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CO2 thickeners have the potential to be a game changer for enhanced oil recovery, carbon capture utilization and storage, and hydraulic fracturing. Thickener design is challenging due to polymers’ low solubility in supercritical CO2 (scCO2) and the difficulty of substantially increasing the viscosity of CO2. In this contribution, we present a framework to design CO2 soluble thickeners, combining calculations using a quantum mechanical solvation model with direct laboratory viscosity testing. The conductor-like polarizable continuum model for solvation free-energy calculations was used to determine functional silicone and silsesquioxane solubilities in scCO2. This method allowed for a fast and efficient identification of CO2-soluble compounds, revealing silsesquioxanes as more CO2-philic than linear polydimethylsiloxane (PDMS), the most efficient non-fluorinated thickener know to date. The rolling ball apparatus was used to measure the viscosity of scCO2 with both PDMS and silicone resins with added silica nanoparticles. Methyl silicone resins were found to be stable and fast to disperse in scCO2 while having a significant thickening effect. They have a larger effect on the solution viscosity than higher-molecular-weight PDMS and are able to thicken CO2 even at high temperatures. Silicone resins are thus shown to be promising scCO2 thickeners, exhibiting enhanced solubility and good rheological properties, while also having a moderate cost and being easily commercially attainable.

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. In molecular docking, whenever experimental structures were not available, in silico structural homology modeling has been the method of choice. However, using computationally predicted structures adds a further degree of uncertainty to the docking process. Recently, AlphaFold (AF), an artificial intelligence-based modeling tool, has shown impressive results in terms of model accuracy within the field of ab initio protein structure prediction. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the performance of AF models in high-throughput docking (HTD) to their corresponding experimental PDB structures using a benchmark set of 16 targets spanning different protein families and binding site properties. Four docking programs and two consensus techniques were used to evaluate the HTD performance. The AF models showed consistently worse performance than their corresponding PDB structures, with zero enrichment factor values in several cases. While AlphaFold shows a remarkable ability to predict protein architecture and binding site anatomy, we conclude that this is not enough to guarantee that AF models can be reliably used for HTD purposes. Moreover, we show that very small variations at the side chain level of essential ligand-binding residues have a large impact on the outcome of HTD, what suggests that post-modeling refinement strategies might be key to increase the chance of success of AF models in prospective HTD campaigns.

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of targets sampling different protein families with a variety of binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

To build the foundation for accurate quantum mechanical (QM) simulation of biomacromolecules in an aqueous environment, we undertook the optimization of the COnductor-like Screening MOdel (COSMO) atomic radii and atomic surface tension coefficients for different semiempirical Hamiltonians adhering to the same computational conditions recently followed in the simulation of biomolecular systems. This optimization was achieved by reproducing experimental hydration free energies of a set consisting of 507 neutral and 99 ionic molecules. The calculated hydration free energies were significantly improved by introducing a multiple atomic-type scheme that reflects different chemical environments. The nonpolar contribution was treated according to the scaled particle Claverie-Pierotti formalism. Separate radii and surface tension coefficient sets have been developed for AM1, PM3, PM5, and RM1 semiempirical Hamiltonians, with an average unsigned error for neutral molecules of 0.64, 0.66, 0.73, and 0.71 kcal/mol, respectively. Free energy calculation of each molecule took on average 0.5 s on a single processor. The new sets of parameters will enhance the quality of semiempirical QM calculations using COSMO in biomolecular systems. Overall, these results further extend the utility of QM methods to chemical and biological systems in the condensed phase.

Melanin-concentrating hormone receptor 1 (MCH-R1) is a G-protein-coupled receptor (GPCR) and a target for the development of therapeutics for obesity. The structure-based development of MCH-R1 and other GPCR antagonists is hampered by the lack of an available experimentally determined atomic structure. A ligand-steered homology modeling approach has been developed (where information about existing ligands is used explicitly to shape and optimize the binding site) followed by docking-based virtual screening. Top scoring compounds identified virtually were tested experimentally in an MCH-R1 competitive binding assay, and six novel chemotypes as low micromolar affinity antagonist "hits" were identified. This success rate is more than a 10-fold improvement over random high-throughput screening, which supports our ligand-steered method. Clearly, the ligand-steered homology modeling method reduces the uncertainty of structure modeling for difficult targets like GPCRs.

With the established chemistry of bridged [(porphyrinate)FeIII-X-CuII(ligand)]n+ [X = O2- (oxo), OH- (hydroxo), O22- (peroxo)] complexes, we investigated the effect of cobalt ion substitution for copper or copper and iron. Thus, in this report, the generation and characterization of new mu-oxo, micro-hydroxo, and micro-peroxo (micro-X) assemblies of [(porphyrinate)MIII-X-CoII/III(TMPA)]n+ assemblies is described, where M = FeIII or CoIII and TMPA = tris(2-pyridylmethyl)amine. The mu-oxo complex [(F8TPP)FeIII-O-CoII(TMPA)]+ (1, F8TPP = tetrakis(2,6-difluorphenyl)porphyrinate) was isolated by an acid-base self-assembly reaction of a 1:1 mixture of (F8TPP)FeIII-OH and [CoII(TMPA)(MeCN)]2+ upon addition of triethylamine. The crystal structure of 1.2C4H10O proved the presence of an unsupported Fe-O-Co moiety; angleFe-O-Co = 171.6 degrees and d(Fe...Co) = 3.58 A. Complex 1 was further characterized by UV-vis (lambdamax = 437 (Soret) and 557 nm), 1H NMR [delta 40.6 (pyrrole-H), 8.8 and 8.7 (m-phenyl-H), 8.0 (p-phenyl-H), 4.4 (PY-4H), 2.6 (PY-3H), 1.0 (PY-5H), -1.1 (PY-6H), and -2.7 (TMPA-CH2-) ppm], electrospray ionization (ESI) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometric methods, Evans method NMR (microeff = 3.1), and superconducting quantum interference device (SQUID) susceptometry (J = -114 cm-1, S = 1). The micro-hydroxo analogue [(F8TPP)FeIII-(OH)-CoII(TMPA)]+ (2) [UV-vis lambdamax = 567 nm; delta 78 ppm (pyrrole-H); Evans NMR microeff = 3.7] was generated by addition of 1 equiv of triflic acid to 1. The protonation is completely reversible, and 1 is regenerated from 2 by addition of triethylamine. While (F8TPP)FeII/[CoII(TMPA)(MeCN)]2+/O2 chemistry does not lead to a stable micro-peroxo species, a dicobalt micro-peroxo complex [(TPP)CoIII-(O22-)-CoIII(TMPA)]2+ (3, TPP = meso-tetraphenylporphyrinate) forms from a reaction of O2 with a 1:1 mixture of the CoII precursor components at -80 degrees C [UV-vis lambdamax = 435 (Soret), 548, and 583 (weak) nm; silent EPR spectrum; diamagnetic NMR spectrum]. The oxygenation/deoxygenation equilibrium is reversible; warming solutions of 3 releases approximately 1 equiv of O2 and the reduced complexes are reformed.

Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.

van der Waals force field parameters are difficult to determine from only experimental data because of the insufficient data-to-parameter ratio, particularly for the diverse set of atom types necessary for force fields used in virtual ligand screening. We present a method which exploits the virtually unlimited number of ab initio calculations, as compared with experimental data, and does not have to rely on unphysical combination rules used by most force fields. Interaction energies of all pairs of molecules from a set of eight compounds at the MP2/d-aug-cc-pVDZ level with a counterpoise correction are used to simultaneously fit parameters in a buffered Lennard-Jones van der Waals potential for six atom types. The transferability of the parameters is demonstrated by the reproducibility of ab initio dimer energies for four molecules in a validation set. Next, sublimation energies of 14 alkanes and 11 nonalkane compounds are calculated using both our potential and the van der Waals potential from the MMFF94...

By combining pseudorandom bead-based aptamer libraries with conjugation chemistry, we have created next-generation aptamers, X-aptamers (XAs). Several X-ligands can be added in a directed or random fashion to the aptamers to further enhance their binding affinities for the target proteins. Here we describe the addition of a drug (N-acetyl-2,3-dehydro-2-deoxyneuraminic acid), demonstrated to bind to CD44-HABD, to a complete monothioate backbone-substituted aptamer to increase its binding affinity for the target protein by up to 23-fold, while increasing the drug's level of binding 1-million fold. Fil: He, Weiguo. University of Texas Health Science Center at Houston; Estados Unidos Fil: Elizondo Riojas, Miguel Angel. University of Texas Health Science Center at Houston; Estados Unidos. Universidad Autónoma de Nuevo León. Hospital Universitario “Dr. José Eleuterio González”; México Fil: Li, Xin. University of Texas Health Science Center at Houston; Estados Unidos Fil: Lokesh, Ganesh Lakshmana Rao. University of Texas Health Science Center at Houston; Estados Unidos Fil: Somasunderam, Anoma. University of Texas Health Science Center at Houston; Estados Unidos Fil: Thiviyanathan, Varatharasa. University of Texas Health Science Center at Houston; Estados Unidos Fil: Volk, David E.. University of Texas Health Science Center at Houston; Estados Unidos Fil: Durland, Ross H.. AM Biotechnologies; Estados Unidos Fil: Englehardt, Johnnie. AM Biotechnologies; Estados Unidos Fil: Cavasotto, Claudio Norberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina Fil: Gorenstein, David G.. University of Texas Health Science Center at Houston; Estados Unidos