Irene Maffucci

Tumor Necrosis Factor‐α (TNF‐α) is a cytokine that is normally produced by immune cells when fighting an infection. But, when too much TNF‐α is produced as in autoimmune diseases, this leads to unwanted and persistent inflammation. Anti‐TNF‐α monoclonal antibodies have revolutionized the therapy of these disorders by blocking TNF‐α and preventing its binding to TNF‐α receptors, thus suppressing the inflammation. Herein, we propose an alternative in the form of molecularly imprinted polymer nanogels (MIP‐NGs). MIP‐NGs are synthetic antibodies obtained by nanomoulding the 3‐dimensional shape and chemical functionalities of a desired target in a synthetic polymer. Using an in‐house developed in silico rational approach, epitope peptides of TNF‐α were generated and ‘synthetic peptide antibodies’ were prepared. The resultant MIP‐NGs bind the template peptide and recombinant TNF‐α with high affinity and selectivity, and can block the binding of TNF‐α to its receptor. Consequently they were applied to neutralize pro‐inflammatory TNF‐α in the supernatant of human THP‐1 macrophages, leading to a downregulation of the secretion of pro‐inflammatory cytokines. Our results suggest that MIP‐NGs, which are thermally and biochemically more stable and easier to manufacture than antibodies, and cost‐effective, are very promising as next generation TNF‐α inhibitors for the treatment of inflammatory diseases.

Motivation Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures. Results In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure. Availability and implementation The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git. Supplementary information Supplementary data are available at Bioinformatics online.