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Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

ABSTRACT: Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .

SUBMITTER: Oeller M

PROVIDER: S-EPMC10656490 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

Oeller Marc M Kang Ryan J D RJD Bolt Hannah L HL Gomes Dos Santos Ana L AL Weinmann Annika Langborg AL Nikitidis Antonios A Zlatoidsky Pavol P Su Wu W Czechtizky Werngard W De Maria Leonardo L Sormanni Pietro P Vendruscolo Michele M

Nature communications 20231117 1

Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate ...[more]

PMID: 37978172

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Project description:BackgroundIn the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.ResultsPEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81-4.05 Å.ConclusionsIn summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.

Project description:A series of cyclic peptides, [(DipR)(WR)4], [(DipR)2(WR)3], [(DipR)3(WR)2], [(DipR)4(WR)], and [DipR]5, and their linear counterparts containing arginine (R) as positively charged residues and tryptophan (W) or diphenylalanine (Dip) as hydrophobic residues, were synthesized and evaluated for their molecular transporter efficiency. The in vitro cytotoxicity of the synthesized peptides was determined in human epithelial ovary adenocarcinoma cells (SK-OV-3), human lymphoblast peripheral blood cells (CCRF-CEM), human embryonic epithelial kidney healthy cells (HEK-293), human epithelial mammary gland adenocarcinoma cells (MDA-MB-468), pig epithelial kidney normal cells (LLC-PK1), and human epithelial fibroblast uterine sarcoma cells (MES-SA). A concentration of 5-10 µM and 3 h incubation were selected in uptake studies. The cellular uptake of a fluorescent-labeled phosphopeptide, stavudine, lamivudine, emtricitabine, and siRNA was determined in the presence of peptides via flow cytometry. Among the peptides, [DipR]5 (10 µM) was found to be the most efficient transporter and significantly improved the uptake of F'-GpYEEI, i.e., by approximately 130-fold after 3 h incubation in CCRF-CEM cells. Confocal microscopy further confirmed the improved delivery of fluorescent-labeled [DipR]5 (F'-[K(DipR)5]) alone and F'-GpYEEI in the presence of [DipR]5 in MDA-MB-231 cells. The uptake of fluorescent-labeled siRNA (F'-siRNA) in the presence of [DipR]5 with N/P ratios of 10 and 20 was found to be 30- and 50-fold higher, respectively, compared with the cells exposed to F'-siRNA alone. The presence of endocytosis inhibitors, i.e., nystatin, chlorpromazine, chloroquine, and methyl β-cyclodextrin, did not completely inhibit the cellular uptake of F'-[K(DipR)5] alone or F'-GpYEEI in the presence of [DipR]5, suggesting that a combination of mechanisms contributes to uptake. Circular dichroism was utilized to determine the secondary structure, while transmission electron microscopy was used to evaluate the particle sizes and morphology of the peptides. The data suggest the remarkable membrane transporter property of [DipR]5 for improving the delivery of various small molecules and cell-impermeable negatively charged molecules (e.g., siRNA and phosphopeptide).

Project description:Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi.

Dataset Information

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

Publications

Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids.

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