Tautomer Preference in PDB Complexes and its Impact on Structure-Based Drug Discovery

Tautomer enrichment is a key step of ligand preparation prior to virtual screening. In this paper, we have investigated how tautomer preference in various media (water, gas phase, and crystal) compares to tautomer preference at the active site of the protein by analyzing the different possible H-bonding contacts for a set of 13 tautomeric structures. In addition, we have explored the impact of four different protocols for the enumeration of tautomers in virtual screening by using Flap, Glide, and Gold as docking tools on seven targets of the DUD data set. Excluding targets in which the binding does not involve tautomeric atoms (HSP90, p38, and VEGFR2), we found that the average receiver operating characteristic curve enrichment at 10% was 0.25 (Gold), 0.24 (Glide), and 0.50 (Flap) by considering only tautomers predicted to be unstable in water versus 0.41 (Gold), 0.56 (Glide), 0.51 (Flap) by limiting the enumeration process only to the predicted most stable tautomer. The inclusion of all tautomers (stable and unstable) yielded slightly poorer results than considering only the most stable form in water

Fluorine-Protein Interactions and 19F NMR Isotropic Chemical Shift: An Empirical Correlation

An empirical correlation between the fluorine isotropic chemical shift measured in 19F NMR spectroscopy and the type of fluorine-protein interactions observed in the crystal structures is presented. The CF, CF2 and CF3 groups present in fluorinated ligands contained in the Protein Data Bank were classified according to their 19F NMR chemical shift and their close intermolecular contacts with the protein atoms. Shielded fluorines, i.e., with increased electron density, are observed preferentially in close contact to hydrogen bond donors of the protein suggesting the possibility of intermolecular hydrogen bond formation. Deshielded fluorines are found preferentially in close contact with hydrophobic sidechains and with the carbon of the carbonyl of the protein backbone. Correlation of the 19F NMR chemical shift and hydrogen bond distance derived experimentally and computed with quantum chemical methods is also presented. The proposed “rule of shielding” provides some insight and guidelines in the selection of the appropriate fluorinated moiety to be judiciously inserted into the molecule for making the most favorable interactions with the receptor

Fluorine Local Environment: From Screening to Drug Design

Fluorine is widely used in the lead optimization phase of drug discovery projects. More recently, fluorine NMR-based spectroscopy has emerged as a versatile, reliable and efficient tool for performing binding and biochemical assays. Different libraries of fluorinated compounds, designed by maximizing the chemical space around the fluorine atom, can be screened for identifying fluorophilic hotspots on the desired macromolecular target. A statistical analysis of the fluorine NMR chemical shift, which is a marker of the fluorine local environment, and of the X-Ray structures of fluorinated molecules has resulted in the development of the “rule of shielding”. This method could become a useful tool for lead optimization and for designing novel chemical scaffolds that recognize protein distinct structural motifs

Hydrogen Bond Acceptor Propensity of different Fluorine Atom Types: An Analysis of Experimentally- and Computationally-derived Parameters

The propensity of organic fluorine acting as a weak hydrogen bond acceptor (HBA) in intermolecular and intramolecular interactions has been the subject of many experimental and theoretical studies often reaching different conclusions. Over the last few years new and stronger evidences have emerged for the direct involvement of fluorine in weak hydrogen bond (HB) formation. However, not all the fluorine atom types can act as weak HBA. In this work we have analyzed the differential HBA propensity of various types of fluorine atoms with a particular emphasis for the different types of alkyl fluorides. This is carried out by evaluating ab initio computed parameters, experimental 19 F NMR chemical shifts and small molecule crystallographic structures (extracted from the CSD database). According to this analysis, shielded (with reference to the 19 F NMR chemical shift) mono-fluorinated alkyl motifs display the highest HBA propensity in agreement with solution studies. Although much weaker than other well characterized HB complexes, the fragile HBs formed by these fluorinated motifs have important implications for the chemical-physical and structural properties of the molecules, chemical reactions and protein/ligand recognition

Weak intermolecular hydrogen-bonds with fluorine: detection and implications for enzymatic/chemical reactions, for chemical physical properties and for ligand/protein fluorine NMR screening

It is well known that strong hydrogen bonding interactions play an important role in many chemical and biological systems. However, weak or very weak hydrogen bonds, often difficult to detect and characterize, can also be relevant in many recognition and reactive processes. Fluorine, as hydrogen bond acceptor, has been the subject of many controversial discussions and there are different opinions about it. Recently, it appears that there is compelling experimental evidence for the involvement of fluorine in weak intramolecular or intermolecular hydrogen bonds. Using established NMR methods, we have characterized and measured the strength of intermolecular hydrogen bond complexes involving the fluorine moieties CH2F, CHF2 and CF3 and compared to the well-known hydrogen bond complex formed between acetophenone and the strong hydrogen bond donor p-fluorophenol. We now report evidence of formation of hydrogen bond involving fluorine with significantly weaker donors i.e. 5-fluoroindole and water. A simple NMR method is proposed for the simultaneous measurement of the strength of hydrogen bond formation of an acceptor with a donor and with water. Important implications of these results for enzymatic/chemical reactions involving fluorine, for chemical physical properties and ligand/protein 19F NMR screening are analyzed with experiments and theoretical simulations

Ligand-Based Fluorine NMR Screening: Principles and Applications in Drug Discovery Projects

Ligand-based fluorine NMR screening has gained popularity in drug discovery projects during the past decade and has become a powerful methodology to produce high quality hits. Its high sensitivity to protein binding makes it particularly suitable for fragment screening, allowing detection and binding strength measurement of very weak affinity ligands. The screening can be performed in direct or competition format, and its versatility allows application to complex biological and chemical systems. As the potential of the methodology has now been recognized and successfully demonstrated in several relevant medicinal chemistry projects, it is now an appropriate time to report the learned lessons and point the way to the future. In this Perspective the principles of the methodology along with several applications to pharmaceutical projects are presented

QM Assisted ML for 19F NMR Chemical Shift Prediction

Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens. In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set. Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm