Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Alexey Strokach

Showing results (1-10 of 9) with videos related to

Pageof 1
Sort By:
Current Opinion in Structural Biology|December 28, 2021
Deep generative modeling for protein designAlexey Strokach, Philip M Kim
Human Mutation|June 28, 2019
Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challengeAlexey Strokach, Carles Corbi-Verge, Philip M Kim
Journal of Molecular Biology|January 15, 2021
ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of MutationsAlexey Strokach, Tian Yu Lu, Philip M Kim
Methods in Molecular Biology (Clifton, N.J.)|October 10, 2018
Predicting the Effect of Mutations on Protein Folding and Protein-Protein InteractionsAlexey Strokach, Carles Corbi-Verge, Joan Teyra, et al.
Cell Systems|September 24, 2020
Fast and Flexible Protein Design Using Deep Graph Neural NetworksAlexey Strokach, David Becerra, Carles Corbi-Verge, et al.
STAR Protocols|May 17, 2021
Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolverAlexey Strokach, David Becerra, Carles Corbi-Verge, et al.
Bioinformatics (Oxford, England)|January 24, 2016
ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinityDaniel K Witvliet, Alexey Strokach, Andrés Felipe Giraldo-Forero, et al.
Nature Biotechnology|January 26, 2023
A universal deep-learning model for zinc finger design enables transcription factor reprogrammingDavid M Ichikawa, Osama Abdin, Nader Alerasool, et al.
Human Genetics|August 7, 2024
Assessing predictions on fitness effects of missense variants in HMBS in CAGI6Jing Zhang, Lisa Kinch, Panagiotis Katsonis, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Current Opinion in Structural Biology|December 28, 2021
Deep generative modeling for protein designAlexey Strokach, Philip M Kim
Human Mutation|June 28, 2019
Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challengeAlexey Strokach, Carles Corbi-Verge, Philip M Kim
Journal of Molecular Biology|January 15, 2021
ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of MutationsAlexey Strokach, Tian Yu Lu, Philip M Kim
Methods in Molecular Biology (Clifton, N.J.)|October 10, 2018
Predicting the Effect of Mutations on Protein Folding and Protein-Protein InteractionsAlexey Strokach, Carles Corbi-Verge, Joan Teyra, et al.
Cell Systems|September 24, 2020
Fast and Flexible Protein Design Using Deep Graph Neural NetworksAlexey Strokach, David Becerra, Carles Corbi-Verge, et al.
STAR Protocols|May 17, 2021
Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolverAlexey Strokach, David Becerra, Carles Corbi-Verge, et al.
Bioinformatics (Oxford, England)|January 24, 2016
ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinityDaniel K Witvliet, Alexey Strokach, Andrés Felipe Giraldo-Forero, et al.
Nature Biotechnology|January 26, 2023
A universal deep-learning model for zinc finger design enables transcription factor reprogrammingDavid M Ichikawa, Osama Abdin, Nader Alerasool, et al.
Human Genetics|August 7, 2024
Assessing predictions on fitness effects of missense variants in HMBS in CAGI6Jing Zhang, Lisa Kinch, Panagiotis Katsonis, et al.
Pageof 1