Cover Image

Towards deep learning sequence-structure co-generation for protein design

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Chentong, Alamdari, Sarah, Domingo-Enrich, Carles, Amini, Ava, Yang, Kevin K.
Format:	Preprint
Published:	2024
Subjects:	Biomolecules
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ApexGen: Simultaneous design of peptide binder sequence and structure for target proteins
by: Xia, Xiaoqiong, et al.
Published: (2025)

From sequence to protein structure and conformational dynamics with AI/ML
by: Ille, Alexander M., et al.
Published: (2025)

The Dayhoff Atlas: scaling sequence diversity for improved protein generation
by: Yang, Kevin, et al.
Published: (2025)

TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment
by: Han, Bingqing, et al.
Published: (2024)

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins
by: von Bülow, Sören, et al.
Published: (2024)

AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy
by: Wang, Tianyue, et al.
Published: (2025)

ProteinGuide: On-the-fly property guidance for protein sequence generative models
by: Xiong, Junhao, et al.
Published: (2025)

Evaluating representation learning on the protein structure universe
by: Jamasb, Arian R., et al.
Published: (2024)

Model-based reinforcement learning for protein backbone design
by: Renard, Frederic, et al.
Published: (2024)

Computational design of intrinsically disordered proteins
by: Tesei, Giulio, et al.
Published: (2025)

Predicting protein folding dynamics using sequence information
by: Galpern, Ezequiel A., et al.
Published: (2025)

Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context
by: Wells, Aydin, et al.
Published: (2025)

Inferring protein folding mechanisms from natural sequence diversity
by: Galpern, Ezequiel A., et al.
Published: (2024)

Diffusion on language model encodings for protein sequence generation
by: Meshchaninov, Viacheslav, et al.
Published: (2024)

MotifBench: A standardized protein design benchmark for motif-scaffolding problems
by: Zheng, Zhuoqi, et al.
Published: (2025)

State-aware protein-ligand complex prediction using AlphaFold3 with purified sequences
by: Xing, Enming, et al.
Published: (2025)

De novo design of protein binders targeting the human sweet taste receptor as potential sweet proteins
by: Ding, Saisai, et al.
Published: (2026)

De novo design of high-affinity protein binders with AlphaProteo
by: Zambaldi, Vinicius, et al.
Published: (2024)

Out-of-equilibrium selection pressure enhances inference from protein sequence data
by: Dietler, Nicola, et al.
Published: (2026)

Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
by: Giri, Nabin, et al.
Published: (2026)

Applying computational protein design to therapeutic antibody discovery -- current state and perspectives
by: Bielska, Weronika, et al.
Published: (2025)

Multiscale differential geometry learning for protein flexibility analysis
by: Feng, Hongsong, et al.
Published: (2024)

Machine learning for RNA-targeting drug design
by: Karroucha, Wissam, et al.
Published: (2025)

DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
by: Lupo, Umberto, et al.
Published: (2024)

The standard coil or globule phases cannot describe the denatured state of structured proteins and intrinsically disordered proteins
by: Righini, F., et al.
Published: (2025)

Sesame: Opening the door to protein pockets
by: Miñán, Raúl, et al.
Published: (2025)

Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
by: Ferraz, Lucas, et al.
Published: (2026)

COLOR: A compositional linear operation-based representation of protein sequences for identification of monomer contributions to properties
by: Pandey, Akash, et al.
Published: (2025)

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor
by: Ye, Jing, et al.
Published: (2024)

Impact of phylogeny on structural contact inference from protein sequence data
by: Dietler, Nicola, et al.
Published: (2022)

AlphaFold2 for protein structure prediction: Best practices and critical analyses
by: Radjasandirane, Ragousandirane, et al.
Published: (2024)

Frontiers in integrative structural biology: modeling disordered proteins and utilizing in situ data
by: Majila, Kartik, et al.
Published: (2024)

A review of topological data analysis and topological deep learning in molecular sciences
by: Wee, JunJie, et al.
Published: (2025)

SAFE setup for generative molecular design
by: Mesbahi, Yassir El, et al.
Published: (2024)

Kirigami: large convolutional kernels improve deep learning-based RNA secondary structure prediction
by: Harary, Marc, et al.
Published: (2024)

Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
by: Chakravarty, Devlina, et al.
Published: (2024)

High fitness paths can connect proteins with low sequence overlap
by: Kantroo, Pranav, et al.
Published: (2024)

Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
by: Zhou, Ziyi, et al.
Published: (2024)

CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
by: Feng, Hongsong, et al.
Published: (2025)

Selection originating from protein stability/foldability: Relationships between protein folding free energy, sequence ensemble, and fitness
by: Miyazawa, Sanzo
Published: (2016)