some terminology - PrestigeDevop/DNA-platform GitHub Wiki
some terms for computational life science ;
Molecular Docking
Molecular docking is a computational procedure that attempts to predict the binding orientation between two molecules: a receptor and a ligand
Prediction of the target-ligand binding mode and approximate binding energy using professional docking software
consensus = ensemble ( averaged estimation)
Prediction of the target-ligand binding mode and approximate binding energy using professional docking software
consensus = ensemble ( averaged estimation)
Target Modeling: Homology modeling and Ab initio modeling for building a model for a target whose structure is unknown.
scaffold hopping =discover structurally novel compounds starting from known active compounds
Molecular Dynamics (MD) Simulation Mainstream MD simulation software and software codes are utilized to gather information about the dynamic properties of targets and docked complexes
Virtual Screening
Virtual screening (VS) is a modern methodology that has been used in the identification of new bioactive substances. It is an in silico method that aims to identify small molecules contained in large databases of compounds with high potential for interaction with target proteins for subsequent biochemical analyses.
Hit series are screened out from the compound library based on molecular docking and affinity scoring. The screening process can be restricted by MD simulations and pharmacophore modeling.
Pharmacophore Modeling
Pharmacophore modeling based on ligands or receptors describes the molecular features necessary for interaction between targets and ligands
Quantitative Structure-Activity Relationship (QSAR) Modeling
CoMFA, CoMSIA, and other widely used multi-dimensional QSAR strategies to establish quantitative relationships between chemical structure and biological activity of compounds.
shape similarity
sometimes with the addition of pharmacophores ElectroShapes-SHAFTS ChemMapper, incorporates pharmacophore matching when calculating the volume similarity.
The similarity of the descriptors in both 2D and 3D methods can be measured by the Tanimoto coefficient The Tanimoto coefficient represents the ratio of the union to the intersection of the shapes of two molecules
Block distance (CBD, also called the Manhattan or Hamming distance), which represents the difference between the sum of two molecular shapes and twice the overlap of two molecular shapes, can also be used to calculate the molecular similarity Shape screening can be divided into two subclasses: indirect target prediction and direct target prediction. probabilities of interaction between the query structure and the annotated targets of the hit compounds
Pharmacophore Screening
Pharmacophore Screening search for similar effect state-of-the-art technology used to identify and extract the possible interactions between a ligand–receptor complex which can be defined as ligand-based, structure-based and complex-based pharmacophore modeling traditional ligand-based virtual screening; an example is the quantitative structure–activity relationship (QSAR )
Virtual Compounds and chemical space ;
To have a more thorough coverage of the chemical space, predicting chemical reaction products is a common way to enrich the search space. A traditional cheminformatics procedure to calculate theoretical reaction products has been using the SMIRKS reaction language [27, 28].
ADMET ; In the context of ADMET properties, quantum computing can be applied to various aspects of absorption, distribution, metabolism, excretion, and toxicity
System biology ; a branch of bioinformatic that study molecular biology in systematic way, modeling system to discover and debriefing(extracting ) knowledge graphs comparative emergent properties and model parameter optimization, P system and modeling of biological networks , tools include OpenCOR, OpenCOBRA, Virtual Cell and Systems Biology Markup Language
Reverse docking
Reverse docking method is able to predict the binding site and pose of a ligand to the receptor which are useful sources for lead optimization
Defining a proper set of target structures and their binding pockets is essential not only for the efficiency but for the accuracy of reverse docking.
Problematic aspects of reverse docking include high computational time, inter-protein noises of docking scores, and deficiency of available target structures.
In most cases, the active site of a protein is already known and can be determined from its co-crystallized small-molecule ligand. However, for some apo-form structures without co-crystallized ligands, the docking program must first recognize the active binding site of these proteins. If the apo-form structure is from a protein for which other co-crystallized structures are available, its active site can also be identified from those protein structures with co-crystallized ligands. Otherwise, de novo detection of the active site of the apo-form structure is required.
Active site recognition is very useful in attempts to dock a query molecule into cavities other than the binding pockets of known ligands,
Decoys are molecules that are presumed to be inactive against a target (i.e. will not likely bind to the target) and are used to validate the performance of molecular docking or a virtual screening workflow
Activity cliffs are molecules with small differences in structure but large differences in potency
chaperones are proteins that assist the conformational folding or unfolding of large proteins or macromolecular protein complexes
Target Fishing aka reverse screening ;
target fishing is also known as reverse screening. This computational method is used to identify potential protein targets of small molecules, such as drugs or natural compounds