Re‐engineering of a carotenoid‐binding protein based on NMR structure DOI
Andrey Nikolaev,

Daria A. Lunegova,

Roman I. Raevskii

и другие.

Protein Science, Год журнала: 2024, Номер 33(12)

Опубликована: Ноя. 16, 2024

Abstract Recently, a number of message passing neural network (MPNN)‐based methods have been introduced that, based on backbone atom coordinates, efficiently recover native amino acid sequences proteins and predict modifications that result in better expressing, more soluble, stable variants. However, usually, X‐ray structures, or artificial structures generated by algorithms trained were employed to define target conformations. Here, we show commonly used ProteinMPNN SolubleMPNN display low sequence recovery determined using NMR. We subsequently propose computational approach successfully apply re‐engineer AstaP, protein natively binds large hydrophobic ligand astaxanthin (C 40 H 52 O 4 ), for which only structure NMR is currently available. The engineered variants, designated NeuroAstaP, are 51 shorter than the 22 kDa parent protein, 38%–42% identity it, exhibit good yields, expressed mostly monomeric form, demonstrate efficient binding carotenoids vitro cells. Altogether, our work further tests limits machine learning engineering paves way MPNN‐based modification NMR‐derived structures.

Язык: Английский

The Development and Opportunities of Predictive Biotechnology DOI Creative Commons
Bettina M. Nestl, Bernd A. Nebel, Verena Resch

и другие.

ChemBioChem, Год журнала: 2024, Номер 25(13)

Опубликована: Май 7, 2024

Recent advances in bioeconomy allow a holistic view of existing and new process chains enable novel production routines continuously advanced by academia industry. All this progress benefits from growing number prediction tools that have found their way into the field. For example, automated genome annotations, for building model structures proteins, structural protein methods such as AlphaFold2

Язык: Английский

Процитировано

6
Noncanonical Amino Acids: Bringing New-to-Nature Functionalities to Biocatalysis DOI Creative Commons
Bart Brouwer, Franco Della‐Felice, Jan Hendrik Illies

и другие.

Chemical Reviews, Год журнала: 2024, Номер 124(19), С. 10877 - 10923

Опубликована: Сен. 27, 2024

Biocatalysis has become an important component of modern organic chemistry, presenting efficient and environmentally friendly approach to synthetic transformations. Advances in molecular biology, computational modeling, protein engineering have unlocked the full potential enzymes various industrial applications. However, inherent limitations natural building blocks sparked a revolutionary shift.

Язык: Английский

Процитировано

5
Reengineering of a flavin‐binding fluorescent protein using ProteinMPNN DOI
Andrey Nikolaev, Alexander Kuzmin, Elena Markeeva

и другие.

Protein Science, Год журнала: 2024, Номер 33(4)

Опубликована: Март 19, 2024

Recent advances in machine learning techniques have led to development of a number protein design and engineering approaches. One them, ProteinMPNN, predicts an amino acid sequence that would fold match user-defined backbone structure. Its performance was previously tested for proteins composed standard acids, as well peptide- protein-binding proteins. In this short report, we test whether ProteinMPNN can be used reengineer non-proteinaceous ligand-binding protein, flavin-based fluorescent CagFbFP. We fixed the native conformation identity 20 acids interacting with chromophore (flavin mononucleotide, FMN) while letting predict rest sequence. The software package suggested replacing 36-48 out remaining 86 so resulting sequences are 55%-66% identical original one. three designs experimentally displayed different expression levels, yet all were able bind FMN fluorescence, thermal stability, other properties similar those Our results demonstrate generate diverging unnatural variants proteins, and, more generally, without losing their capabilities.

Язык: Английский

Процитировано

3
Engineering Candida boidinii formate dehydrogenase for activity with NMN(H) DOI
Salomon Vainstein, Scott Banta

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 22, 2024

Abstract Multi-step enzymatic reaction cascades often involve cofactors that serve as electron donors/acceptors in addition to the primary substrates. The co-localization of can lead cross-talk and competition, which be unfavorable for production a targeted product. Orthogonal pathways allow reactions interest operate independently from metabolic within cell; non-canonical cofactor analogs have been explored means create these orthogonal pathways. Here, we aimed engineer formate dehydrogenase Candid boidinii (CbFDH) activity with nicotinamide adenine mononucleotide (NMN(H)). We used PyRosetta structural alignment design mutations enable CbFDH use NMN + oxidation formate. Although suggested did not result enhanced , found was able easily single disrupted all activity.

Язык: Английский

Процитировано

0
From De Novo to Xeno: Advancing Macromolecule Design beyond Proteins DOI Creative Commons
Tyler S. Stukenbroeker

ACS Synthetic Biology, Год журнала: 2024, Номер 13(8), С. 2271 - 2275

Опубликована: Авг. 16, 2024

Protein synthesis methods have been adapted to incorporate an ever-growing level of non-natural components. Meanwhile, design de novo protein structure and function has rapidly emerged as a viable capability. Yet, these two exciting trends yet intersect in meaningful way. The ability perform with non-proteinogenic components requires that computation align on common targets applications. This perspective examines the state art areas identifies specific, consequential applications advance field toward generalized macromolecule design.

Язык: Английский

Процитировано

0
Re‐engineering of a carotenoid‐binding protein based on NMR structure DOI
Andrey Nikolaev,

Daria A. Lunegova,

Roman I. Raevskii

и другие.

Protein Science, Год журнала: 2024, Номер 33(12)

Опубликована: Ноя. 16, 2024

Abstract Recently, a number of message passing neural network (MPNN)‐based methods have been introduced that, based on backbone atom coordinates, efficiently recover native amino acid sequences proteins and predict modifications that result in better expressing, more soluble, stable variants. However, usually, X‐ray structures, or artificial structures generated by algorithms trained were employed to define target conformations. Here, we show commonly used ProteinMPNN SolubleMPNN display low sequence recovery determined using NMR. We subsequently propose computational approach successfully apply re‐engineer AstaP, protein natively binds large hydrophobic ligand astaxanthin (C 40 H 52 O 4 ), for which only structure NMR is currently available. The engineered variants, designated NeuroAstaP, are 51 shorter than the 22 kDa parent protein, 38%–42% identity it, exhibit good yields, expressed mostly monomeric form, demonstrate efficient binding carotenoids vitro cells. Altogether, our work further tests limits machine learning engineering paves way MPNN‐based modification NMR‐derived structures.

Язык: Английский

Процитировано

0