Modeling of protein conformational changes with Rosetta guided by limited experimental data

Sala, Davide; Alamo, Diego del; Mchaourab, Hassane S.; Meiler, Jens

doi:10.1016/j.str.2022.04.013

Cited by 22 publications

(17 citation statements)

References 89 publications

(84 reference statements)

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“…These distance distributions directly provide information on protein tertiary and quaternary structure. Coupled with high-resolution structural techniques, SDSL EPR provides insight into protein conformational landscapes and how they change in response to different stimuli [16][17][18][19][20][21][22][23][24][25][26][27]. Accordingly, SDSL EPR is particularly useful for validation and refinement of protein structural models as well as expansion of these models to include conformational heterogeneity and distinct alternate conformational states.…”

Section: Introductionmentioning

confidence: 99%

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

Tessmer

Stoll

2023

PLoS Comput Biol

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Here we introduce chiLife, a Python package for site-directed spin label (SDSL) modeling for electron paramagnetic resonance (EPR) spectroscopy, in particular double electron–electron resonance (DEER). It is based on in silico attachment of rotamer ensemble representations of spin labels to protein structures. chiLife enables the development of custom protein analysis and modeling pipelines using SDSL EPR experimental data. It allows the user to add custom spin labels, scoring functions and spin label modeling methods. chiLife is designed with integration into third-party software in mind, to take advantage of the diverse and rapidly expanding set of molecular modeling tools available with a Python interface. This article describes the main design principles of chiLife and presents a series of examples.

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Section: Introductionmentioning

confidence: 99%

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

Tessmer

Stoll

2023

PLoS Comput Biol

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“…Recently, a structure-based, machine learning algorithm was successfully used to obtain the FAST-PETase, which was a mutation of PETase and exhibited superior PET-hydrolytic activity than wild-type and engineered alternatives at 30–50°C ( Lu et al, 2022 ). Similarly, machine learning methods such as AlphaFold2 ( Varadi et al, 2022 ), ProBound ( Rube et al, 2022 ), RoseTTAFold ( Sala et al, 2022 ), showed great potential in deciphering structure–function relationship and in precisely guiding protein engineering.…”

Section: Bottlenecks and Challenges Of Recycling Plasticsmentioning

confidence: 99%

Enzyme catalyzes ester bond synthesis and hydrolysis: The key step for sustainable usage of plastics

et al. 2023

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Petro-plastic wastes cause serious environmental contamination that require effective solutions. Developing alternatives to petro-plastics and exploring feasible degrading methods are two solving routes. Bio-plastics like polyhydroxyalkanoates (PHAs), polylactic acid (PLA), polycaprolactone (PCL), poly (butylene succinate) (PBS), poly (ethylene furanoate) s (PEFs) and poly (ethylene succinate) (PES) have emerged as promising alternatives. Meanwhile, biodegradation plays important roles in recycling plastics (e.g., bio-plastics PHAs, PLA, PCL, PBS, PEFs and PES) and petro-plastics poly (ethylene terephthalate) (PET) and plasticizers in plastics (e.g., phthalate esters, PAEs). All these bio- and petro-materials show structure similarity by connecting monomers through ester bond. Thus, this review focused on bio-plastics and summarized the sequences and structures of the microbial enzymes catalyzing ester-bond synthesis. Most of these synthetic enzymes belonged to α/β-hydrolases with conserved serine catalytic active site and catalyzed the polymerization of monomers by forming ester bond. For enzymatic plastic degradation, enzymes about PHAs, PBS, PCL, PEFs, PES and PET were discussed, and most of the enzymes also belonged to the α/β hydrolases with a catalytic active residue serine, and nucleophilically attacked the ester bond of substrate to generate the cleavage of plastic backbone. Enzymes hydrolysis of the representative plasticizer PAEs were divided into three types (I, II, and III). Type I enzymes hydrolyzed only one ester-bond of PAEs, type II enzymes catalyzed the ester-bond of mono-ester phthalates, and type III enzymes hydrolyzed di-ester bonds of PAEs. Divergences of catalytic mechanisms among these enzymes were still unclear. This review provided references for producing bio-plastics, and degrading or recycling of bio- and petro-plastics from an enzymatic point of view.

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“…[21][22][23][24] DEER distance distributions can also refine distorted protein conformations by searching conformations that have similar theoretical and experimental distance distributions. [25][26][27][28][29][30] DEER distance distributions have been used for positioning R1 side chains through multilateration [31][32][33] and been proposed as distance restraints for modeling protein structures, similar to the distance restraints obtained by NMR spectroscopy. 23,25,27,29,31,33 In the combination of alternative states predicted by AlphaFold2, a machine learning model for structural prediction, DEER data have been proposed to have the ability to sample alternative conformational states.…”

Section: Introductionmentioning

confidence: 99%

“…[25][26][27][28][29][30] DEER distance distributions have been used for positioning R1 side chains through multilateration [31][32][33] and been proposed as distance restraints for modeling protein structures, similar to the distance restraints obtained by NMR spectroscopy. 23,25,27,29,31,33 In the combination of alternative states predicted by AlphaFold2, a machine learning model for structural prediction, DEER data have been proposed to have the ability to sample alternative conformational states. 34,35 Despite these extensive applications, the DEER technique has not been applied to model a threedimensional structure with high precision.…”

Section: Introductionmentioning

confidence: 99%

DEERefiner-assisted structural refinement using pulsed dipolar spectroscopy: a study on multidrug transporter LmrP

Kao,

Chiang

2023

Phys. Chem. Chem. Phys.

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Modeling of protein conformational changes with Rosetta guided by limited experimental data

Cited by 22 publications

References 89 publications

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

Enzyme catalyzes ester bond synthesis and hydrolysis: The key step for sustainable usage of plastics

DEERefiner-assisted structural refinement using pulsed dipolar spectroscopy: a study on multidrug transporter LmrP

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