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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

ABSTRACT: BACKGROUND:Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far; however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce. RESULTS:First, we adopted sparse modeling and small molecule information to construct a PPB prediction model for cyclic peptides. As cyclic peptide data are limited, applying multidimensional nonlinear models involves concerns regarding overfitting. However, models constructed by sparse modeling can avoid overfitting, offering high generalization performance and interpretability. More than 1000 PPB data of small molecules are available, and we used them to construct a prediction models with two enumeration methods: enumerating lasso solutions (ELS) and forward beam search (FBS). The accuracies of the prediction models constructed by ELS and FBS were equal to or better than those of conventional non-linear models (MAE?=?0.167-0.174) on cross-validation of a small molecule compound dataset. Moreover, we showed that the prediction accuracies for cyclic peptides were close to those for small molecule compounds (MAE?=?0.194-0.288). Such high accuracy could not be obtained by a simple method of learning from cyclic peptide data directly by lasso regression (MAE?=?0.286-0.671) or ridge regression (MAE?=?0.244-0.354). CONCLUSION:In this study, we proposed a machine learning techniques that uses low-dimensional sparse modeling to predict the PPB value of cyclic peptides computationally. The low-dimensional sparse model not only exhibits excellent generalization performance but also improves interpretation of the prediction model. This can provide common an noteworthy knowledge for future cyclic peptide drug discovery studies.

SUBMITTER: Tajimi T

PROVIDER: S-EPMC6311893 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

Tajimi Takashi T Wakui Naoki N Yanagisawa Keisuke K Yoshikawa Yasushi Y Ohue Masahito M Akiyama Yutaka Y

BMC bioinformatics 20181231 Suppl 19

<h4>Background</h4>Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug c ...[more]

PMID: 30598072

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Project description:Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR]5, a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR]x (x = 6-9) and hybrid cyclic-linear peptides, [R6K]W6 and [R5K]W7, containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR]9 was found to be the most potent tyrosine kinase inhibitor. [WR]9 showed higher inhibitory activity (IC50 = 0.21 μM) than [WR]5, [WR]6, [WR]7, and [WR]8 with IC50 values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ-33P]ATP. Consistent with the result above, [WR]9 inhibited other protein kinases such as Abl kinase activity with an IC50 value of 0.35 μM, showing 2.2-fold higher inhibition than [WR]5 (IC50 = 0.79 μM). [WR]9 also inhibited PKCa kinase activity with an IC50 value of 2.86 μM, approximately threefold higher inhibition than [WR]5 (IC50 = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR]9 exhibited IC50 values of <0.25 μM against Akt1, Alk, and Btk. These data suggest that [WR]9 is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [R6K]W6 and [R5K]W7 against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR](5-9). The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein-peptide complexes indicate that the presence of either [WR]5 or [WR]9 affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR]9.

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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

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