Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

In silico study revealed the inhibitory activity of selected phytomolecules of C. rotundus against VacA implicated in gastric ulcer

Gastric ulcer is associated with weakening of the mucous coating of the stomach and damages to the intestinal lining. It is caused by H. pylori assisted by enzymes including VacA, which necessitates the need for inhibitors of VacA. Bioactive compounds from Cyperus rotundus have been documented to have anti-inflammatory activities. However, the mechanism of action of the phytochemicals is not characterized. This research aimed to assess, in silico, the potential of selected bioactive compounds against VacA based on the binding to its active sites. VacA and bioactive compounds structures were obtained from protein database and PubChem webserver, respectively. All compounds, including 2 controls, omeprazole and cimetidine were docked against the protein using AutoDock Vina and screened based on the binding energy. The selected complexes were subjected to pharmacokinetics and toxicity screening. Finally, molecular dynamics simulation and MMPBSA were carried out on two best compounds. 17 compounds interacted with the active site of VacA with higher binding affinities, with 7 of them - aureusidine, catechin, chlorogenic acid, isorhamnetin, isovitexin, oreintin, and vitexin having the best behaviours based on ADMET and druglikeness screening. Molecular dynamics and MMPBSA experiments of two of the hits corroborated good stability and binding energy for Ellagic Acid and Scirpusin B (ΔG = -14.38 and -13.20 kcal mol-1, respectively). These phytochemicals showed good pharmacokinetic profiles with respect to the control drugs. This study revealed that the identified compounds of C. rotundus may serve as VacA inhibitors and may be potent candidates for novel drug formulations in gastric ulcer treatment.Communicated by Ramaswamy H. Sarma.

SILVR: Guided Diffusion for Molecule Generation

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 μM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

NMR Data-Driven Docking of HDM2-Inhibitor Complexes

We present an automated NMR-guided docking workflow that can be used to generate models of protein-ligand complexes based on data from NOE NMR experiments. The first step is to generate a number of intermolecular distance constraints from experimental NOE data. Then, the ligand is docked on an ensemble of receptor structures to account for protein flexibility, and multiple poses are generated. Finally, we use the NOE-based constraints to filter and score docking poses based on the percentage of NOE constraints that are consistent with protein-ligand interatomic distances. This workflow was successfully used during a Lead Optimization project to generate models of synthetic PPI inhibitors bound to the HDM2 protein.

Icotinib, Almonertinib, and Olmutinib: A 2D Similarity/Docking-Based Study to Predict the Potential Binding Modes and Interactions into EGFR

In the current study, a 2D similarity/docking-based study was used to predict the potential binding modes of icotinib, almonertinib, and olmutinib into EGFR. The similarity search of icotinib, almonertinib, and olmutinib against a database of 154 EGFR ligands revealed the highest similarity scores with erlotinib (0.9333), osimertinib (0.9487), and WZ4003 (0.8421), respectively. In addition, the results of the docking study of the three drugs into EGFR revealed high binding free energies (ΔG_b = −6.32 to −8.42 kcal/mol) compared to the co-crystallized ligands (ΔG_b = −7.03 to −8.07 kcal/mol). Analysis of the top-scoring poses of the three drugs was done to identify their potential binding modes. The distances between Cys797 in EGFR and the Michael acceptor sites in almonertinib and olmutinib were determined. In conclusion, the results could provide insights into the potential binding characteristics of the three drugs into EGFR which could help in the design of new more potent analogs.

Phytochemical discrimination of Pinus species based on GC-MS and ATR-IR analyses and their impact on Helicobacter pylori

INTRODUCTION

The leaves and cones of Pinus plants as well as their essential oils have been used in traditional medicine for the treatment of several ailments.

OBJECTIVES

Phytochemical discrimination of Pinus species and investigation of their anti-Helicobacter pylori activity in vitro and in silico.

MATERIALS AND METHODS

Gas chromatography-mass spectrometry (GC-MS) and attenuated total reflectance infrared (ATR-IR) metabolic profiling of the essential oils of Pinus species. Principal component analysis (PCA) and hierarchal cluster analysis (HCA) were applied for discrimination and segregation of Pinus species.

RESULTS

GC-MS revealed the presence of 76 constituents, where monoterpenes represented the major class with the dominance of α-pinene (72%) followed by β-pinene (16%) for P. canariensis. β-Pinene was the dominant component in P. pinea (24%) followed by terpinolene (11%). α-Pinene (17%) and caryophyllene (12%) were the major components in P. halepensis, while, 3-carene (33%) and α-pinene (17%) represented the major constituents of P. roxburghii oil. By applying PCA and HCA on GC-MS and ATR-IR data analysis, ATR-IR displayed much better discrimination for Pinus species. The pine oils showed promising inhibitory effects on Helicobacter pylori. Furthermore, in silico molecular modelling was carried out where the calculated free binding energies of phytochemicals identified ranged from -33.71 to -19.67 kcal/mol for urease and -41.18 to -16.57 kcal/mol for shikimate kinase. This suggests favourable binding of pine essential oil components to both enzymes, thus explaining their potential inhibitory activity on H. pylori.

CONCLUSION

GC-MS and ATR-IR based metabolic analyses could discriminate between Pinus species. Pine essential oils can be used as promising therapeutic drugs to protect against H. pylori infection.

Benchmarking the Ability of Common Docking Programs to Correctly Reproduce and Score Binding Modes in SARS-CoV-2 Protease Mpro

The coronavirus SARS-CoV-2 main protease, Mpro, is conserved among coronaviruses with no human homolog and has therefore attracted significant attention as an enzyme drug target for COVID-19. The number of studies targeting Mpro for in silico screening has grown rapidly, and it would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. Clearly, current attempts at designing drugs targeting Mpro with the aid of computational docking would benefit from a priori knowledge of the ability of docking programs to predict correct binding modes and score these correctly. In the current work, we tested the ability of several leading docking programs, namely, Glide, DOCK, AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of Mpro ligands in 193 crystal structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of Mpro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction with virtual screening and docking.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

In Silico and In Vitro Anti-Helicobacter Pylori Effects of Combinations of Phytochemicals and Antibiotics

Helicobacter pylori infection is a WHO class 1 carcinogenic factor of gastric adenocarcinoma. In the past decades, many studies have demonstrated the increasing trend of antibiotic resistance and pointed out the necessity of new effective treatment. This study was aimed at identifying phytochemicals that can inhibit H. pylori and possibly serve as adjuvant treatments. Here, in silico molecular docking and drug-like properties analyses were performed to identify potential inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase. These three enzymes are targets of the treatment of H. pylori. Susceptibility and synergistic testing were performed on the selected phytochemicals and the positive control antibiotic, amoxicillin. The in-silico study revealed that oroxindin, rosmarinic acid and verbascoside are inhibitors of urease, shikimate kinase and aspartate-semialdehyde dehydrogenase, respectively, in which, oroxindin has the highest potency against H. pylori, indicated by a minimum inhibitory concentration (MIC) value of 50 μg/mL. A combination of oroxindin and amoxicillin demonstrated additive effects against H. pylori, as indicated by a fractional inhibitory concentration (FIC) value of 0.75. This study identified phytochemicals that deserve further investigation for the development of adjuvant therapeutic agents to current antibiotics against H. pylori.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Double-mutant cycles: new directions and applications

Double-mutant cycle (DMC) analysis is a powerful approach for detecting and quantifying the energetics of both direct and long-range interactions in proteins and other chemical systems. It can also be used to unravel higher-order interactions (e.g. three-body effects) that lead to cooperativity in protein folding and function. In this review, we describe new applications of DMC analysis based on advances in native mass spectrometry and high-throughput methods such as next generation sequencing and protein complementation assays. These developments have facilitated carrying out high-throughput DMC analysis, which can be used to characterize increasingly higher-order interactions and very large interaction networks in proteins. Such studies have provided insights into the extent of cooperativity (epistasis) in protein structures. High-throughput DMC studies have also been used to validate correlated mutation analysis and can provide restraints for protein docking.

RosettaLigandEnsemble: A Small-Molecule Ensemble-Driven Docking Approach

RosettaLigand is a protein-small-molecule (ligand) docking software capable of predicting binding poses and is used for virtual screening of medium-sized ligand libraries. Structurally similar small molecules are generally found to bind in the same pose to one binding pocket, despite some prominent exceptions. To make use of this information, we have developed RosettaLigandEnsemble (RLE). RLE docks a superimposed ensemble of congeneric ligands simultaneously. The program determines a well-scoring overall pose for this superimposed ensemble before independently optimizing individual protein-small-molecule interfaces. In a cross-docking benchmark of 89 protein-small-molecule co-crystal structures across 20 biological systems, we found that RLE improved sampling efficiency in 62 cases, with an average change of 18%. In addition, RLE generated more consistent docking results within a congeneric series and was capable of rescuing the unsuccessful docking of individual ligands, identifying a nativelike top-scoring model in 10 additional cases. The improvement in RLE is driven by a balance between having a sizable common chemical scaffold and meaningful modifications to distal groups. The new ensemble docking algorithm will work well in conjunction with medicinal chemistry structure-activity relationship (SAR) studies to more accurately recapitulate protein-ligand interfaces. We also tested whether optimizing the rank correlation of RLE-binding scores to SAR data in the refinement step helps the high-resolution positioning of the ligand. However, no significant improvement was observed.

Predicting Productive Binding Modes for Substrates and Carbocation Intermediates in Terpene Synthases-Bornyl Diphosphate Synthase as a Representative Case

Terpene synthases comprise a family of enzymes that convert acyclic oligo-isoprenyl diphosphates to terpene natural products with complex, polycyclic carbon backbones via the generation and protection of carbocation intermediates. To accommodate this chemistry, terpene synthase active sites generally are lined with alkyl and aromatic, i.e., nonpolar, sidechains. Predicting the correct, mechanistically relevant binding modes for entire terpene synthase reaction pathways remains an unsolved challenge. Here we describe a method for identifying such modes: TerDockin, a series of protocols to predict the orientation of carbon skeletons of substrates and derived carbocations relative to the bound diphosphate group in terpene synthase active sites. Using this recipe for bornyl diphosphate synthase, we have predicted binding modes that are consistent with all current experimental observations, including the results of isotope labeling experiments and known stereoselectivity. In addition, the predicted binding modes recapitulate key findings of a seminal study involving more computationally demanding QM/MM molecular dynamics methods on part of this pathway. This work illustrates the value of the TerDockin approach as a starting point for more involved calculations and sets the stage for the rational engineering of this family of enzymes.