Ligand specificity protein-ligand binding area forecasting method

Abstract

The invention provides a two-stage ligand specificity protein-ligand binding area forecasting method. The method includes the following steps of firstly, forecasting protein-ligand binding residues in a protein sequence through a ligand specificity forecasting model and based on inputted protein sequence information and extraction and combination of multi-view characteristics; secondly, conducting spatial clustering on the binding residues obtained in the first step, conducting the spatial clustering through a spatial clustering algorithm, and obtaining one or more binding areas. The ligand specificity protein-ligand binding area forecasting method has the advantages that forecasting accuracy can be effectively improved by using the modularized ligand specificity forecasting model, the binding areas can be further obtained from the forecasted binding residues by using the spatial clustering algorithm, and therefore the protein-ligand binding area forecasting can be really achieved.

Description

Technical field [0001] The present invention relates to the field of protein-ligand interaction prediction, in particular to a ligand-specific protein-ligand binding region prediction method. Background technique [0002] Proteins do not exist in isolation during life activities, they need to interact with other biomolecules to complete specific functions. This interaction is common and indispensable in life. Proteins strengthen or inhibit their biological functions by binding other biological molecules. The biological molecules bound by proteins are called ligands, and they can be metal ions, small organic / inorganic molecules, macromolecules (such as proteins), or nucleic acids. When interacting with the ligand, some key residues in the protein form a pocket-shaped area to complete the recognition and binding of the specific ligand. These key residues are called binding residues, and the pocket-shaped regions formed are also called binding regions. [0003] Identifying these ke...

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Problems solved by technology

[0008] However, a comprehensive analysis of the above prediction models shows that: on the one hand, the existing methods usually make predictions in a general sense without fully considering the differences between ligand types, thereby reducing the reliability of the prediction results; On the one hand, the vast majority of existing methods are just a binding state prediction, they judge each residue in the sequence as one of the two states of binding or non-binding, only binding residue prediction, and Not really a binding region prediction

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