The electronic density obtained from a QTAIM analysis used as molecular descriptor. A study performed in a new series of DHFR inhibitors

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Toward Universal Substituent Constants: Relating QTAIM Functional Group Descriptors to Substituent Effect Proxies

Substituents modulate reactions, but their effects are commonly described by using proxies to their functional group properties. Substituent descriptors from the quantum theory of atoms in molecules, which are true functional group properties, are related here to these proxies, which have historically had chemically relevant meaning. Due to the large number of descriptors, multivariate analysis is used to intuit their significance. Multiple linear regression, principal component, and partial least squares regression analyses highlight that these substituent descriptors contain similar information to the proxies while being intrinsic, predictable substituent properties. Sources of error limiting quantitative reproduction of the proxy data include transferability, experimental accuracy, and solvation issues.

Targeting Protein Pockets with Halogen Bonds: The Role of the Halogen Environment

Protein pockets that form a halogen bond (X-bond) with a halogenated ligand molecule simultaneously form other (mainly hydrophobic) interactions with the halogen atom that can be considered as its "X-bond environment" (XBenv). Most studies in the field have focused on the X-bond, with the properties of the XBenv usually overlooked. In this work, we derived a protocol that evaluates the XBenv strength as a measure of the propensity of a protein pocket to host an X-bond. The charge density-based topological descriptors in combination with machine learning tools were employed to predict formation and strength of the interactions that conform the XBenv as a function of their geometrical parameters. On the basis of these results, we propose that the XBenv can be used as a footprint to judge the chance of a protein pocket to form an X-bond.

Study of polyphenols from Caesalpinia paraguariensis as α-glucosidase inhibitors: kinetics and structure–activity relationship

Ellagic derivatives isolated from Caesalpinia paraguariensis bark: (1) ellagic acid, (2) 3-O-methylellagic, (3) 3,3′-O-dimethylellagic acid, and (4) 3,3′-O-dimethylellagic-4-O-β-d-xylopyranoside and their binding modes on α-glucosidase.

Combined MD/QTAIM techniques to evaluate ligand-receptor interactions. Scope and limitations

In medicinal chemistry, it is extremely important to evaluate, as accurately as possible, the molecular interactions involved in the formation of different ligand-receptor (L-R) complexes. Evaluating the different molecular interactions by quantum mechanics calculations is not a simple task, since formation of an L-R complex is a dynamic process. In this case, the use of combined techniques of molecular dynamics (MD) and quantum calculations is one the best possible approaches. In this work we report a comparative study using combined MD and QTAIM (Quantum Theory of Atoms In Molecules) calculations for five biological systems with different levels of structural complexity. We have studied Acetylcholinesterase (AChE), D2 Dopamine Receptor (D2DR), beta Secretase (BACE1), Dihydrofolate Reductase (DHFR) and Sphingosine Kinase 1 (SphK1). In these molecular targets, we have analyzed different ligands with diverse structural characteristics. The inhibitory activities of most of them have been previously measured in our laboratory. Our results indicate that QTAIM calculations can be extremely useful for in silico studies. It is possible to obtain very accurate information about the strength of the molecular interactions that stabilize the formation of the different L-R complexes. Better correlations can be obtained between theoretical and experimental data by using QTAIM calculations, allowing us to discriminate among ligands with similar affinities. QTAIM analysis gives fairly accurate information for weak interactions which are not well described by MD simulations. QTAIM study also allowed us to evaluate and determine which parts of the ligand need to be modified in order to increase its interactions with the molecular target. In this study we have discussed the importance of combined MD/QTAIM calculations for this type of simulations, showing their scopes and limitations.

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.

Combining Charge Density Analysis with Machine Learning Tools To Investigate the Cruzain Inhibition Mechanism

Trypanosoma cruzi, a flagellate protozoan parasite, is responsible for Chagas disease. The parasite major cysteine protease, cruzain (Cz), plays a vital role at every stage of its life cycle and the active-site region of the enzyme, similar to those of other members of the papain superfamily, is well characterized. Taking advantage of structural information available in public databases about Cz bound to known covalent inhibitors, along with their corresponding activity annotations, in this work, we performed a deep analysis of the molecular interactions at the Cz binding cleft, in order to investigate the enzyme inhibition mechanism. Our toolbox for performing this study consisted of the charge density topological analysis of the complexes to extract the molecular interactions and machine learning classification models to relate the interactions with biological activity. More precisely, such a combination was useful for the classification of molecular interactions as "active-like" or "inactive-like" according to whether they are prevalent in the most active or less active complexes, respectively. Further analysis of interactions with the help of unsupervised learning tools also allowed the understanding of how these interactions come into play together to trigger the enzyme into a particular conformational state. Most active inhibitors induce some conformational changes within the enzyme that lead to an overall better fit of the inhibitor into the binding cleft. Curiously, some of these conformational changes can be considered as a hallmark of the substrate recognition event, which means that most active inhibitors are likely recognized by the enzyme as if they were its own substrate so that the catalytic machinery is arranged as if it is about to break the substrate scissile bond. Overall, these results contribute to a better understanding of the enzyme inhibition mechanism. Moreover, the information about main interactions extracted through this work is already being used in our lab to guide docking solutions in ongoing prospective virtual screening campaigns to search for novel noncovalent cruzain inhibitors.