Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies.

Coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide as a severe pandemic and caused enormous global health and economical damage. Since December 2019, more than 197 million cases have been reported, causing 4.2 million deaths.

De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection.

The adaptive immune system recognizes tumor antigens at an early stage to eradicate cancer cells. This process is accompanied by systemic proliferation of the tumor antigen-specific T lymphocytes. While detection of asymptomatic early-stage cancers is challenging due to small tumor size and limited somatic alterations, tracking peripheral T cell repertoire changes may provide an attractive solution to cancer diagnosis.

[Scaffold hopping computational approach for searching novel β-lactamase inhibitors].

We present a novel computational ligand-based virtual screening approach with scaffold hopping capabilities for the identification of novel inhibitors of β-lactamases which confer bacterial resistance to β-lactam antibiotics. The structures of known β-lactamase inhibitors were used as query ligands, and a virtual in silico screening a database of 8 million drug-like compounds was performed in order to select the ligands with similar shape and charge distribution. A set of numerical descriptors was used such as chirality, eigen spectrum of matrices of interatomic distances and connectivity together with higher order moment invariants that showed their efficiency in the field of pattern recognition but have not yet been employed in drug discovery.

Estimation of the protein-ligand interaction energy for model building and validation.

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode.

Synthesis, SAR and molecular docking study of novel non-β-lactam inhibitors of TEM type β-lactamase.

The novel classes of acylated phenoxyanilide and thiourea compounds were investigated for their ability to inhibit TEM type β-lactamase enzyme. Two compounds 4g and 5c reveal the inhibition potency in micromolar range and show their action by non-covalent binding in the vicinity of the TEM-171 active site. The structure activity relationship around carbon chain length and different substituents in ortho- and para-positions of acylated phenoxyanilide as well as molecular modelling study has been performed.