Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Dobchev, Dimitar A.; Mäger, Imre; Tulp, Indrek; Karelson, Gunnar; Tamm, Tarmo; Tämm, Kaido; Jänes, Jaak; Langel, Ülo; Karelson, Mati

doi:10.2174/157340910791202478

Cited by 52 publications

(45 citation statements)

References 12 publications

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“…HA solutions (6.25 mg/mL) in ultrapure water were degassed with bubbling N 2 (30 minutes) and aliquots were exposed to pulsed ultrasound for 120, 20 or 5 minutes (13 ml, 8.7 W/cm 2 , 1s on/1s off, 6–9 °C, Vibracell Model VCX500, 12.8 mm tip probe, Sonics and Materials, Inc. Newton, CT). After sonication, the solution was passed through a nylon syringe filter (pore size=0.45 µm) to yield low molecular weight HAs (~27 kDa, 59 kDa or 98 kDa, as determined by viscometry [49]), and the filtrate was freeze-dried as white foam (Figure 1 (a)). …”

Section: Methodsmentioning

confidence: 99%

Screening of hyaluronic acid–poly(ethylene glycol) composite hydrogels to support intervertebral disc cell biosynthesis using artificial neural network analysis

et al. 2014

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Hyaluronic acid (HA) poly(ethylene glycol) (PEG) composite hydrogels have been widely studied for both cell delivery and soft tissue regeneration applications. A very broad range of physical and biological properties have been engineered into HA-PEG hydrogels that may differentially affect cellular “outcomes” of survival, synthesis and metabolism. The objective of this study was to rapidly screen multiple HA-PEG composite hydrogel formulations for an effect on matrix synthesis and behaviors of nucleus pulposus (NP) and anulus fibrosus (AF) cells of the intervertebral disc (IVD). A secondary objective was to apply artificial neural network (ANN) analysis to identify relationships between HA-PEG composite hydrogel formulation parameters and biological outcome measures for each cell type of the IVD. Eight different hydrogels were developed from preparations of thiolated HA (HA-SH) and PEG vinylsulfone (PEG-VS) macromers, and used as substrates for NP and AF cell culture in vitro. Hydrogel mechanical properties ranged from 70-489 kPa depending on HA molecular weight, and measures of matrix synthesis, metabolite consumption and production, and cell morphology were obtained to study relationships to hydrogel parameters. Results showed that NP and AF cell numbers were highest upon the HA-PEG hydrogels formed from the lower molecular weight HA, with evidence of higher sGAG production also upon lower HA molecular weight composite gels. All cells formed more multi-cell clusters upon any HA-PEG composite hydrogel as compared to gelatin substrates. Formulations were clustered into neurons based largely on their HA molecular weight, with few effects of PEG molecular weight observed on any measured parameters.

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Section: Methodsmentioning

confidence: 99%

Screening of hyaluronic acid–poly(ethylene glycol) composite hydrogels to support intervertebral disc cell biosynthesis using artificial neural network analysis

et al. 2014

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“…The guanidino group on an arginine residue is especially valuable in binding nucleic acids, given that it can perform electrostatic, hydrogen bond, cation- π , and π - π interactions. Artificial neural networks and principal components analysis have been employed to study cell-penetrating peptides in an attempt to classify them according to their permeability [14]. Boltzmannian stochastics have also been used to calculate populations of 3D structures of CPPs using PepLook, calculating both intra- and intermolecular interactions [15].…”

Section: Introductionmentioning

confidence: 99%

Peptide-Ligand Binding Modeling of siRNA with Cell-Penetrating Peptides

García-Sosa

Tulp

Langel

et al. 2014

BioMed Research International

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The binding affinity of a series of cell-penetrating peptides (CPP) was modeled through docking and making use of the number of intermolecular hydrogen bonds, lipophilic contacts, and the number of sp3 molecular orbital hybridization carbons. The new ranking of the peptides is consistent with the experimentally determined efficiency in the downregulation of luciferase activity, which includes the peptides' ability to bind and deliver the siRNA into the cell. The predicted structures of the complexes of peptides to siRNA were stable throughout 10 ns long, explicit water molecular dynamics simulations. The stability and binding affinity of peptide-siRNA complexes was related to the sidechains and modifications of the CPPs, with the stearyl and quinoline groups improving affinity and stability. The reranking of the peptides docked to siRNA, together with explicit water molecular dynamics simulations, appears to be well suited to describe and predict the interaction of CPPs with siRNA.

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“…(3-5, 26-28) These previous predictors were mostly trained using physicochemical descriptors, with datasets obtained from non-standardized experiments and containing only canonical residues. (6,(29)(30)(31) Inclusion of chemically diverse unnatural moieties is challenging because such physicochemical descriptors may not be readily available. The ability to encode for unnatural residues, however, would greatly expand the chemical search space, and Peptide sequences are represented as row matrices comprised of residue fingerprints.…”

Section: Developing the Machine Learning Modelmentioning

confidence: 99%

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Schissel

Mohapatra

Wolfe

et al. 2020

Preprint

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There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with highthroughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequenceactivity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases.One sentence summary: Deep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus. typically occurs by energy-dependent uptake, meaning that endosomal escape presents an additional challenge. While some peptides can efficiently escape the endosome, designing a novel CPP sequence for this task is nearly impossible. In addition to the diversity of physicochemical properties of CPPs, variation in experimental design has resulted in inconsistent and sometimes contradictory datasets.(22) These inconsistencies preclude establishing sequence-activity relationships to guide the design of next-generation CPPs and can be remedied by testing PMO-CPP conjugates in a nuclear delivery-based assay that provides quantitative activity data and selects for sequences that can escape the endosome. In order to uncover CPP design principles for PMO delivery, it is necessary to have a standardized, biologically relevant dataset with which to train machine learning models.

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Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Cited by 52 publications

References 12 publications

Screening of hyaluronic acid–poly(ethylene glycol) composite hydrogels to support intervertebral disc cell biosynthesis using artificial neural network analysis

Screening of hyaluronic acid–poly(ethylene glycol) composite hydrogels to support intervertebral disc cell biosynthesis using artificial neural network analysis

Peptide-Ligand Binding Modeling of siRNA with Cell-Penetrating Peptides

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

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