An overview of descriptors to capture protein properties – Tools and perspectives in the context of QSAR modeling

Emonts, J.; Buyel, Johannes F.

doi:10.1016/j.csbj.2023.05.022

Cited by 12 publications

(10 citation statements)

References 139 publications

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“…Similar to the first group, in the group of bistriazole derivatives, the lowest value of lipophilicity is shown by compound 15 with the deoxy-β-D-glucopyranosyl substituent. In these groups of compounds (11)(12)(13)(14)(15)(16)(17)(18), similar correlations were observed as for 3-10. Comparing the lipophilicity of mono-and bistriazole compounds containing the same substituents at the triazole ring, bistriazole derivatives had higher log P TLC values.…”

Section: Discussionsupporting

confidence: 82%

“…Lipophilicity is connected to other parameters, like hydrophobicity (φ 0 ), permeability by Caco-2 membrane, skin, blood-brain barrier (BB), and penetration to the central nervous system (CNS). The design of a new compound is often supported by computational methods which determine the lipophilicity and permeability parameters using in silico methods [11,12]. However, most programs determining the logP parameter calculate lipophilicity using atomic methods which do not include every structural parameter, such as the spatial arrangement of atoms or the position of a heterocyclic atom in a ring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin

Kadela-Tomanek,

Sokal,

Stocerz

et al. 2024

Applied Sciences

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Bioavailability describes the properties that determine the passage of a compound through biological barriers. In many cases, bioavailability depends on the lipophilicity of the compound. In this study, the lipophilicity as well as other bioavailability properties of the mono- and bistriazole derivatives of betulin are presented. The lipophilicity was determined using RP-TLC and theoretical methods. The experimental lipophilicity of mono- and bistriazole derivatives is in the range from 4.39 to 7.85 and from 3.75 to 8.83, respectively. The lipophilicity of mono- and bistriazoles is similar, and the logPTLC depends on the type of substituent at the triazole ring. The introduction of a substituent with oxygen and nitrogen atoms decreases lipophilicity. Comparing the experimental and theoretical lipophilicity shows that the milogP and XLOGP3 programs best reproduce the experimental values. The in silico-determined pharmacokinetic parameters show that monotriazole derivatives could be used as oral drugs while bistriazoles show low availability after oral administration. Triazoles could be used as transdermal drugs. The analysis of in silico bioavailability parameters shows that the type of substituent at the triazole ring influences the pharmacokinetic properties, while the number of triazole rings slightly affects the bioavailability properties of the compound.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin

Kadela-Tomanek,

Sokal,

Stocerz

et al. 2024

Applied Sciences

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“…Consequently, molecular dynamics simulations provide a sophisticated means to unravel the intricate and multifaceted interactions between proteins and interfaces, offering valuable insights that might be challenging to attain through traditional experimental methods alone. 143 Moreover, chemoinformatics plays a pivotal role in drug discovery by allowing the prediction of binding affinities between protein-protein and protein–interface interactions through techniques like quantitative structure-activity relationship (QSAR) 144 models and molecular docking. 145,146 In the realm of materials science, it contributes to the rational design of surfaces and materials, optimizing protein interactions for various applications, including medical implants.…”

Section: Artificial Strategies Of Protein-based Bioactive Coatings In...mentioning

confidence: 99%

Protein-based bioactive coatings: from nanoarchitectonics to applications

Fu,

Wang,

Zhou

et al. 2024

Chem. Soc. Rev.

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“…[17] A comprehensive overview can be found elsewhere. [18] Using structural protein features to predict protein retention times was first described in 2001 by Mazza et al, who calculated protein features using the transferable atom equivalent method [5,19,20] and the proprietary software platform MOE. By applying a genetic algorithm for feature selection, a PLS model was trained, capable to predict retention times for ion exchange chromatography from protein structure models.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein retention in ion‐exchange chromatography using an open source QSPR workflow

Neijenhuis,

Le Bussy,

Geldhof

et al. 2024

Biotechnology Journal

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Protein‐based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open‐source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open‐source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross‐validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

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An overview of descriptors to capture protein properties – Tools and perspectives in the context of QSAR modeling

Cited by 12 publications

References 139 publications

Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin

Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin

Protein-based bioactive coatings: from nanoarchitectonics to applications

Predicting protein retention in ion‐exchange chromatography using an open source QSPR workflow

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