Discovery Studio™ (DS) is a professional life science molecular simulation software. The main functions of DS currently include: protein characterization (including protein-protein interactions), homology modeling, molecular mechanics calculations and molecular dynamics simulations, structure-based drug design tools (including ligand-protein interactions, novel drug design and molecular docking), small molecule-based drug design tools (including quantitative conformational relationships, pharmacophore, database screening, and ADMET), and combinatorial library design and analysis.
CD ComputaBio presents a series of tutorials on Discovery Studio software, including molecular docking, pharmacophore construction, ADMET property prediction, homology modeling, amino acid mutation, etc. We will continue to update them afterwards.
Pretreatment of small molecule ligands and protein receptors is essential, whether for molecular docking or pharmacophore construction, etc. This part of the tutorial focuses on the pretreatment of ligand-receptor systems and the definition of receptor binding sites.
Molecular docking is the process of placing a ligand molecule at the receptor active site and then evaluating the ligand-receptor interaction in real time according to the principles of geometric complementarity, energy complementarity, and chemical environment complementarity and finding the best binding mode between the two molecules. This part of the tutorial includes precise molecular docking, fully flexible receptor-ligand docking, and conformational analysis after molecular docking.
Pharmacophores provide a very useful tool to represent the nature of molecules involved in ligand-target receptor interactions as well as the location of functional groups, and all types of non-covalent interactions can be represented as geometric entities. In addition to the abstract characterization of known structures, pharmacophore modeling can help to design new molecules and predict their activity. This section covers the construction and validation of four types of pharmacophore models, such as: molecular common feature-based, receptor-based, receptor-ligand complex-based, and pharmacophore models with activity prediction capabilities.
In drug design, structure-based drug design methods (mainly molecular docking) will be helpless when the structure of the receptor is unknown. In contrast, the QSAR method is a small molecule ligand-based drug design method that aims to study and reveal the quantitative variation pattern between the activity of a compound and its molecular structure or physicochemical characteristics using mathematical and statistical methods. If bioactivity data can be collected for a series of structural analogues, the QSAR (quantitative conformational relationship) approach can be used to predict the relevant activity of unknown compounds. This part of the tutorial focuses on the construction of three-dimensional quantitative conformational relationship (3D-QSAR) models.
ADMET properties refer to the absorption, distribution, metabolism, excretion and toxicity of a molecule in an organism. ADMET descriptors can help to eliminate compounds with poor ADMET properties early to avoid costly structural modifications, and to evaluate the effect of structural optimization on improving ADMET properties to avoid excessive resources spent on synthesis. This part of the tutorial covers the prediction of ADMET properties of small molecule compounds as well as the prediction of toxicological properties.
The theoretical basis of homology modeling is that the conserved tertiary structure of proteins far exceeds the conserved primary sequence. Therefore, one can construct the spatial structure of an unknown structure protein (target) by using one or more proteins of known structure (template protein, template). This part of the tutorial covers methods for homology modeling as well as model evaluation.
Amino acid fixed-point mutations of proteins can be used in the design of enzymes and antibodies, but are inefficient due to the blindness in performing amino acid selection. Virtual amino acid mutagenesis can be used to determine the optimal combination of amino acid mutations by alanine scanning and saturation mutagenesis, thus providing guidance for amino acid targeted mutagenesis in experiments. This part of the tutorial focuses on the interaction force-based virtual amino acid mutagenesis of a protein-ligand complex.