Computational Study of pKa shift of Aspartate residue in Thioredoxin: Role of Configurational Sampling and Solvent Model

Alchemical free energy calculations are widely used in predicting pKa, and binding free energy calculations in biomolecular systems. These calculations are carried out using either Free Energy Perturbation (FEP) or Thermodynamic Integration (TI). Numerous efforts have been made to improve the accuracy and efficiency of such calculations, especially by boosting conformational sampling. In this paper, we use a technique that enhances the conformational sampling by temperature acceleration of collective variables for alchemical transformations and applies it to the prediction of pKa of the buried Asp 26 residue in thioredoxin protein. We discuss the importance of enhanced sampling in the pKa calculations. The effect of the solvent models in the computed pKa values is also presented.Comment: 29 pages with 13 figure

Experimental versus theoretical log D_7.4, pK_a and plasma protein binding values for benzodiazepines appearing as new psychoactive substances

The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances in order to interpret and predict their effects upon humans. Experimental log D7.4, pKa and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3‐hydroxyphenazepam, 4’‐chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam and pyrazolam) and compared with values generated by various software packages (ACD/I‐lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I‐LAB returned the most accurate values for log D7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pKa. Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future ‘training’ of predictive models for these new psychoactive substances

Examining the PM6 semiempirical method for pKa prediction across a wide range of oxyacids

The pK~a~ estimation ability of the semiempirical PM6 method was evaluated across a broad range of oxyacids and compared to results obtained using the SPARC software program. Compound classes under consideration included acetic acids, alicyclic and aromatic heterocyclic acids, benzoic acids, boronic acids, hydroxamic acids, oximes, peroxides, peroxyacids, phenols, α-saturated acids, α-saturated alcohols, sulfinic acids, α-unsaturated acids, and α-unsaturated alcohols. PM6 accurately predicts the acidity of acetic and benzoic acids and their derivatives, but is less reliable for alicyclic and aromatic heterocyclic acids and phenols. α-Saturated acids are reliably modeled by PM6 except for polyacid derivatives with α-alcohol moieties. α-Saturated alcohols only appear to yield reliable PM6 results where an α-hydroxy or α-alkoxy moiety is absent. Carboxylic acids with simple α-alkene unsaturation are well approximated by PM6 except where alkyne α-unsaturation or α-carboxylation are also present. The PM6 and SPARC methods exhibit approximately equal pKa prediction performance for the acetic, alicyclic, and benzoic acids. SPARC outperforms PM6 on the peroxides, peroxyacids, phenols, and α-saturated acids and α-saturated alcohols. pKa values for boron, nitrogen, and sulfur oxyacids do not appear to be reliably estimated by either the PM6 or SPARC methods. The findings will help guide the potential appropriateness of results from the PM6 pK~a~ estimation method for waste treatment and environmental fate investigations

An assessment of organic solvent based equilibrium partitioning methods for predicting the bioconcentration behavior of perfluorinated sulfonic acids, carboxylic acids, and sulfonamides

SPARC, KOWWIN, and ALOGPS octanol-water partitioning (log K~ow~) and distribution (log D) constants were calculated for all C~1~ through C~8~ and the straight chain C~9~ through C~15~ perfluoroalkyl sulfonic acids (PFSAs) and carboxylic acids (PFCAs). Application of five established models for estimating bioconcentration factors (BCFs) were applied to the PFSA and PFCA log K~ow~ and log D data and compared to available field and laboratory BCF data. Wide variability was observed between the methods for estimating log K~ow~ and log D values, ranging up to several log units for particular congeners, and which was further compounded by additional variability introduced by the different BCF equations applied. With the exception of n-perfluorooctanecarboxylic acid (n-PFOA), whose experimental BCF was poorly modeled by all approaches, the experimental BCF values of the other PFSA and PFCA congeners were reasonably approximated by the ALOGPS log P values in combination with any of the five log K~ow~ based BCF equations. The SPARC and KOWWIN log K~ow~ and log D values provided generally less accurate BCF estimates regardless of the BCF equation applied. However, the SPARC K~ow~ values did provide BCF estimates for PFSA congeners with errors <0.3 log units using any of the five BCF equations. Model lipophilic and proteinophilic solvent based distribution constant calculations for the PFSA and PFCA congeners with experimental BCFs exhibited similar relationships with their corresponding BCF values. For longer chain PFCA and PFSA congeners, increasing hydrophobicity of the perfluoroalkyl chain appears to be driving corresponding increases in BCF values. Perfluorooalkyl sulfonamides are expected to display similar chain length and branching pattern influences on BCFs, but no experimental data are currently available upon which to validate the estimated values which range widely between the various approaches by up to 10 log units. The amidic proton acidity on primary and secondary perfluoroalkyl sulfonamides will play a significant role in the partitioning of these compounds with both abiotic and biotic organic matter, and will need to be taken into account when assessing their environmental and biological fate

Ancestral Causal Inference

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.Comment: In Proceedings of Advances in Neural Information Processing Systems 29 (NIPS 2016

Towards the “Eldorado” of pKa Determination: A Reliable and Rapid DFT Model

The selection of a “perfect tool” for the theoretical determination of acid-base dissociation constants (Ka) is still puzzling. Recently, we developed a user-friendly model exploiting CAM-B3LYP for determining pKa with impressive reliability. Herein, a new challenge is faced, examining a panel of functionals belonging to different rungs of the “Jacob’s ladder” organization, which classifies functionals according to their level of theory. Specifically, meta-generalized gradient approximations (GGAs), hybrid-GGAs, and the more complex range-separated hybrid (RSH)-GGAs were investigated in predicting the pKa of differently substituted carboxylic acids. Therefore, CAM-B3LYP, WB97XD, B3PW91, PBE1PBE, PBEPBE and TPSSTPSS were used, with 6-311G+(d,p) as the basis set and the solvation model based on density (SMD). CAM-B3LYP showed the lowest mean absolute error value (MAE = 0.23) with relatively high processing time. PBE1PBE and B3PW91 provided satisfactory predictions (MAE = 0.34 and 0.38, respectively) with moderate computational time cost, while PBEPBE, TPSSTPSS and WB97XD led to unreliable results (MAE > 1). These findings validate the reliability of our model in predicting carboxylic acids pKa, with MAE well below 0.5 units, using a simplistic theoretical level and a low-cost computational approach

Intrinsically disordered inhibitor of glutamine synthetase is a functional protein with random-coil-like pKa values

The sequential action of glutamine synthetase (GS) and glutamate synthase (GOGAT) in cyanobacteria allows the incorporation of ammonium into carbon skeletons. In the cyanobacterium Synechocystis sp. PCC 6803, the activity of GS is modulated by the interaction with proteins, which include a 65-residue-long intrinsically disordered protein (IDP), the inactivating factor IF7. This interaction is regulated by the presence of charged residues in both IF7 and GS. To understand how charged amino acids can affect the binding of an IDP with its target and to provide clues on electrostatic interactions in disordered states of proteins, we measured the pKa values of all IF7 acidic groups (Glu32, Glu36, Glu38, Asp40, Asp58, and Ser65, the backbone C-terminus) at 100 mM NaCl concentration, by using NMR spectroscopy. We also obtained solution structures of IF7 through molecular dynamics simulation, validated them on the basis of previous experiments, and used them to obtain theoretical estimates of the pKa values. Titration values for the two Asp and three Glu residues of IF7 were similar to those reported for random-coil models, suggesting the lack of electrostatic interactions around these residues. Furthermore, our results suggest the presence of helical structure at the N-terminus of the protein and of conformational changes at acidic pH values. The overall experimental and in silico findings suggest that local interactions and conformational equilibria do not play a role in determining the electrostatic features of the acidic residues of IF7.Ministerio de Economía y Competitividad CTQ 2015-64445-R, BFU2013- 41712-P, BIO2016-75634PJunta de Andalucía BIO-284Generalitat Valenciana Prometeo 018/201