The group expresses strong support and fascination with the article, often referring to its innovative use of deep learning and experimental validation, with phrases like 'game-changing' and 'broad utility.' Their tone indicates a high level of engagement and perceived impact, exemplified by references to rescue of failed designs, high accuracy, and real-world applications.
AgreementStrong agreement
Most discussions praise the article, highlighting its groundbreaking nature in protein design and emphasizing its superior performance over previous methods.
InterestHigh interest
The posts demonstrate high curiosity and excitement about proteinMPNN, often referencing its applications and breakthroughs in the field.
EngagementHigh engagement
Numerous comments reflect deep involvement, mentioning experiments, methodologies, and broader implications, indicating active participation and analysis.
ImpactHigh impact
Many discussions suggest that ProteinMPNN is a transformative tool with the potential to revolutionize drug design and structural biology, emphasizing its significance.
Abstract in brief
ProteinMPNN is a deep learning-based method for designing protein sequences, showing superior performance compared to traditional approaches like Rosetta, with a sequence recovery rate of 52.4%.
It can accurately design amino acid sequences on native protein backbones and can handle complex scenarios, such as multiple chains or coupled amino acids, making it versatile for various protein design tasks.
The method has been validated through experimental techniques like X-ray crystallography and cryo-electron microscopy, successfully rescuing previously failed designs and demonstrating high accuracy and broad utility in protein engineering.]
One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
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Aug 9, 2022 · 69 likes · 14 shares · 8,489 followersView post →
VD
VECTOR SPACE BIOSCIENCES - Accelerating Discovery
@VectorSpaceBio · X
https://t.co/WcoytQlcVD
Robust deep learning–based protein sequence design using ProteinMPNN
This video explores the significance of protein structure, AI in drug design, and recent advances in protein engineering using deep learning methods like ProteinMPNN, with insights from scientific research and…
Join us on December 5th at 1900 for the AI Awards Lithuania 2022 nominees announcement, featuring brief presentations from top publications including innovative research on spectral conditioning for graph generators…
RT @liambai21: Recent breakthroughs:
- Protein structure is a graph. ProteinMPNN (2022), a GNN, is the best tool for fixed-backbone protei…
Jan 15, 2024 · 1 share · 42,621 followersView post →
LB
Liam Bai
@liambai21 · X
Recent breakthroughs:
- Protein structure is a graph. ProteinMPNN (2022), a GNN, is the best tool for fixed-backbone protein design. For a desired structure – e.g. one that binds to a therapeutic target – give me a protein sequence that folds into it. https://t.co/lnluWxx3lb
Jan 15, 2024 · 4 likes · 1 share · 2,063 followersView post →
FM
Frank Paul Mora
@InTime_49 · X
@DdelAlamo Nothing about FAD in the paper. There is scant information on ProteinMPNN
This title is in a few places at no cost. It is good background.
Robust deep learning based protein sequence design using ProteinMPNN
RT @jreuben1: ProteinMPNN: Robust deep learning–based protein sequence design https://t.co/3JX25F89VQ finding a sequence that folds to a de…
Jan 10, 2023 · 5 shares · 8,215 followersView post →
CB
CompChemBio Bot
@CompChemBioBot · X
RT @jreuben1: ProteinMPNN: Robust deep learning–based protein sequence design https://t.co/3JX25F89VQ finding a sequence that folds to a de…
Jan 10, 2023 · 5 shares · 2,741 followersView post →
J
(((JReuben1)))
@jreuben1 · X
ProteinMPNN: Robust deep learning–based protein sequence design https://t.co/3JX25F89VQ finding a sequence that folds to a desired structure by learning to approximate energy optimization functions #Bioinformatics
Jan 10, 2023 · 11 likes · 5 shares · 1,999 followersView post →
W
Wikinger
@wikinger7 · X
Robust deep learning–based protein sequence design using ProteinMPNN
https://t.co/NqdIrcnH7F
Once again, we are thrilled to share the nominees for the Best #AIResearch Paper and Young Researcher Award!
In case you missed the announcement event, where 3 of the nominees introduced their work, see the recording here: https://t.co/pik6TeDN9Z
#AIAwards #AILithuania https://t.co/NmU9yJ1CSm
RT @EricTopol: The next chapter following the breakthrough of #AI protein 3D structure prediction from amino acid sequence: designing and d…
Nov 12, 2022 · 95 shares · 165 followersView post →
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Krzysztof Buczek
@InnovationPL · X
Naukowcy z Uniwersytetu Waszyngtońskiego opracowali narzędzia sztucznej
inteligencji pod nazwą ProteinMPNN oparte deep learning do przewidywania i projektowania struktury białek. System wykorzystuje sieć neuronową do sekwencjonowania związków aminokwasów https://t.co/nPiwF9QYul
Oct 21, 2022 · 1 like · 1,715 followersView post →
#ScienceMag #ProteinMPNN Robust deep learning–based protein sequence design using ProteinMPNN https://t.co/nSeenqT7nw
On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. https://t.co/2YfMAfMm2C
➦ Robust deep learning–based protein sequence design using ProteinMPNN | Science
» A network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.
https://t.co/Bi5yC6pOPE
By the way,over the past two years,machine learning has revolutionized protein structure prediction. Now,three papers in Science describe a similar revolution in protein design.
DOI: 10.1126/science.add2187
#AI #MachineLearning #learning #technology https://t.co/8U0Ist3eNS
Sep 20, 2022 · 1 like · 1 share · 47 followersView post →
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Nkumbulo
@jamessodabi · X
RT @EricTopol: The next chapter following the breakthrough of #AI protein 3D structure prediction from amino acid sequence: designing and d…
In one paper, they used what they call “deep network hallucination” to design cyclic protein assemblies.
2/
@JustasDauparas @BasileWicky @spellock22
https://t.co/dknrux6Qx3
Robust deep learning–based protein sequence design using ProteinMPNN: broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold
https://t.co/1mcbYoh8IP
[LG] Robust deep learning based protein sequence design using ProteinMPNN
J Dauparas, I Anishchenko, N Bennett, H Bai... [University of Washington & Lawrence Berkeley Laboratory] (2022)
https://t.co/xx4epm82to biorxiv: https://t.co/AZ3oTs46Zk
#MachineLearning #ML #AI https://t.co/dMWG9VByVR
Sep 17, 2022 · 1 like · 8,323 followersView post →
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Ricardo Lebrón, Ph.D.
@rlebron_bioinfo · X
Robust deep learning–based protein sequence design using ProteinMPNN https://t.co/JHAyViwn2V
#AlphaFold 革命を推し進め、#インスタントタンパク 設計に到達したUWグループのScience2ペーパーに注目。#ProteinMPNN を開発。StockholmのElofsson： “You draw your protein, push a button, and get something that in ten times works.” 競合多数、分野は急進中。
https://t.co/JjLUQs8H8z
Robust deep learning–based protein sequence design using #ProteinMPNN
@JustasDauparas
#DeepLearning #AlphaFold #ProteinSequenceDesign
https://t.co/Oh6YanaHjJ https://t.co/9i3NJ8l3JK
However, experiments on 150 hallucinations were a monumental failure (all insoluble). Activation maximization had generated adversarial sequences. ProteinMPNN sequence design (https://t.co/vk354KAeMb @JustasDauparas et al.) rescued these backbones, yielding over 70% soluble! 3/5 https://t.co/692tQks9Dn
RT @ml4proteins: One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
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Aug 9, 2022 · 14 shares · 5,531 followersView post →
DT
Dr Chris Thorpe
@drchristhorpe · X
RT @ml4proteins: One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
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Aug 9, 2022 · 14 shares · 615 followersView post →
K楊
Kevin K. Yang 楊凱筌
@KevinKaichuang · X
RT @ml4proteins: One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
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Aug 9, 2022 · 14 shares · 21,669 followersView post →
PX
Paul Lourdu Xavier
@PaulLXavier · X
RT @ml4proteins: One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
Sign up at…
Aug 9, 2022 · 14 shares · 1,182 followersView post →
MS
Machine Learning for Protein Engineering Seminar
@ml4proteins · X
One week until @JustasDauparas's talk on Robust deep learning based protein sequence design using ProteinMPNN
Sign up at https://t.co/Rb5MIOuOCQ to get reminders and zoom links!
Aug 9, 2022 · 69 likes · 14 shares · 8,489 followersView post →
MK
Mohamed Alpha kamara
@MK_444M · X
RT @machinelearnflx: Robust deep learning based protein sequence design using ProteinMPNN https://t.co/Yn10bAtvBQ #DeepLearning
RT @Datascience__: Robust deep learning based protein sequence design using ProteinMPNN https://t.co/agCkWVOccA #DeepLearning
Jun 8, 2022 · 2 shares · 4,945 followersView post →
DS
Data science
@Datascience__ · X
Robust deep learning based protein sequence design using ProteinMPNN https://t.co/agCkWVOccA #DeepLearning
Jun 8, 2022 · 4 likes · 2 shares · 33,193 followersView post →
C
CindyWei
@TzuTingCindy · X
RT @JustasDauparas: 1/5 Robust deep learning based protein sequence design using ProteinMPNN https://t.co/aDnbzjKY7a The high rate of exper…
Jun 8, 2022 · 161 shares · 171 followersView post →
TC
The AI Conference
@AIconference · X
RT utnk1 RT @JustasDauparas: 1/5 Robust deep learning based protein sequence design using ProteinMPNN https://t.co/lMIwXhACPM The high rate of experimental success and applicability to almost any protein sequence design problem has made it the standard … https://t.co/FxwZwlZdMo
RT @JustasDauparas: 1/5 Robust deep learning based protein sequence design using ProteinMPNN https://t.co/aDnbzjKY7a The high rate of exper…
Jun 7, 2022 · 161 shares · 481 followersView post →
DD
Danny Diaz
@aiproteins · X
This paper is pretty awesome work! Only half way through it and having a blast reading.
Robust deep learning based protein sequence design using ProteinMPNN https://t.co/3olJxTamtw
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