Ivan Anishchenko
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Explore the profile of Ivan Anishchenko including associated specialties, affiliations and a list of published articles.
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37
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4008
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Recent Articles
1.
Lauko A, Pellock S, Sumida K, Anishchenko I, Juergens D, Ahern W, et al.
Science
. 2025 Feb;
:eadu2454.
PMID: 39946508
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...
2.
Anishchenko I, Kipnis Y, Kalvet I, Zhou G, Krishna R, Pellock S, et al.
bioRxiv
. 2024 Oct;
PMID: 39386615
Modeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing...
3.
Humphreys I, Zhang J, Baek M, Wang Y, Krishnakumar A, Pei J, et al.
Nat Microbiol
. 2024 Sep;
9(10):2642-2652.
PMID: 39294458
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 RoseTTAFold2-Lite,...
4.
An L, Said M, Tran L, Majumder S, Goreshnik I, Lee G, et al.
Science
. 2024 Jul;
385(6706):276-282.
PMID: 39024436
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 varying...
5.
Humphreys I, Zhang J, Baek M, Wang Y, Krishnakumar A, Pei J, et al.
bioRxiv
. 2024 Apr;
PMID: 38645026
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...
6.
Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, et al.
Science
. 2024 Mar;
384(6693):eadl2528.
PMID: 38452047
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...
7.
An L, Said M, Tran L, Majumder S, Goreshnik I, Lee G, et al.
bioRxiv
. 2024 Jan;
PMID: 38187589
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...
8.
Baek M, McHugh R, Anishchenko I, Jiang H, Baker D, DiMaio F
Nat Methods
. 2023 Nov;
21(1):117-121.
PMID: 37996753
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...
9.
Yeh A, Norn C, Kipnis Y, Tischer D, Pellock S, Evans D, et al.
Nature
. 2023 Feb;
614(7949):774-780.
PMID: 36813896
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, but has been...
10.
Wang J, Lisanza S, Juergens D, Tischer D, Watson J, Castro K, et al.
Science
. 2022 Jul;
377(6604):387-394.
PMID: 35862514
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...