Assessment of protein-ligand complexes in CASP15

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

New prediction categories in CASP15

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

Integrative X-ray Structure and Molecular Modeling for the Rationalization of Procaspase-8 Inhibitor Potency and Selectivity

Caspases are a critical class of proteases involved in regulating programmed cell death and other biological processes. Selective inhibitors of individual caspases, however, are lacking, due in large part to the high structural similarity found in the active sites of these enzymes. We recently discovered a small-molecule inhibitor, 63-R, that covalently binds the zymogen, or inactive precursor (pro-form), of caspase-8, but not other caspases, pointing to an untapped potential of procaspases as targets for chemical probes. Realizing this goal would benefit from a structural understanding of how small molecules bind to and inhibit caspase zymogens. There have, however, been very few reported procaspase structures. Here, we employ X-ray crystallography to elucidate a procaspase-8 crystal structure in complex with 63-R, which reveals large conformational changes in active-site loops that accommodate the intramolecular cleavage events required for protease activation. Combining these structural insights with molecular modeling and mutagenesis-based biochemical assays, we elucidate key interactions required for 63-R inhibition of procaspase-8. Our findings inform the mechanism of caspase activation and its disruption by small molecules and, more generally, have implications for the development of small molecule inhibitors and/or activators that target alternative (e.g., inactive precursor) protein states to ultimately expand the druggable proteome.

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU

In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.

Structural Basis of Altered Potency and Efficacy Displayed by a Major in Vivo Metabolite of the Antidiabetic PPARγ Drug Pioglitazone

Pioglitazone (Pio) is a Food and Drug Administration-approved drug for type-2 diabetes that binds and activates the nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ), yet it remains unclear how in vivo Pio metabolites affect PPARγ structure and function. Here, we present a structure-function comparison of Pio and its most abundant in vivo metabolite, 1-hydroxypioglitazone (PioOH). PioOH displayed a lower binding affinity and reduced potency in co-regulator recruitment assays. X-ray crystallography and molecular docking analysis of PioOH-bound PPARγ ligand-binding domain revealed an altered hydrogen bonding network, including the formation of water-mediated bonds, which could underlie its altered biochemical phenotype. NMR spectroscopy and hydrogen/deuterium exchange mass spectrometry analysis coupled to activity assays revealed that PioOH better stabilizes the PPARγ activation function-2 (AF-2) co-activator binding surface and better enhances co-activator binding, affording slightly better transcriptional efficacy. These results indicating that Pio hydroxylation affects its potency and efficacy as a PPARγ agonist contributes to our understanding of PPARγ-drug metabolite interactions.

Fatal propofol infusion syndrome in association with ketogenic diet

Propofol is used for the treatment of refractory status epilepticus. When given as a long-term infusion propofol may cause a rare but frequently fatal complication, the propofol infusion syndrome. The hallmarks are metabolic acidosis, lipemia, rhabdomyolysis and myocardial failure. Propofol infusion syndrome is caused by impaired fatty acid oxidation. Beside anticonvulsants the ketogenic diet, a high-fat, low-carbohydrate, adequate-protein diet, is an effective treatment for difficult-to-control seizures. We report a 10-year-old boy with catastrophic epilepsy, who developed fatal propofol infusion syndrome when a ketogenic diet was initiated. Substances like propofol which impair fatty acid oxidation may pose an increased risk if combined with ketogenic diet.

Transparency effects on powder speckle decorrelation

Laser speckle decorrelation has previously been applied to measure the mean powder size of bulk powder beds and the roughness of porous rocks. The angular decorrelation rate of laser speckle from bulk powder beds correlates strongly with the mean powder size. However it was found that the angular decorrelation rates of transparent powders were much higher than those of opaque or dyed powders of the same size distribution. The accuracy of size measurements could be severely compromised if transparency effects were not compensated. Monte Carlo modelling of remitted photon path lengths in a powder bed suggests and experimental data supports that diffuse reflectance data may be used to correct for transparency effects on angular decorrelation rate data.

Repeated immunotherapy using intratumoural injection with recombinant interleukin-2 and tumour-infiltrating lymphocytes inhibits growth of breast cancer and induces apoptosis of tumour cells

This study tested the effect of repeated intratumoural injection with recombinant interleukin-2 (rIL-2) and tumour-infiltrating lymphocytes (TILs) on inhibition of growth of breast cancer and on induction of apoptosis of tumour cells. The tumour cell line LDLX43 was used to induce breast cancer in Wistar rats. Group I (10 rats) was the control. Group II (12 rats) received repeated intratumoural injection with rIL-2 and TILs. rIL-2 at the dose of 5 x 10(5) IU/day was given for 7 days, and 1 x 10(7) TILs were injected on the second day of each rIL-2 therapy, for a treatment session. Overall, two treatment sessions of immunotherapy with rIL-2 and TILs were given in all treated animals. Rapid increased tumour volume was found in the control group. In the treated group the total response rate was 42%, of which 25% tumours showed partial regression and 17% tumours reached complete remission where infiltration of plenty of T lymphocytes was detected, indicating that T cell-mediated antitumour immunity is primarily responsible for tumour rejection. Further investigation showed the repeated immunotherapy using intratumoural injection with rIL-2 and TILs could induce the development of apoptosis of breast cancer cells.