JP-7854675-B1 - Method for constructing the molecular structure of a compound

JP7854675B1JP 7854675 B1JP7854675 B1JP 7854675B1JP-7854675-B1

Abstract

[Problem] To provide a method that helps drug discovery professionals to generate and construct ideas for compound molecular structures that may bind to organic polymers (proteins, DNA/RNA, etc.) using a personal computer, without requiring large computing resources and computation time, complex operating procedures, or expertise in cutting-edge computer science or artificial intelligence. [Solution] Loose random packing is performed by dropping spheres of a specified radius onto the molecular surface of the cavity where the compound bonds in the three-dimensional structural model of the organic polymer. Next, the radius of the packed spheres is increased and the spheres that protrude from the molecular surface of the cavity are removed, and the remaining packed spheres are connected with a three-dimensional minimum spanning tree to construct the carbon skeleton structure of the compound, and then the ring structure is reconstructed. Carbon atoms at a distance that can interact with the main atoms of the organic polymer are converted to atoms and bonds suitable for interaction to construct the compound structure, and then strain is reduced, and the stabilized complex structure of the organic polymer and compound is calculated using a docking tool. [Selection Diagram] Figure 1

Inventors

岩瀬一彦

Assignees

岩瀬一彦

Dates

Publication Date: 20260507
Application Date: 20250912
Priority Date: 20250131

Claims (2)

A method for constructing a compound molecular structure on the surface of a cavity in an organic polymer to which a compound is bonded, by having a computer perform the following steps 1 through 5, The first step involves downloading a PDB file of the three-dimensional structural model of the target organic polymer from the Protein Databank, opening the downloaded PDB file using a molecular graphics tool to display the three-dimensional structural model of the organic polymer, specifying the recessed surface of the organic polymer to which the compound is bound and the organic polymer anchor, and outputting it as a VRML or X3D file. The second step involves opening the VRML or X3D file created in the first step using a 3D-CG toolkit, creating a mesh of collision shapes for the organic polymer depression surface and organic polymer anchors using rigidbody passive objects, arranging filling spheres with radii of 0.3 to 1.2 Å as rigidbody active objects with convex hulls as collision shapes on top of the organic polymer depressions, and then dropping and filling them. The top of the organic polymer depressions is then blocked with one to several plates whose collision shapes are meshed using rigidbody passive objects, and rigidbody simulations are performed one to several times with a ratio of convex hulls of the filling spheres' collision shapes ranging from 10% to 100%. As a result of the rigidbody simulations, the organic polymer depression surface, filling spheres, and organic polymer anchors are output as VRML or X3D files. The third step involves opening the VRML or X3D file created in the second step with a 3D-CG toolkit, slightly increasing the radius of the packed spheres to 0.4–1.5 Å to remove spheres that protrude from the surface of the organic polymer depressions, further increasing the radius of the packed spheres to 0.9–3.0 Å to remove spheres that protrude from the surface of the organic polymer depressions except near the organic polymer anchors, thereby narrowing down the packed spheres to be used for compound construction, outputting the narrowed-down packed spheres together with the organic polymer anchors as a VRML or X3D file, and then converting the packed spheres in the output file into a PDB file with the organic polymer anchors as carbon atoms. The fourth step involves reading only the carbon atoms that were packed spheres from the PDB file created in the third step using a three-dimensional minimal spanning tree tool, constructing a carbon graph by connecting the centers of the carbon atoms, and then forming a ring structure using a genetic algorithm that searches for a path from the coordinates of the carbon atoms selected from the constructed carbon graph, or by using reinforcement learning that selects bonds involved in the formation of a ring structure and earns a reward. After constructing the carbon skeleton structure by specifying the longest spanned side chain of the ring structure, the carbon skeleton structure and organic polymer anchors are output as a PDB file. Step 5 involves opening the PDB file created in Step 4 with a molecular graphics tool, converting carbon atoms that are within interacting distance of the organic polymer anchor into interacting atoms or bonds to construct the compound molecular structure, then reducing the strain, and finally calculating the stabilized organic polymer/compound complex structure with a docking tool. method.
A program that causes an information processing device to perform a process to construct a compound molecular structure on the surface of a depression in an organic polymer to which a compound is bound, by executing the following steps 1 through 5, The first step is to download a PDB file of the three-dimensional structural model of the target organic polymer from the Protein Databank, open the downloaded PDB file using a molecular graphics tool to display the three-dimensional structural model of the organic polymer, specify the recessed surface of the organic polymer to which the compound is bound and the organic polymer anchor, and output it as a VRML or X3D file. The second step involves opening the VRML or X3D file created in the first step using a 3D-CG toolkit, creating a mesh of collision shapes for the organic polymer depression surface and organic polymer anchors using rigidbody passive objects, arranging filling spheres with radii of 0.3 to 1.2 Å on top of the organic polymer depression using rigidbody active objects with convex hulls as collision shapes, and then dropping and filling them. The top of the organic polymer depression is then blocked with one to several plates with mesh collision shapes using rigidbody passive objects, and rigidbody simulations are performed one to several times with a ratio of convex hulls for the collision shapes of the filling spheres ranging from 10% to 100%. As a result of the rigidbody simulations, the organic polymer depression surface, filling spheres, and organic polymer anchors are output as VRML or X3D files. The third step involves opening the VRML or X3D file created in the second step using a 3D-CG toolkit, slightly increasing the radius of the packed spheres to 0.4–1.5 Å to remove spheres that protrude from the surface of the organic polymer depressions, further increasing the radius of the packed spheres to 0.9–3.0 Å to remove spheres that protrude from the surface of the organic polymer depressions except near the organic polymer anchors, thereby narrowing down the packed spheres to be used for compound construction, outputting the narrowed-down packed spheres together with the organic polymer anchors as a VRML or X3D file, and then converting the output file's packed spheres as carbon atoms together with the organic polymer anchors into a PDB file. The fourth step involves reading only the carbon atoms that were packed spheres from the PDB file created in the third step using a three-dimensional minimal spanning tree tool, constructing a carbon graph by connecting the centers of the carbon atoms, and then forming a ring structure using a genetic algorithm that searches for a path from the coordinates of the carbon atoms selected from the constructed carbon graph, or by using reinforcement learning that selects bonds involved in the formation of a ring structure to obtain a reward. After constructing the carbon skeleton structure by specifying the longest spanned side chain of the ring structure, the carbon skeleton structure and organic polymer anchors are output as a PDB file. The fifth step involves opening the PDB file created in the fourth step using a molecular graphics tool, converting carbon atoms that are within interacting distance of the organic polymer anchor into interacting atoms or bonds to construct the compound molecular structure, then reducing the strain, and finally calculating the stabilized organic polymer/compound complex structure using a docking tool. program.

Description

This invention relates to a method for constructing compound molecular structures that can be used in the molecular design of biologically active compounds. The invention also relates to a program for carrying out this method. It is said that developing a single new drug takes many years and a great deal of money. Even with such investment, the success rate of new drug development is extremely low. Until now, compounds have been collected extensively through synthesis, culture, and extraction, and high-throughput screening (HTS) has been performed to search for candidate compounds by creating libraries consisting of hundreds of thousands to over a million compounds. Alternatively, virtual screening (VS) has been performed by inputting the atomic coordinates of proteins and compounds into a computer, specifying the compound binding site on the protein, and narrowing down the compounds that may bind to that site to search for candidate compounds. However, HTS is expensive and has a low hit rate, and while VS has a higher hit rate than HTS, it also has challenges such as requiring enormous computation time to significantly improve accuracy. Therefore, VS has been used in compound library design to improve the hit rate of HTS, and supercomputers have been used to improve the accuracy of VS. Recently, in addition to VS using supercomputers, artificial intelligence technology that uses deep learning on vast amounts of data has also been utilized. In contrast to these large-scale compound design methods, there is a need for a method that allows drug discovery personnel to design compounds simply. Regarding compound design methods, Kuntz et al. generated spheres along the surface normal at each surface point across the entire surface of the protein three-dimensional structure, filling in depressions, and searched for compounds so that the distance between the sphere pairs and the compound atom pairs matched (Non-Patent Literature 1). Itai et al. also set dummy atoms at the positions of heteroatoms that could be hydrogen-bonding partners for hydrogen-bonding functional groups of protein amino acid residues located in depressions on the protein molecular surface, and searched for protein compound complex structures so that the distance between the dummy atom pairs and the hydrogen-bonding heteroatom pairs of the compound atoms matched (Patent Literature 1). However, these methods cannot generate compounds. Itai et al. proposed a method for designing the molecular structure of a compound by determining the type and position of each atom in the compound binding cavity of a three-dimensional protein structure model using random numbers and electrostatic potential to determine the dihedral angle, evaluating the interaction energy, and arranging the atoms accordingly (Non-Patent Literature 2). Fujitani et al. also proposed a method for designing the compound structure by evaluating the interaction energy and arranging substructures of a compound in the compound binding cavity of a three-dimensional protein structure model, and then chemically linking the substructures in a feasible manner (Non-Patent Literature 3). However, the former method has difficulty generating appropriate compounds by adding atoms one by one, and the energy calculation accuracy is insufficient. The latter method requires running numerous independent molecular dynamics simulations in a large-scale parallel environment to perform energy calculations with high accuracy. In recent years, a method has been proposed (Non-Patent Document 4) that uses a conditional variational autoencoder to learn the atomic density within the cavities where compounds bind to a three-dimensional protein structure model, thereby generating the molecular structure of a compound. However, this method, which utilizes artificial intelligence technology requiring large amounts of data and long learning times, is highly dependent on the quality and quantity of existing data, and is not easily implemented by drug discovery personnel who are not computer science experts. International Publication No. 93/20525 Kuntz, Irwin D. et al., A geometric approach to macromolecule-ligand interactions, Journal of Molecular Biology, vol.161, no.2, pp.269-288, 1982.Nishibata, Y. et al., Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation, Tetrahedron, vol.47, no.43, pp.8985-8990, 1991.Yamashita, T. et al., Molecular Dynamics Simulation-Based Evaluation of the Binding Free Energies of Computationally Designed Drug Candidates: Importance of the Dynamical Effects, Chemical and Pharmaceutical Bulletin, vol.62, no.7, pp.661-667, 2014.Ragoza, M. et al., Generating 3D molecules conditional on receptor binding sites with deep generative models, Chemical Science, vol.13, no.9, pp.2701-2713, 2022. This is a conceptual diagram of the method for constructing the molecular structure of a compound according to the present invention.This is