CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution.
J Chem Inf Model
; 62(19): 4629-4641, 2022 10 10.
Article
em En
| MEDLINE
| ID: mdl-36154171
ABSTRACT
Directed evolution, a revolutionary biotechnology in protein engineering, optimizes protein fitness by searching an astronomical mutational space via expensive experiments. The cluster learning-assisted directed evolution (CLADE) efficiently explores the mutational space via a combination of unsupervised hierarchical clustering and supervised learning. However, the initial-stage sampling in CLADE treats all clusters equally despite many clusters containing a large portion of non-functional mutations. Recent statistical and deep learning tools enable evolutionary density modeling to access protein fitness in an unsupervised manner. In this work, we construct an ensemble of multiple evolutionary scores to guide the initial sampling in CLADE. The resulting evolutionary score-enhanced CLADE, called CLADE 2.0, efficiently selects a training set within a small informative space using the evolution-driven clustering sampling. CLADE 2.0 is validated by using two benchmark libraries both having 160,000 sequences from four-site mutational combinations. Extensive computational experiments and comparisons with existing cutting-edge methods indicate that CLADE 2.0 is a new state-of-art tool for machine learning-assisted directed evolution.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos