Pattern recognition strategies for molecular surfaces. II. Surface complementarity

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

MOLECULAR FACE — A NOVEL MOLECULAR MODEL SHOWING BOTH MOLECULAR SPATIAL CONTOUR AND FRONTIER ELECTRON DENSITY

The spatial knowledge is the first one of all information about an object. Molecular shape and size, molecular van der Waals surface and/or solvent-accessible surface etc. have been widely studied and applied. This paper is to show that a molecular face (MF) for a molecule may be defined uniquely and intrinsically via the molecular intrinsic characteristic contours (MICC) with coding the molecular electron density (ED) as the fourth dimension. The significant feature of an MF provides both molecular spatial appearance and its frontier electron density, being an intuitive picture as a molecular fingerprint or face. With simple examples, the physical significance of an MF is then demonstrated.

Investigation of the molecular surface area and volume: Defined and calculated by the molecular face theory

Based on the molecular face (MF) theory, the molecular face surface area (MFSA) and molecular face volume (MFV) are defined. For a variety of organic molecules and several inorganic molecules, the MFSA and MFV have been studied and calculated in terms of an algorithm of our own via the Matlab package. The MFV shows a very good linear relationship with the experimentally measured critical molar volume. It is also found that the MFSA and MFV have significant linear correlations with those of the commonly used hard-sphere model and the electron density isosurface.

Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.

Organization of rhodopsin molecules in native membranes of rod cells–an old theoretical model compared to new experimental data

It has been shown that rhodopsin forms an oligomer in the shape of long double rows of monomers. Because of the importance of rhodopsin as a template for all G protein-coupled receptors, its dimeric, tetrameric and higher-oligomeric structures also provide a useful pattern for similar structures in GPCRs. New experimental data published recently are discussed in the context of a proposed model of the rhodopsin oligomer 1N3M deposited in the protein data bank. The new rhodopsin structure at 2.2 A resolution with all residues resolved as well as an electron cryomicroscopy structure from 2D crystals of rhodopsin are in agreement with the 1N3M model. Accommodation of movement of transmembrane helix VI, regarded as a major event during the activation of rhodopsin, in a steady structure of the oligomer is also discussed. [Figure: see text]. Superimposition of the 1U19 (red wire), 1GZM (purple wire) and 1N3M (blue wire) rhodopsin structures. Size of the wires is proportional to thermal factors of backbone C(alpha) atoms, view parallel to the membrane.

Method and algorithm of obtaining the molecular intrinsic characteristic contours (MICCs) of organic molecules

The molecular intrinsic characteristic contour (MICC) is defined as the set of all the classical turning points of electron movement in a molecule. Studies on the MICCs of some medium organic molecules, such as dimethylether, acetone, and some homologues of alkanes, alkenes, and alkynes, as well as the electron density distributions on the MICCs, are shown for the first time. Results show that the MICC is an intrinsic approach to shape and size of a molecule. Unlike the van der Waals hard-sphere model, the MICC is a smooth contour, and it has a clear physical meaning. Detailed investigations on the cross-sections of MICCs have provided a kind of important information about atomic size changing in the process of forming molecules. Studies on electron density distribution on the MICC not only provide a new insight into molecular shape, but also show that the electron density distribution on the boundary surface relates closely with molecular properties and reactivities. For the homologues of alkanes, Rout(H), Dmin, and Dmax (the minimum and maximum of electron density on the MICC), all have very good linear relationships with minus of the molecular ionization potential. This work may serve as a basis for exploring a new reactivity indicator of chemical reactions and for studying molecular shape properties of large organic and biological molecules.

Pattern recognition strategies for molecular surfaces: III. Binding site prediction with a neural network

An algorithm for the identification of possible binding sites of biomolecules, which are represented as regions of the molecular surface, is introduced. The algorithm is based on the segmentation of the molecular surface into overlapping patches as described in the first article of this series.1 The properties of these patches (calculated on the basis of physical and chemical properties) are used for the analysis of the molecular surfaces of 7821 proteins and protein complexes. Special attention is drawn to known protein binding sites. A binding site identification algorithm is realized on the basis of the calculated data using a neural network strategy. The neural network is able to classify surface patches as protein-protein, protein-DNA, protein-ligand, or nonbinding sites. To show the capability of the algorithm, results of the surface analysis and the predictions are presented and discussed with representative examples.

New fuzzy logic strategies for bio-molecular recognition

The concepts of molecular similarity and molecular complementarity, playing important roles in the broad field of molecular recognition, are chemical problems, in which the eyeball technique used by a human observer is very successful but which are very hard to code into a computer algorithm. Based on the model of molecular surfaces, our new approach defines overlapping surface patches with similar molecular properties. These patches are used to represent local features of the molecule in a way, which is beyond the atomistic resolution but can nevertheless be applied in partial similarity as well as complementarity analyses in a very general sense. It is shown that this molecular description can be used as the first step in a docking algorithm for complexes, where the structures of both molecules are known, as well as for the identification of possible active sites without the knowledge of specific molecules binding to this site.

Identification of protein biochemical functions by similarity search using the molecular surface database eF-site

The identification of protein biochemical functions based on their three-dimensional structures is strongly required in the post-genome-sequencing era. We have developed a new method to identify and predict protein biochemical functions using the similarity information of molecular surface geometries and electrostatic potentials on the surfaces. Our prediction system consists of a similarity search method based on a clique search algorithm and the molecular surface database eF-site (electrostatic surface of functional-site in proteins). Using this system, functional sites similar to those of phosphoenoylpyruvate carboxy kinase were detected in several mononucleotide-binding proteins, which have different folds. We also applied our method to a hypothetical protein, MJ0226 from Methanococcus jannaschii, and detected the mononucleotide binding site from the similarity to other proteins having different folds.

Pattern recognition strategies for molecular surfaces. I. Pattern generation using fuzzy set theory

A new method for the characterization of molecules based on the model approach of molecular surfaces is presented. We use the topographical properties of the surface as well as the electrostatic potential, the local lipophilicity/hydrophilicity, and the hydrogen bond density on the surface for characterization. The definition and the calculation method for these properties are reviewed shortly. The surface is segmented into overlapping patches with similar molecular properties. These patches can be used to represent the characteristic local features of the molecule in a way that is beyond the atomistic resolution but can nevertheless be applied for the analysis of partial similarities of different molecules as well as for the identification of molecular complementarity in a very general sense. The patch representation can be used for different applications, which will be demonstrated in subsequent articles.