Synthesis, molecular docking, electrochemical and fluorimetric analysis of new caffeic and cinnamic acid-conjugated hemorphin derivatives designed as potential anticonvulsant and antinociceptive agents

Based on the pharmacophore model of opioid receptors, our team recently synthesized a series of short-chain hemorphin peptide analogs containing non-natural amino acids. They demonstrated anticonvulsant and antinociceptive activity with low neurotoxicity. In the present study, a series of novel bioconjugates of N-modified hemorphin analogs containing second pharmacophore cinnamic acids (CA) or caffeic (KA) were synthesized by a traditional solid-phase Fmoc chemistry method for peptide synthesis. Electrochemical and fluorimetric analysis, in vivo anticonvulsant and antinociceptive activity in mice were conducted on the compounds. The three CA acid- (H4-CA, H5-CA, and H7-CA) and three KA acid- (H4-KA, H5-KA, and H7-KA) conjugated hemorphin derivatives exhibited potency at the highest doses of 2 µg/5 µl, administered by intracerebroventricular (icv) mode, against seizure spread in the maximal electroshock test (MES) in mice. The KA-conjugated H5-KA derivate, at the lowest dose, was the only compound that suppressed clonic seizures in the subcutaneous pentylenetetrazol (scPTZ) test. Except for the H5-CA, all tested CA acid- and KA acid-conjugated peptide derivates had the potency to increase the latency for clonic seizures in a dose-dependent mode. The activity against the psychomotor seizures in the 6-Hz test was detected only for the H4-CA (0.5 µg) and H4-KA (0.5 µg and 1 µg), respectively. All investigated peptides showed a more pronounced antinociceptive effect in the "intraplantar formalin" test compared to the "hot plate" test. Shorter chain analogs showed a better antinociceptive profile against tonic pain. The data suggest a DOR and KOR-mediated mechanism of action. According to the docking analysis, H7-CA showed a different antinociceptive profile than other investigated peptides. The novel peptide derivates did not exhibit neurotoxicity in the rotarod test. Our findings suggest that conjugated CA and KA morphine peptides can be used to develop novel morphine-related analogs with anticonvulsant and antinociceptive activity.

Synthesis, characterization and evaluation of anti-hyperalgesia, anticonvulsant and antioxidant activity of novel VV-hemorphin-5 analogs

Two series of new VV-hemorphin-5 analogs with structures Val-Val-Tyr-Xxx-Trp-Thr-Gln-NH2 and Adam-Val-Val-Tyr-Xxx-Trp-Thr-Gln-NH2 , where Xxx is Ac5c (1-aminocyclopentane-1-carboxylic acid), Ac6c (1-aminocyclohexane-1-carboxylic acid), Ac7c (1-aminocycloheptane-1-carboxylic acid), and Adam is the low-molecular-weight lipophilic adamantyl building block, were synthesized, characterized electrochemically and evaluated for antioxidant, anti-hyperalgesia, and anticonvulsant activity. The design of the compounds followed the strategy to improve the propensity for aqueous solubility and/or to increase their affinity for the target receptor or enzyme. The partition coefficient value shows that the peptide scaffold goes from hydrophilic to lipophilic with the increasing size of the cycloalkane ring and even more with the introduction of the adamantane. The peptides C5-V and C7-V were the only analogs that provoked an immediate antinociceptive effect changing the mechanical pain threshold. The six new peptide analogs produced a significant and long-lasting carrageenan model of inflammatory pain in rats. While the adamantane hemorphin analog Ad7-V was the only compound with the potency to suppress psychomotor seizures in the 6-Hz test, the C6-V and Ad6-V exhibited protective activity against the seizure spread in the maximal electroshock seizure test in mice. The active analogs did not show neurotoxicity or sedative effects. Our results revealed a structure-related specific activity of a newly designed hemorphin analog that could be used as a template for future modification and preparation of compounds with potential analgesic and anticonvulsant activity.

Predicting pKa Values of Quinols and Related Aromatic Compounds with Multiple OH Groups

Quinonoid compounds play central roles as redox-active agents in photosynthesis and respiration and are also promising replacements for inorganic materials currently used in batteries. To design new quinonoid compounds and predict their state of protonation and redox behavior under various conditions, their pK_a values must be known. Methods that can predict the pK_a values of simple phenols cannot reliably handle complex analogues in which multiple OH groups are present and may form intramolecular hydrogen bonds. We have therefore developed a straightforward method based on a linear relationship between experimental pK_a values and calculated differences in energy between quinols and their deprotonated forms. Simple adjustments allow reliable predictions of pK_a values when intramolecular hydrogen bonds are present. Our approach has been validated by showing that predicted and experimental values for over 100 quinols and related compounds differ by an average of only 0.3 units. This accuracy makes it possible to select proper pK_a values when experimental data vary, predict the acidity of quinols and related compounds before they are made, and determine the sites and orders of deprotonation in complex structures with multiple OH groups.

SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model

A variety of fields would benefit from accurate [Formula: see text] predictions, especially drug design due to the effect a change in ionization state can have on a molecule's physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic [Formula: see text]s of 24 drug like small molecules. We recently built a general model for predicting [Formula: see text]s using a Gaussian process regression trained using physical and chemical features of each ionizable group. Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton. These features are fed into a Scikit-learn Gaussian process to predict microscopic [Formula: see text]s which are then used to analytically determine macroscopic [Formula: see text]s. Our Gaussian process is trained on a set of 2700 macroscopic [Formula: see text]s from monoprotic and select diprotic molecules. Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge. Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic. Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy. The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable.

An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge

In this work we have developed a hybrid QM and MM approach to predict pKa of small drug-like molecules in explicit solvent. The gas phase free energy of deprotonation is calculated using the M06-2X density functional theory level with Pople basis sets. The solvation free energy difference of the acid and its conjugate base is calculated at MD level using thermodynamic integration. We applied this method to the 24 drug-like molecules in the SAMPL6 blind pKa prediction challenge. We achieved an overall RMSE of 2.4 pKa units in our prediction. Our results show that further optimization of the protocol needs to be done before this method can be used as an alternative approach to the well established approaches of a full quantum level or empirical pKa prediction methods.

Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge

In this work, quantum mechanical methods were used to predict the microscopic and macroscopic pK_a values for a set of 24 molecules as a part of the SAMPL6 blind challenge. The SMD solvation model was employed with M06-2X and different basis sets to evaluate three pK_a calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pK_a units) and has high correlation (R² = 0.93), with respect to experiment. This approach can be improved by applying a linear correction to yield an RMSE of 0.73 pK_a units. Additionally, we consider including explicit solvent representation and multiple lower-energy conformations to improve the predictions for outliers. Adding three water molecules explicitly can reduce the error by 2-4 pK_a units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pK_a prediction.

Effects of structural changes of new organophosphorus cationic exchangers on a solvent extraction of cobalt, nickel and manganese from acidic chloride media

The effects of structural changes of organophosphorus cationic exchangers on metal-distribution curves (extraction efficiency vs. pH) have been investigated for the recovery of cobalt(ii), nickel(ii) and manganese(ii) from acidic chloride solutions.

Linear free-energy relationships between a single gas-phase ab initio equilibrium bond length and experimental pKa values in aqueous solution

Remarkably simple yet effective linear free energy relationships were discovered between a single ab initio computed bond length in the gas phase and experimental pKa values in aqueous solution. The formation of these relationships is driven by chemical features such as functional groups, meta/para substitution and tautomerism. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets (HCSs). Surprisingly, all molecules in a given high correlation subset share the same conformation in the gas phase. Here we show that accurate pKa values can be predicted from such HCSs. This is achieved within an accuracy of 0.2 pKa units for 5 drug molecules.

First principles pKa calculations on carboxylic acids using the SMD solvation model: effect of thermodynamic cycle, model chemistry, and explicit solvent molecules

Aqueous pK(a) values are calculated from first principles for a set of carboxylic acids using the SMD solvation model with various model chemistries, thermodynamic cycles, and treatments of explicit solvation. In all, 108 unique theoretical protocols are examined. The direct (D) and water proton exchange (PX) cycles are trialled along with a new approach, termed the semidirect (SD) cycle. The SD thermodynamic cycle offers some improvements over the D and PX schemes, as it bypasses the gas-phase heterolytic bond dissociation calculation required in the conventional D approach while also avoiding an aqueous OH(-) calculation required by the PX method when using water as the reference acid. With all three cycles, the recommended model chemistry employs M05-2X/cc-pVTZ Gibbs energies of solvation with a single discrete water molecule and a high-level composite method for the gas-phase reaction energies. With the SD cycle, these calculations result in a mean unsigned error of less than 1 pK(a) units, with respective mean signed error and maximum unsigned error of less than 0.5 and 2 pK(a) units. Similar results are obtained with the D and PX cycles, and further improvement is required in both the gas and aqueous phase ab initio energy calculations before we can truly discriminate between the thermodynamic cycles investigated here.