S100P belongs to the S100 family of calcium-binding proteins regulating diverse cellular processes. Certain S100 family members (S100A4 and S100B) are associated with cancer and used as biomarkers of metastatic phenotype. Also S100P is abnormally expressed in tumors and implicated in migration-invasion, survival, and response to therapy. Here we show that S100P binds the tumor suppressor protein p53 as well as its negative regulator HDM2, and that this interaction perturbs the p53-HDM2 binding and increases the p53 level. Paradoxically, the S100P-induced p53 is unable to activate its transcriptional targets hdm2, p21WAF, and bax following the DNA damage. This appears to be related to reduced phosphorylation of serine residues in both N-terminal and C-terminal regions of the p53 molecule. Furthermore, the S100P expression results in lower levels of pro-apoptotic proteins, in reduced cell death response to cytotoxic treatments, followed by stimulation of therapy-induced senescence and increased clonogenic survival. Conversely, the S100P silencing suppresses the ability of cancer cells to survive the DNA damage and form colonies. Thus, we propose that the oncogenic role of S100P involves binding and inactivation of p53, which leads to aberrant DNA damage responses linked with senescence and escape to proliferation. Thereby, the S100P protein may contribute to the outgrowth of aggressive tumor cells resistant to cytotoxic therapy and promote cancer progression.

As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small. This necessitates novel concepts for quick and accurate data selection. For this purpose, a set of machine learning algorithms for diffraction data classification has been implemented and tested. The proposed pre-processing and analysis workflow efficiently distinguished between amorphous ice and carbon support, providing proof of the principle of machine learning based identification of positions of interest. While limited in its current context, this approach exploits inherent characteristics of narrow electron beam diffraction patterns and can be extended for protein data classification and feature extraction.