Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Molecular mechanism of Acanthopanax senticosus in the treatment of Alzheimer's disease based on network pharmacology and molecular docking

Alzheimer's disease is the most common neurodegenerative disease. Acanthopanax senticosus, also known as Ciwujia or Siberian ginseng in Chinese, has a wide range of antioxidant and anti-inflammatory activities. The study aims to explore the action mechanism of A. senticosus against Alzheimer's disease using network pharmacology and molecular docking. The active ingredients and targets of A. senticosus were searched through the ETCM database, and Alzheimer's disease-related targets were obtained through the OMIM and GeneCards databases. The Cytoscape 3.7.2 software was used to construct a "drug-component-target" relationship network, and the target genes of A. senticosus against Alzheimer's disease were imported into the String database to establish a protein interaction (PPI) network. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene enrichment analyses were performed through the Metascape database to obtain potential pathways of action of A. senticosus for the treatment of Alzheimer's disease, and the ability of these active ingredients to bind to core targets was then verified by molecular docking. 51 active ingredients were screened from A. senticosus, and 88 effective targets for Alzheimer's disease were screened. Topological and pathway-enrichment analyses revealed that A. senticosus could play a beneficial role in the treatment of Alzheimer's disease by regulating apoptosis and inflammation. Molecular docking results showed that Ciwujianoside B, Chiisanoside, and Ciwujianoside D1 had strong binding abilities to key target proteins (TNFα, IL1β, and CASP3). Collectively, A. senticosus is feasible in the treatment of Alzheimer's disease.

RestraintMaker: a graph-based approach to select distance restraints in free-energy calculations with dual topology

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed "single topology" or "dual topology". As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site [Formula: see text]-dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Approaching Pharmacological Space: Events and Components

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.