From sequence to function through structure: Deep learning for protein design
The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve condit...
Main Authors: | Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-01-01
|
Series: | Computational and Structural Biotechnology Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022005086 |
Similar Items
-
Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction
by: Sumin Lee, et al.
Published: (2023-01-01) -
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction
by: Niraj Verma, et al.
Published: (2021-01-01) -
Generative power of a protein language model trained on multiple sequence alignments
by: Damiano Sgarbossa, et al.
Published: (2023-02-01) -
Current Challenges and Opportunities in Designing Protein–Protein Interaction Targeted Drugs
by: Shin WH, et al.
Published: (2020-11-01) -
Review of Deep Learning Applications in Healthcare
by: XUE Fenghao, JIANG Haibo, TANG Dan
Published: (2023-04-01)