DRAGON models (global) for predicting plasma protein binding (square-root of fraction unbound).
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ABSTRACT:
SUBMITTER: Domenico Gadaleta
PROVIDER: S-TOXR904 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description: Not available
| S-TOXR905 | biostudies-other
Project description: Not available
| S-TOXR903 | biostudies-other
Project description:We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITEcsc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew's correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.
| S-EPMC7522209 | biostudies-literature
Project description:The objective of this work was to establish that unbound maximum concentrations may be reasonably predicted from a combination of computed molecular properties assuming subcutaneous (SQ) dosing. Additionally, we show that the maximum unbound plasma and brain concentrations may be projected from a mixture of in vitro absorption, distribution, metabolism, excretion experimental parameters in combination with computed properties (volume of distribution, fraction unbound in microsomes). Finally, we demonstrate the utility of the underlying equations by showing that the maximum total plasma concentrations can be projected from the experimental parameters for a set of compounds with data collected from clinical research.
| S-EPMC4025848 | biostudies-literature
Project description:DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential functions can be applied on protein-DNA co-crystallized structures to generate PWMs that are considerably consistent with experimental data. However, this success has not been extended to DNA-binding proteins lacking co-crystallized structures. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state). Given an unbound query protein and a template complex, the proposed method first employs structure alignment to generate synthetic protein-DNA complexes for the query protein. Once a complex is available, an atomic-level knowledge-based potential function is employed to predict PWMs characterizing the sequences to which the query protein can bind. The evaluation of the proposed method is based on seven DNA-binding proteins, which have structures of both DNA-bound and unbound forms for prediction as well as annotated PWMs for validation. Since this work is the first attempt to predict target sequences of DNA-binding proteins from their unbound structures, three types of structural variations that presumably influence the prediction accuracy were examined and discussed. Based on the analyses conducted in this study, the conformational change of proteins upon binding DNA was shown to be the key factor. This study sheds light on the challenge of predicting the target DNA sequences of a protein lacking co-crystallized structures, which encourages more efforts on the structure alignment-based approaches in addition to docking- and homology modeling-based approaches for generating synthetic complexes.
| S-EPMC3270014 | biostudies-literature
Project description:Predicting the fraction unbound of a drug in plasma plays a significant role in understanding its pharmacokinetic properties during in vitro studies of drug design and discovery. Owing to the gaining reliability of machine learning in biological predictive models and development of automated machine learning techniques for the ease of nonexperts of machine learning to optimize and maximize the reliability of the model, in this experiment, we built an in silico prediction model of a fraction unbound drug in human plasma using a chemical fingerprint and a freely available AutoML framework. The predictive model was trained on one of the largest data sets ever of 5471 experimental values using four different AutoML frameworks to compare their performance on this problem and to choose the most significant one. With a coefficient of determination of 0.85 on the test data set, our best prediction model showed better performance than other previously published models, giving our model significant importance in pharmacokinetic modeling.
| S-EPMC7970465 | biostudies-literature
Project description:ObjectivesTo describe the unbound and total flucloxacillin pharmacokinetics in critically ill patients and to define optimal dosing strategies.Patients and methodsObservational multicentre study including a total of 33 adult ICU patients receiving flucloxacillin, given as intermittent or continuous infusion. Pharmacokinetic sampling was performed on two occasions on two different days. Total and unbound flucloxacillin concentrations were measured and analysed using non-linear mixed-effects modelling. Serum albumin was added as covariate on the maximum binding capacity and endogenous creatinine clearance (CLCR) as covariate for renal function. Monte Carlo simulations were performed to predict the unbound flucloxacillin concentrations for different dosing strategies and different categories of endogenous CLCR.ResultsThe measured unbound concentrations ranged from 0.2 to 110 mg/L and the observed unbound fraction varied between 7.0% and 71.7%. An integral two-compartmental linear pharmacokinetic model based on total and unbound concentrations was developed. A dose of 12 g/24 h was sufficient for 99.9% of the population to achieve a concentration of >2.5 mg/L (100% fT>5×MIC, MIC = 0.5 mg/L).ConclusionsCritically ill patients show higher unbound flucloxacillin fractions and concentrations than previously thought. Consequently, the risk of subtherapeutic exposure is low.
| S-EPMC8598283 | biostudies-literature
Project description:[This corrects the article DOI: 10.1021/acsomega.0c05846.].
| S-EPMC10720273 | biostudies-literature
Project description:BackgroundStandard antibiotic dosing is not suitable for critically ill patients, due to altered pharmacokinetics (PK) in these patients. Knowledge of protein binding is important for optimizing antibiotic exposure because only the unbound fraction is pharmacologically active. If unbound fractions can be predicted, minimal sampling techniques and less costly methods can be routinely used.MethodsData from the DOLPHIN trial, a prospective randomized clinical trial that included critically ill patients, were used. Total and unbound ceftriaxone concentrations were determined using a validated UPLC-MS/MS method. A non-linear saturable binding model was made using 75% of the trough concentrations and validated on the remaining data. Our model and previously published models were tested for their performance for subtherapeutic (<1 mg/L) and high (>10 mg/L) unbound concentrations.ResultsIn total, 113 patients were sampled [Acute Physiology And Chronic Health Evaluation version 4 (APACHE IV) score 71 (IQR 55-87), albumin 28 g/L (IQR 24-32)]. This resulted in 439 samples (trough = 224, peak = 215). Unbound fractions were significantly different between samples taken at trough and peak times [10.9% (IQR 7.9-16.4) versus 19.7% (IQR 12.9-26.6), P < 0.0001], which was not explained by concentration differences. Our model and most literature models showed good sensitivity and low specificity to determine high and subtherapeutic ceftriaxone trough concentrations using only the total ceftriaxone and albumin concentrations.ConclusionsCeftriaxone protein binding is not concentration related in critically ill patients. Existing models show good ability to predict high concentrations, but low specificity in predicting subtherapeutic concentrations.
| S-EPMC10068419 | biostudies-literature
Project description:The mathematical foundation of quantum mechanics is built on linear algebra, while the application of nonlinear operators can lead to outstanding discoveries under some circumstances, such as the prediction of positron, a direct outcome of the Dirac equation which stems from the square-root of the Klein-Gordon equation. In this article, we propose a model of square-root higher-order Weyl semimetal (SHOWS) by inheriting features from its parent Hamiltonians. It is found that the SHOWS hosts both “Fermi-arc” surface and hinge states that respectively connect the projection of the Weyl points on the side surface and arris. We theoretically construct and experimentally observe the exotic SHOWS state in three-dimensional (3D) stacked electric circuits with honeycomb-kagome hybridizations and double-helix interlayer couplings. Our results open the door for realizing the square-root topology in 3D solid-state platforms. The topological properties of square-root Weyl semimetals are derived from the square of the Hamiltonian. Here, the authors propose a tight-binding model for a square-root higher-order Weyl semimetal hosting both Fermi-arc surface and hinge states.
| S-EPMC9509390 | biostudies-literature