Design and optimization of enzymatic activity in a de novo β‐barrel scaffold

Kipnis, Yakov; Chaib, Anissa Ouald; Vorobieva, Anastassia; Cai, Guangyang; Reggiano, Gabriella; Basanta, Benjamin; Kumar, Eshan; Mittl, Peer R. E.; Hilvert, Donald; Baker, David

doi:10.1002/pro.4405

Cited by 11 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While most current success involves generating protein scaffolds or activities that are already known, it will be exciting to see more efforts that focus on generating enzymes that do not resemble those in nature and/or exhibit non-natural activities. In protein engineering, certain protein folds are more evolvable for certain reasons, including elevated stability , that is imparted by residues outside the active site, , balanced with flexibility to change conformation and accommodate new substrates and reactions . Proteins that express well in a host organism for evolution are also preferred.…”

Section: Discovery Of Functional Enzymes With Machine Learningmentioning

confidence: 99%

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Yang,

Li,

Arnold

2024

ACS Cent. Sci.

View full text Add to dashboard Cite

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiencyor even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

show abstract

Section: Discovery Of Functional Enzymes With Machine Learningmentioning

confidence: 99%

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Yang,

Li,

Arnold

2024

ACS Cent. Sci.

View full text Add to dashboard Cite

show abstract

“…Thanks to major recent advances in computational protein design, we now have access to a far wider range of de novo protein scaffolds that can serve as templates for enzyme design. For example, a designed eight stranded β-barrel was recently elaborated into a selective aldolase for the cleavage of the model substrate S-methodol, albeit with reduced efficiency compared with evolved retro-aldolase RA95.5-8F. More recently a closed α-helical solenoid protein with a large central cavity was converted into a high affinity ( K D < 10 nM) heme binding protein dnHEM1 using Rosetta Match and Rosetta FastDesign (Figure B) .…”

Section: De Novo Protein Foldsmentioning

confidence: 99%

Building Enzymes through Design and Evolution

Hossack,

Hardy,

Green

2023

ACS Catal.

View full text Add to dashboard Cite

Designing efficient enzymes is a formidable challenge at the forefront of modern biocatalysis. Here, we review recent developments in the field and illustrate how the interplay between computational design and advanced protein engineering has given rise to enzymes with diverse activities. Natural proteins have been re-engineered computationally to embed designed catalytic sites, affording active catalysts that can be optimized through laboratory evolution to enhance efficiency and selectivity. Computational design tools can reliably generate stable de novo proteins with shapes and backbone geometries beyond those found in nature, which can serve as idealized templates for hosting catalytic sites. Genetic code reprogramming methods have been used to introduce additional functional elements into protein active sites to expand the range of chemistries accessible with designer enzymes. Finally, the recent emergence of powerful protein design tools based on deep learning promises to have a transformative impact on the field by greatly increasing the design speed and model accuracy. By bringing together the latest computational and experimental tools for enzyme design, we are optimistic that the ambition of reliably building useful biocatalysts from scratch is within reach.

show abstract

“…[11] The computational design of a β-barrel, a complete β-sheet protein with a central cavity, is another example. [12] This scaffold was the starting point for the design of an artificial retro-aldolase, [13] a membrane-spanning protein [14] and a pH-and Ca(II)-sensitive fluorescent sensor. [15] Despite these early achievements, β-sheet peptide motifs have been largely neglected in the design of functional miniproteins.…”

Section: Introductionmentioning

confidence: 99%

“…The computational design of a β‐barrel, a complete β‐sheet protein with a central cavity, is another example [12] . This scaffold was the starting point for the design of an artificial retro‐aldolase, [13] a membrane‐spanning protein [14] and a pH‐ and Ca(II)‐sensitive fluorescent sensor [15] …”

Section: Introductionmentioning

confidence: 99%

Design of Functional Globular β‐Sheet Miniproteins

Pham,

Thomas

2024

ChemBioChem

View full text Add to dashboard Cite

The design of discrete β‐sheet peptides is far less advanced than e.g. the design of α‐helical peptides. The reputation of β‐sheet peptides as being poorly soluble and aggregation‐prone often hinders active design efforts. Here, we show that this reputation is unfounded. We demonstrate this by looking at the β‐hairpin and WW domain. Their structure and folding have been extensively studied and they have long served as model systems to investigate protein folding and folding kinetics. The resulting fundamental understanding has led to the development of hyperstable β‐sheet scaffolds that fold at temperatures of 100°C or high concentrations of denaturants. These have been used to design functional miniproteins with protein or nucleic acid binding properties, in some cases with such success that medical applications are conceivable. The β‐sheet scaffolds are not always completely rigid, but can be specifically designed to respond to changes in pH, redox potential or presence of metal ions. Some engineered β‐sheet peptides also exhibit catalytic properties, although not comparable to those of natural proteins. Previous reviews have focused on the design of stably folded and nonaggregating β‐sheet sequences. In our review, we now also address design strategies to obtain functional miniproteins from β‐sheet folding motifs.

show abstract

Design and optimization of enzymatic activity in a de novo β‐barrel scaffold

Cited by 11 publications

References 45 publications

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Building Enzymes through Design and Evolution

Design of Functional Globular β‐Sheet Miniproteins

Contact Info

Product

Resources

About