Halogen bonding in halocarbon-protein complexes and computational tools for rational drug design

ABSTRACT

Introduction: Halogens have a prominent role in drug design. Often used as a mean to improve ADME properties, they are also becoming a tool in protein–ligand recognition given their ability to form a non-covalent interaction, termed halogen bond, where halogens act as electrophilic species interacting with electron-rich partners. Rational drug design of halogen-bonding lead molecules requires an accurate description of halocarbon-protein complexes by computational tools though not all methods are able to tackle this non-covalent interaction.

Areas covered: The authors present a review of computational methodologies that can be used to properly describe halogen bonds in the context of protein–ligand complexes, providing also insights on how these methods can be used in the context of computer-aided drug design.

Expert opinion: Although in the last few years many computational tools, ranging from fast screening methods to the more expensive QM calculations, have been developed to tackle the halogen bonding phenomenon, they are not yet standard in the literature. This will eventually change as official software distributions are including support for halogen bonding in their methods. Tackling desolvation of halogenated species seems to be a good strategy to improve the accuracy of computational methods, that will be more commonly used prior to laboratory work in the future.

KEYWORDS:

• Halogen bonding
• protein-ligand complexes
• drug design
• σ-hole
• force field
• molecular docking
• quantum-mechanical calculations

Article Highlights

• Database mining or surveys allow the clarification of the targetable residues and underlying geometrical features in crystal structures. On the other hand, a few pre-screening methods allow for the fast calculation of halogen bond descriptors in large datasets of compounds.

• Standard force field methods are unable to describe halogen bonds given their inability to properly account for the halogen anisotropy using a single charge to describe the halogen atom. However, this problem can be solved in a simple, yet elegant approach, by placing a positive extra point of charge at the tip of the halogen to represent the σ-hole. Many flavors of this strategy have been implemented in the most popular force fields (AMBER/GAFF, CHARMM, OPLS, GROMOS).

• Docking is quite attractive for virtual screening routines; however, as for force fields, halogen bonding was not standard in scoring functions. This issue has been tackled in recent years, either by using an EP-based strategy, knowledge-based scoring functions or QM-derived scoring functions that specifically account for the halogen bonding capability of halocarbon ligands.

• Application of QM methods in full protein-ligand systems is generally not feasible. However, several strategies have been developed, mainly using smaller models, that can be extremely useful in the context of rational drug design. Alternatively, semiempirical QM methods which were specifically designed to tackle halogen bonding can also be applied to full-sized systems.

• Computational methods have been gaining importance, not only in the interpretation of experimental data but most importantly, in guiding experimental work. In the last few years, several success cases where computational tools were used a priori have been reported, showing the increased accuracy of such methods in tackling halogen bonds in protein–ligand systems.

• This box summarizes the key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The authors are funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal through the Investigador FCT Program IF/00069/2014, exploratory project IF/00069/2014/CP1216/CT0006 (to PJ Costa.), doctoral grant SFRH/BD/116614/2016 (to R Nunes), project PTDC/QUI-QFI/28455/2017, and research unit projects UID/MULTI/00612/2019, UID/MULTI/04046/2019. This work was also financed by FCT through Programa Operacional Regional de Lisboa (Lisboa 2020), Portugal 2020, and the European Regional Development Fund (FEDER) from the European Union under project No. LISBOA-01-0145-FEDER-028455.