IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.

The accurate prediction of peptide-major histocompatibility complex (MHC) class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC (pMHC) prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalize well to different datasets, which limits the practicality of these approaches. While existing methods rely on a 34 amino acid pseudo-sequence, our findings uncover the involvement of 147 positions in direct interactions between MHC and peptide.

Sustainable green synthesis of leaf extract-stabilized silver nanoparticles and their applications: colorimetric sensing of Sn and Hg and antifungal and antimicrobial properties.

(Hc) (commonly known as Gulbakawali) leaf extract was used for the stable and sustainable green synthesis of silver nanoparticles (Hc-AgNPs), which were biodegradable and non-toxic. leaf extract was used as a reducing agent to stabilize the Hc-AgNPs by converting Ag to Ag without adding any capping agent. It demonstrated stability for up to six months, and no agglomeration was observed.

Mechanisms of action of monoclonal antibodies in oncology integrated in IMGT/mAb-DB.

Background: Cancer cells activate different immune checkpoint (IC) pathways in order to evade immunosurveillance. Immunotherapies involving ICs either block or stimulate these pathways and enhance the efficiency of the immune system to recognize and attack cancer cells. In this way, the development of monoclonal antibodies (mAbs) targeting ICs has significant success in cancer treatment.