modeling protein–small molecule conformational ensembles with PLACER
Modeling protein–small molecule conformational ensembles with PLACER Open
Modeling the conformational heterogeneity of protein–small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level desc…
View article: Atomic context-conditioned protein sequence design using LigandMPNN
Atomic context-conditioned protein sequence design using LigandMPNN Open
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to mod…
View article: Computational design of serine hydrolases
Computational design of serine hydrolases Open
The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation…
View article: Modeling protein-small molecule conformational ensembles with PLACER
Modeling protein-small molecule conformational ensembles with PLACER Open
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems, but remains an outstanding challenge. We reasoned that while residue level des…
View article: Protein interactions in human pathogens revealed through deep learning
Protein interactions in human pathogens revealed through deep learning Open
Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseT…
View article: Binding and sensing diverse small molecules using shape-complementary pseudocycles
Binding and sensing diverse small molecules using shape-complementary pseudocycles Open
We describe an approach for designing high-affinity small molecule–binding proteins poised for downstream sensing. We use deep learning–generated pseudocycles with repeating structural units surrounding central binding pockets with widely …
View article: Essential and virulence-related protein interactions of pathogens revealed through deep learning
Essential and virulence-related protein interactions of pathogens revealed through deep learning Open
Identification of bacterial protein–protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep…
View article: Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Generalized biomolecular modeling and design with RoseTTAFold All-Atom Open
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and …
View article: Atomic context-conditioned protein sequence design using LigandMPNN
Atomic context-conditioned protein sequence design using LigandMPNN Open
Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to mo…
View article: De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles
De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles Open
A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complem…
View article: Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA Open
Protein–RNA and protein–DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein–nucleic acid complexes without homology to known comple…
View article: Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom Open
Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules and other non-pro…
View article: Efficient and accurate prediction of protein structure using RoseTTAFold2
Efficient and accurate prediction of protein structure using RoseTTAFold2 Open
AlphaFold2 and RoseTTAFold predict protein structures with very high accuracy despite substantial architecture differences. We sought to develop an improved method combining features of both. The resulting method, RoseTTAFold2, extends the…
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: CASP15 RNA target predictions with RoseTTAFold-NA
CASP15 RNA target predictions with RoseTTAFold-NA Open
Structure predictions of CASP-15 RNA targets made using RoseTTAFold-NA on December 9, 2022.
View article: CASP15 RNA target predictions with RoseTTAFold-NA
CASP15 RNA target predictions with RoseTTAFold-NA Open
Structure predictions of CASP-15 RNA targets made using RoseTTAFold-NA on December 9, 2022.
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: Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA
Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA Open
Protein-nucleic acid complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is …
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: 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: Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs Open
Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFo…
View article: Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues Open
Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFol…
View article: Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues Open
AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues. Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for…
View article: Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues Open
AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues. Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for…
View article: Characterizing disease-associated human proteins without available protein structures or homologues
Characterizing disease-associated human proteins without available protein structures or homologues Open
AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues. Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for…
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: Computed structures of core eukaryotic protein complexes
Computed structures of core eukaryotic protein complexes Open
Deep learning for protein interactions The use of deep learning has revolutionized the field of protein modeling. Humphreys et al . combined this approach with proteome-wide, coevolution-guided protein interaction identification to conduct…
View article: Structures of core eukaryotic protein complexes
Structures of core eukaryotic protein complexes Open
Protein-protein interactions play critical roles in biology, but despite decades of effort, the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions that have not yet been identified. Here, we…