Matches in SemOpenAlex for { <https://semopenalex.org/work/W3216356365> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3216356365 abstract "Abstract Motivation Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Since such opportunistic analogies do not allow capturing highly structured data, current deep models struggle to generate physically realistic tertiary structures. Results We present novel deep generative models that build upon the graph variational autoencoder framework. In contrast to existing literature, we represent tertiary structures as ‘contact’ graphs, which allow us to leverage graph-generative deep learning. Our models are able to capture rich, local and distal constraints and additionally compute disentangled latent representations that reveal the impact of individual latent factors. This elucidates what the factors control and makes our models more interpretable. Rigorous comparative evaluation along various metrics shows that the models, we propose advance the state-of-the-art. While there is still much ground to cover, the work presented here is an important first step, and graph-generative frameworks promise to get us to our goal of unraveling the exquisite structural complexity of protein molecules. Availability and implementation Code is available at https://github.com/anonymous1025/CO-VAE. Supplementary information Supplementary data are available at Bioinformatics Advances online." @default.
- W3216356365 created "2021-12-06" @default.
- W3216356365 creator A5006709853 @default.
- W3216356365 creator A5036792105 @default.
- W3216356365 creator A5044722808 @default.
- W3216356365 creator A5048756500 @default.
- W3216356365 creator A5090356888 @default.
- W3216356365 date "2021-01-01" @default.
- W3216356365 modified "2023-10-18" @default.
- W3216356365 title "Generating tertiary protein structures via interpretable graph variational autoencoders" @default.
- W3216356365 cites W1597762634 @default.
- W3216356365 cites W1995808589 @default.
- W3216356365 cites W2015470526 @default.
- W3216356365 cites W2066962978 @default.
- W3216356365 cites W2143668817 @default.
- W3216356365 cites W2145991251 @default.
- W3216356365 cites W2149197198 @default.
- W3216356365 cites W2201713963 @default.
- W3216356365 cites W2343931134 @default.
- W3216356365 cites W2913735009 @default.
- W3216356365 cites W3021785094 @default.
- W3216356365 cites W3045149055 @default.
- W3216356365 cites W3046922321 @default.
- W3216356365 cites W3048900928 @default.
- W3216356365 cites W3112468720 @default.
- W3216356365 cites W3129230823 @default.
- W3216356365 cites W3177828909 @default.
- W3216356365 cites W3092953205 @default.
- W3216356365 doi "https://doi.org/10.1093/bioadv/vbab036" @default.
- W3216356365 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36700110" @default.
- W3216356365 hasPublicationYear "2021" @default.
- W3216356365 type Work @default.
- W3216356365 sameAs 3216356365 @default.
- W3216356365 citedByCount "11" @default.
- W3216356365 countsByYear W32163563652022 @default.
- W3216356365 countsByYear W32163563652023 @default.
- W3216356365 crossrefType "journal-article" @default.
- W3216356365 hasAuthorship W3216356365A5006709853 @default.
- W3216356365 hasAuthorship W3216356365A5036792105 @default.
- W3216356365 hasAuthorship W3216356365A5044722808 @default.
- W3216356365 hasAuthorship W3216356365A5048756500 @default.
- W3216356365 hasAuthorship W3216356365A5090356888 @default.
- W3216356365 hasBestOaLocation W32163563651 @default.
- W3216356365 hasConcept C101738243 @default.
- W3216356365 hasConcept C108583219 @default.
- W3216356365 hasConcept C119857082 @default.
- W3216356365 hasConcept C132525143 @default.
- W3216356365 hasConcept C153083717 @default.
- W3216356365 hasConcept C154945302 @default.
- W3216356365 hasConcept C165696696 @default.
- W3216356365 hasConcept C167966045 @default.
- W3216356365 hasConcept C38652104 @default.
- W3216356365 hasConcept C39890363 @default.
- W3216356365 hasConcept C41008148 @default.
- W3216356365 hasConcept C80444323 @default.
- W3216356365 hasConceptScore W3216356365C101738243 @default.
- W3216356365 hasConceptScore W3216356365C108583219 @default.
- W3216356365 hasConceptScore W3216356365C119857082 @default.
- W3216356365 hasConceptScore W3216356365C132525143 @default.
- W3216356365 hasConceptScore W3216356365C153083717 @default.
- W3216356365 hasConceptScore W3216356365C154945302 @default.
- W3216356365 hasConceptScore W3216356365C165696696 @default.
- W3216356365 hasConceptScore W3216356365C167966045 @default.
- W3216356365 hasConceptScore W3216356365C38652104 @default.
- W3216356365 hasConceptScore W3216356365C39890363 @default.
- W3216356365 hasConceptScore W3216356365C41008148 @default.
- W3216356365 hasConceptScore W3216356365C80444323 @default.
- W3216356365 hasFunder F4320306076 @default.
- W3216356365 hasIssue "1" @default.
- W3216356365 hasLocation W32163563651 @default.
- W3216356365 hasLocation W32163563652 @default.
- W3216356365 hasLocation W32163563653 @default.
- W3216356365 hasOpenAccess W3216356365 @default.
- W3216356365 hasPrimaryLocation W32163563651 @default.
- W3216356365 hasRelatedWork W1974618110 @default.
- W3216356365 hasRelatedWork W2527569769 @default.
- W3216356365 hasRelatedWork W2567271240 @default.
- W3216356365 hasRelatedWork W2922457425 @default.
- W3216356365 hasRelatedWork W3044458868 @default.
- W3216356365 hasRelatedWork W3094512554 @default.
- W3216356365 hasRelatedWork W4213225422 @default.
- W3216356365 hasRelatedWork W4250304930 @default.
- W3216356365 hasRelatedWork W4287817957 @default.
- W3216356365 hasRelatedWork W4289656111 @default.
- W3216356365 hasVolume "1" @default.
- W3216356365 isParatext "false" @default.
- W3216356365 isRetracted "false" @default.
- W3216356365 magId "3216356365" @default.
- W3216356365 workType "article" @default.