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Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK_a Shifts Mislead Popular Bioisosterism Concepts.

ABSTRACT: Medicinal-chemistry optimization follows strategies replacing functional groups and attaching larger substituents at a promising lead scaffold. Well-established bioisosterism rules are considered, however, it is difficult to estimate whether the introduced modifications really match the required properties at a binding site. The electron density distribution and pK_a values are modulated influencing protonation states and bioavailability. Considering the adjacent H-bond donor/acceptor pattern of the hinge binding motif in a kinase, we studied by crystallography a set of fragments to map the required interaction pattern. Unexpectedly, benzoic acid and benzamidine, decorated with the correct substituents, are totally bioisosteric just as carboxamide and phenolic OH. A mono-dentate pyridine nitrogen out-performs bi-dentate functionalities. The importance of correctly designing pK_a values of attached functional groups by additional substituents at the parent scaffold is rendered prominent.

SUBMITTER: Oebbeke M

PROVIDER: S-EPMC7821265 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK<sub>a</sub> Shifts Mislead Popular Bioisosterism Concepts.

Oebbeke Matthias M Siefker Christof C Wagner Björn B Heine Andreas A Klebe Gerhard G

Angewandte Chemie (International ed. in English) 20201029 1

Medicinal-chemistry optimization follows strategies replacing functional groups and attaching larger substituents at a promising lead scaffold. Well-established bioisosterism rules are considered, however, it is difficult to estimate whether the introduced modifications really match the required properties at a binding site. The electron density distribution and pK<sub>a</sub> values are modulated influencing protonation states and bioavailability. Considering the adjacent H-bond donor/acceptor ...[more]

PMID: 33021032

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Project description:The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

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Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK_a Shifts Mislead Popular Bioisosterism Concepts.

Publications

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK<sub>a</sub> Shifts Mislead Popular Bioisosterism Concepts.

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Fragment Binding to Kinase Hinge: If Charge Distribution and Local pKa Shifts Mislead Popular Bioisosterism Concepts.

Publications

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK<sub>a</sub> Shifts Mislead Popular Bioisosterism Concepts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pK_a Shifts Mislead Popular Bioisosterism Concepts.