Aptamer Database
Summary: The research group also develops and maintains the Aptamer Database, an open-access, curated platform designed for the storage, visualization, and exploration of DNA and RNA aptamer data. The database provides researchers with searchable aptamer sequences, secondary structures, minimum free energy (MFE) information, metadata, and downloadable predicted or experimental 3D structures (PDB files) to support research in molecular biology, computational chemistry, bioinformatics, and drug discovery.
Molhybrid
Summary: Molhybrid (Molecular hybridization) is a powerful computational strategy in drug discovery that generates novel compounds by combining pharmacophoric fragments derived from known bioactive molecules. By exploring existing chemical space and merging diverse structural features, this method creates a virtual library of hybrid molecules with potential biological activity.
DerivatizeMe
Summary: DerivatizeMe is primarily a means for very rapid decoration of a molecule that is in 3 dimensions with small substituents. The decoration involves positioning of incoming substituents and supports conformational searching and minimization for larger substituents. This decoration is driven by hydrogen atoms within the parent molecule.
Citation: Lester T. Sigauke, Özlem Taştan Bishop and Kevin A. Lobb, Introducing DerivatizeME and its Application in the Augmentation of a Natural Product Library, Journal of Computational Biophysics and Chemistry, 20 (03), pp. 233-250 (2021). DOI: 10.1142/S2737416521500101
AMADAR
Summary: AMADAR can identify the transition state of a Diels-Alder reaction, given the product, with a 95% success rate. It requires only an input SMILES string of the product, and generates 3D structures for reactants, products, and the transition state. It also refines the transition state at a user-defined high level of theory and performs IRC analysis for Diels-Alder processes.
Citation: Isamura, B.K., Lobb, K. AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis. J Cheminform 14, 39 (2022). , DOI: 10.1186/s13321-022-00618-3
T_SELEX
Summary: T_SELEX is a Python package designed for generating RNA aptamer libraries and predicting their secondary and tertiary structures, RNA-RNA interactions, and performing macromolecular docking. It is designed for Linux systems and offers a structured workflow for high-throughput RNA aptamer analysis.
Citation: Kabelo Phuti Mokgopa, Kevin Alan Lobb, Tendamdzimu Tshiwawa et al. T_SELEX program: Theoretical SELEX tool for Rational Design and Selection of RNA Aptamers Targeting Macromolecules,(2024) DOI:10.21203/rs.3.rs-5038044/v1