Dataset Information

Novel Approach for Characterizing Propofol Binding Affinities to Serum Albumins from Different Species.

ABSTRACT: The interaction between the main carrier (serum albumin, SA) of endogenous and exogenous compounds in the bloodstream of different species (human, bovine, canine, rat, rabbit, and sheep) and a general anesthetic agent (propofol, PR) was investigated using an experimental technique (high-performance liquid chromatography) and computational methods (molecular docking, molecular dynamics, sequence, and phylogenetic analyses). The obtained results revealed the differences in the PR binding affinity to various homologous forms of this protein with reliable statistics (R 2 = 0.9 and p-value < 0.005), correlating with the evolutionary relationships among SAs from different species. Additionally, the protein conformational changes (root-mean-square deviation ? 1.0 Å) and amino acid conservation of binding sites in protein domains were detected, contributing to the SA-PR binding modes. Overall, the outcomes from this study might provide a novel methodology to assess protein-ligand interactions and to gain some interesting insights into drug pharmacokinetics and pharmacodynamics to explain its variations among different species.

SUBMITTER: Shityakov S

PROVIDER: S-EPMC7557242 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Novel Approach for Characterizing Propofol Binding Affinities to Serum Albumins from Different Species.

Shityakov Sergey S Fischer Anneli A Su Kuan-Pin KP Hussein Aqeel A AA Dandekar Thomas T Broscheit Jens J

ACS omega 20200930 40

The interaction between the main carrier (serum albumin, SA) of endogenous and exogenous compounds in the bloodstream of different species (human, bovine, canine, rat, rabbit, and sheep) and a general anesthetic agent (propofol, PR) was investigated using an experimental technique (high-performance liquid chromatography) and computational methods (molecular docking, molecular dynamics, sequence, and phylogenetic analyses). The obtained results revealed the differences in the PR binding affinity ...[more]

PMID: 33073080

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Project description:Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn.The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Dataset Information

Novel Approach for Characterizing Propofol Binding Affinities to Serum Albumins from Different Species.

Publications

Novel Approach for Characterizing Propofol Binding Affinities to Serum Albumins from Different Species.

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