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The tumor suppressor p14ARF is widely deregulated in many types of cancers and is believed to function as a failsafe mechanism, inhibiting proliferation and inducing apoptosis as cellular response to a high oncogene load. We have found that a 22-amino-acid-long peptide derived from the N-terminal part of p14ARF, denoted ARF(1-22), which has previously been shown to mimic the function of p14ARF, has cell-penetrating properties. This peptide is internalized to the same extent as the cell-penetrating peptide (CPP) TP10 and dose-dependently decreases proliferation in MCF-7 and MDA MB 231 cells. Uptake of the ARF(1-22) peptide is associated with low membrane disturbance, measured by deoxyglucose and lactate dehydrogenase (LDH) leakage, as compared to its scrambled peptide. Also, flow cytometric analysis of annexin V/propidium iodide (PI) binding and Hoechst staining of nuclei suggest that ARF(1-22) induces apoptosis, whereas scrambled or inverted peptide sequences have no effect. The ARF(1-22) peptide mainly translocates cells through endocytosis, and is found intact inside cells for at least 3 hours. To our knowledge, this is the first time a CPP having pro-apoptopic activity has been designed from a protein.

An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA).

Based on a highly detailed materials characterisation database (including atomistic and multiscale modelling), single and univariate statistical methods, combined with machine learning techniques, revealed key descriptors of biological functions.