Computer Prediction of pK a Values in Small Molecules and Proteins

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

Recent Progress in the Endosomal Escape Mechanism and Chemical Structures of Polycations for Nucleic Acid Delivery

Nucleic acid-based therapies are seeing a spiralling surge. Stimuli-responsive polymers, especially pH-responsive ones, are gaining widespread attention because of their ability to efficiently deliver nucleic acids. These polymers can be synthesized and modified according to target requirements, such as delivery sites and the nature of nucleic acids. In this regard, the endosomal escape mechanism of polymer-nucleic acid complexes (polyplexes) remains a topic of considerable interest owing to various plausible escape mechanisms. This review describes current progress in the endosomal escape mechanism of polyplexes and state-of-the-art chemical designs for pH-responsive polymers. The importance is also discussed of the acid dissociation constant (i.e., pKa) in designing the new generation of pH-responsive polymers, along with assays to monitor and quantify the endosomal escape behavior. Further, the use of machine learning is addressed in pKa prediction and polymer design to find novel chemical structures for pH responsiveness. This review will facilitate the design of new pH-responsive polymers for advanced and efficient nucleic acid delivery.

pKa prediction of per- and polyfluoroalkyl acids in water using in silico gas phase stretching vibrational frequencies and infrared intensities

To successfully understand and model the environmental fate of per- and polyfluoroalkyl substances (PFAS), it is necessary to know key physicochemical properties (PChPs) such as pKa; however, measured PChPs of PFAS are scarce and of uncertain reliability. In this study, quantitative structure-activity relationships (QSARs) were developed by correlating calculated (M062-X/aug-cc-pVDZ) vibrational frequencies (VF) and corresponding infrared intensities (IRInt) to the pKa of carboxylic acids, sulfonic acids, phosphonic acids, sulfonamides, betaines, and alcohols. Antisymmetric stretching VF of the anionic species were used for all subclasses except for alcohols where the OH stretching VF performed better. The individual QSARs predicted the pKa for each subclass mostly within 0.5 pKa units from the experimental values. The inclusion of IRInt as a pKa predictor for carboxylic acids improved the results by decreasing the root-mean-square error from 0.35 to 0.25 (n > 100). Application of the developed QSARs to estimate the pKa of PFAS within each subclass revealed that the length of the perfluoroalkyl chain has minimal effect on the pKa, consistent with other models but in stark contrast with the limited experimental data available.

Reliable and Accurate Prediction of Single-Residue pKa Values through Free Energy Perturbation Calculations

Accurate prediction of the pK_a's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue pK_a values. We begin with an initial set of 191 residues with experimentally determined pK_a values. To isolate sampling limitations from force field inaccuracies, we develop an algorithm to classify residues whose environments are significantly affected by crystal packing effects. We then report an approach to identify buried histidines that require significant sampling beyond what is achieved in typical FEP calculations. We therefore define a clean data set not requiring algorithms capable of predicting major conformational changes on which other pK_a prediction methods can be tested. On this data set, we report an RMSE of 0.76 pK_a units for 35 ASP residues, 0.51 pK_a units for 44 GLU residues, and 0.67 pK_a units for 76 HIS residues.

Potentiometric Determination of Acid Dissociation Constants (pK a) for an Anticancer Pyrrole-Imidazole Polyamide

To optimize the pharmacological properties of an anticancer pyrrole-imidazole (Py-Im) polyamide (PIP-1), we characterized the acid dissociation constants of PIP-1, three other structurally related hairpin-shaped polyamides, and a cyclic polyamide bearing the same core sequence as PIP-1 via potentiometric titration. The acidities of the carboxylic acid at the C-terminus and the tertiary amine in the triamine linker remained very similar among the polyamides tested, whereas the pK a of the N-methylimidazole (Im) moieties varied with the peptide sequence and molecular architecture. A nearly 0.2 pH unit pK a shift of terminal Im toward the neutral state compared to internal Im was observed. Furthermore, according to the dissociation constants, a speciation diagram of PIP-1 as a function of pH is presented, which allows an assessment of the net charge and distribution of protonated species in the range of physiological pH.

PROTACs bearing piperazine-containing linkers: what effect on their protonation state?

Proteolysis targeting chimeras (PROTACs) represent an emerging class of compounds for innovative therapeutic application. Their bifunctional nature induces the formation of a ternary complex (target protein/PROTAC/E3 ligase) which allows target protein ubiquitination and subsequent proteasomal-dependent degradation. To date, despite great efforts being made to improve their biological efficacy PROTACs rational design still represents a challenging task, above all for the modulation of their physicochemical and pharmacokinetics properties. Considering the pivotal role played by the linker moiety, recently the insertion of a piperazine moiety into the PROTAC linker has been widely used, as this ring can in principle improve rigidity and increase solubility upon protonation. Nevertheless, the pK _a of the piperazine ring is significantly affected by the chemical groups located nearby, and slight modifications in the linker could eliminate the desired effect. In the present study, the pK _a values of a dataset of synthesized small molecule compounds including PROTACs and their precursors have been evaluated in order to highlight how a fine modulation of piperazine-containing linkers can impact the protonation state of these molecules or similar heterobifunctional ones. Finally, the possibility of predicting the trend through in silico approaches was also evaluated.

Recent Developments of Computational Methods for pKa Prediction Based on Electronic Structure Theory with Solvation Models

The protonation/deprotonation reaction is one of the most fundamental processes in solutions and biological systems. Compounds with dissociative functional groups change their charge states by protonation/deprotonation. This change not only significantly alters the physical properties of a compound itself, but also has a profound effect on the surrounding molecules. In this paper, we review our recent developments of the methods for predicting the Ka, the equilibrium constant for protonation reactions or acid dissociation reactions. The pKa, which is a logarithm of Ka, is proportional to the reaction Gibbs energy of the protonation reaction, and the reaction free energy can be determined by electronic structure calculations with solvation models. The charge of the compound changes before and after protonation; therefore, the solvent effect plays an important role in determining the reaction Gibbs energy. Here, we review two solvation models: the continuum model, and the integral equation theory of molecular liquids. Furthermore, the reaction Gibbs energy calculations for the protonation reactions require special attention to the handling of dissociated protons. An efficient method for handling the free energy of dissociated protons will also be reviewed.