A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Woolfson, Derek N.

doi:10.1016/j.jmb.2021.167160

Cited by 117 publications

(111 citation statements)

References 214 publications

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“…The birth of protein engineering technology has opened up a new route for researchers to develop excellent biocatalysts by redesigning natural enzymes (Marshall et al, 2021;Watanabe et al, 2021;Xiong et al, 2021). Many enzyme engineering design strategies have emerged, such as directed evolution, rational, semi-rational, de novo, computer-assisted, and artificial intelligence (Bunzel et al, 2021;Narayanan et al, 2021;Tunyasuvunakool et al, 2021;Woolfson, 2021;Wu et al, 2021). These strategies have been used to improve enzyme stability, activity, and selectivity for substrates.…”

Section: Discussion and Prospectsmentioning

confidence: 99%

Mini Review: Advances in 2-Haloacid Dehalogenases

et al. 2021

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The 2-haloacid dehalogenases (EC 3.8.1.X) are industrially important enzymes that catalyze the cleavage of carbon–halogen bonds in 2-haloalkanoic acids, releasing halogen ions and producing corresponding 2-hydroxyl acids. These enzymes are of particular interest in environmental remediation and environmentally friendly synthesis of optically pure chiral compounds due to their ability to degrade a wide range of halogenated compounds with astonishing efficiency for enantiomer resolution. The 2-haloacid dehalogenases have been extensively studied with regard to their biochemical characterization, protein crystal structures, and catalytic mechanisms. This paper comprehensively reviews the source of isolation, classification, protein structures, reaction mechanisms, biochemical properties, and application of 2-haloacid dehalogenases; current trends and avenues for further development have also been included.

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Section: Discussion and Prospectsmentioning

confidence: 99%

Mini Review: Advances in 2-Haloacid Dehalogenases

et al. 2021

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“… 49 , 158 Incorporating the dynamics and conformational changes upon binding at the interfaces between two molecules remains one of the major challenges in protein design in general, as well as in the design of antigen-specific antibodies. 159 …”

Section: Learnability Of Antibody–antigen Bindingmentioning

confidence: 99%

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Akbar

Bashour

Rawat

et al. 2022

mAbs

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Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

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“…We anticipate that Socket2 and data generated from it will be of use in gathering CC sequence statistics and structural parameters to improve sequence-to-structure relationships for CC-prediction ( Ludwiczak et al , 2019 ), modelling ( Guzenko and Strelkov, 2018 ) and design ( Korendovych and DeGrado, 2020 ; Woolfson, 2017 , 2021 ). It will also facilitate the development and population of sequence and structural databases such as CC+ ( Testa et al , 2009 ), which, likewise, can be used to test CC-prediction algorithms and to develop rules for CC design.…”

Section: Applicationsmentioning

confidence: 99%

“…The helices can be assembled in parallel or antiparallel arrangements, and as homo- or heteromeric complexes ( Lupas et al , 2017 ). In addition to their importance in biology, CCs are productive targets for de novo protein design ( Korendovych and DeGrado, 2020 ; Woolfson, 2017 , 2021 ), leading to applications in cell biology, synthetic biology and biotechnology ( Beesley and Woolfson, 2019 ; Dawson et al , 2019 ; Lapenta et al , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Socket2: a program for locating, visualizing and analyzing coiled-coil interfaces in protein structures

Kumar

Woolfson

2021

Bioinformatics

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Motivation Protein-protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualise, and analyse CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterises CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and non-expert users alike. Results The original Socket only classified CCs with up to 6 helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating non-natural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualising CCs, and tabs for viewing the sequence repeats, helix-packing angles, and core-packing geometries of CCs identified and calculated by Socket2. Availability and implementation Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT license and available to download under the Downloads tab of the Socket2 home page.

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A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Cited by 117 publications

References 214 publications

Mini Review: Advances in 2-Haloacid Dehalogenases

Mini Review: Advances in 2-Haloacid Dehalogenases

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Socket2: a program for locating, visualizing and analyzing coiled-coil interfaces in protein structures

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