Importance weighted expectation-maximization for protein sequence design
Designing protein sequences with desired biological function is crucial in biology and
chemistry. Recent machine learning methods use a surrogate sequence-function model to …
chemistry. Recent machine learning methods use a surrogate sequence-function model to …
Proximal exploration for model-guided protein sequence design
Designing protein sequences with a particular biological function is a long-lasting challenge
for protein engineering. Recent advances in machine-learning-guided approaches focus on …
for protein engineering. Recent advances in machine-learning-guided approaches focus on …
Accurate and efficient protein sequence design through learning concise local environment of residues
B Huang, T Fan, K Wang, H Zhang, C Yu, S Nie… - …, 2023 - academic.oup.com
Motivation Computational protein sequence design has been widely applied in rational
protein engineering and increasing the design accuracy and efficiency is highly desired …
protein engineering and increasing the design accuracy and efficiency is highly desired …
[HTML][HTML] Protein sequence design with deep generative models
Protein engineering seeks to identify protein sequences with optimized properties. When
guided by machine learning, protein sequence generation methods can draw on prior …
guided by machine learning, protein sequence generation methods can draw on prior …
[HTML][HTML] Adaptive machine learning for protein engineering
Abstract Machine-learning models that learn from data to predict how protein sequence
encodes function are emerging as a useful protein engineering tool. However, when using …
encodes function are emerging as a useful protein engineering tool. However, when using …
Multi-indicator comparative evaluation for deep learning-based protein sequence design methods
J Yu, J Mu, T Wei, HF Chen - Bioinformatics, 2024 - academic.oup.com
Motivation Proteins found in nature represent only a fraction of the vast space of possible
proteins. Protein design presents an opportunity to explore and expand this protein …
proteins. Protein design presents an opportunity to explore and expand this protein …
PDBench: evaluating computational methods for protein-sequence design
Ever increasing amounts of protein structure data, combined with advances in machine
learning, have led to the rapid proliferation of methods available for protein-sequence …
learning, have led to the rapid proliferation of methods available for protein-sequence …
A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering
Protein engineering through directed evolution and (semi-) rational approaches has been
applied successfully to optimize protein properties for broad applications in molecular …
applied successfully to optimize protein properties for broad applications in molecular …
Joint generation of protein sequence and structure with RoseTTAFold sequence space diffusion
Protein denoising diffusion probabilistic models (DDPMs) show great promise in the de novo
generation of protein backbones but are limited in their inability to guide generation of …
generation of protein backbones but are limited in their inability to guide generation of …
Generative models for protein sequence modeling: recent advances and future directions
M Mardikoraem, Z Wang, N Pascual… - Briefings in …, 2023 - academic.oup.com
The widespread adoption of high-throughput omics technologies has exponentially
increased the amount of protein sequence data involved in many salient disease pathways …
increased the amount of protein sequence data involved in many salient disease pathways …