Induced fit docking, and the use of QM/MM methods in docking

Abstract

Docking methods are popular computational techniques in drug discovery to identify new active molecules that bind to a given biological target. Although widely used, the predictive reliability of docking methods is often limited by the inability to accurately and efficiently model protein flexibility and quantify binding strength. We highlight several emerging concepts that address those methodological issues including a discussion on the incorporation of QM/MM methodologies in the scoring process.

Molecular Docking and Structure-Based Drug Design Strategies

“…Covalent docking algorithms are aimed to explore the energy landscape available to the ligand when it is covalently linked to the receptor, as well as evaluate the binding energetics of the interaction [91]. Despite the recent resurgence of covalent drugs, molecular modeling methods devised to address the problem of covalent docking are not as developed as those dedicated to noncovalent docking [92].…”

“…However, the formation of covalent bonds is not satisfactorily approached by these methods [93]. The issue of covalent-bond formation can be appropriately handled by quantum mechanical methods (QM), which are able to explore the whole reaction mechanism [92].…”

Acoplamiento Molecular: Avances Recientes y Retos

“…Notable improvements on docking have been made when molecular dynamics are used to prepare the structures of molecular targets, for instance, to conduct energy minimization and assign charges (Alonso, Bliznyuk & Gready, 2006;Uehara & Tanaka, 2017). For ligand preparation, methods that have improved docking results include: optimization using semiempiric charges (Adeniyi & Soliman, 2017;Marzaro et al, 2013;Oferkin et al, 2015;Xu & Lill, 2013), COSMO solvation (Oferkin et al, 2015), molecular mechanics Poison-Boltzmann/ surface area (Halperin, Wolfson & Nussinov, 2002), and force field optimization (Zoete, Cuendet, Grosdidier & Michielin, 2011). Moreover, it has been shown that forcefield optimization with implicit solvent is comparable to advanced semi-empirical methods (e.g.…”

“…Hence, the first step is the identification of key water molecules and their contribution (Kumar & Zhang, 2013). Methods that have advanced the correct representation of water molecules in proteins include: free energy perturbation methods (Jorgensen & Thomas, 2008), Monte Carlo probability (Parikh & Kellogg, 2014), molecular dynamics of water on the binding site (as implemented by Schrödinger (Kumar & Zhang, 2013;Waszkowycz, Clark & Gancia, 2011)), water displacement as implemented by PLANTS (Korb, Stützle & Exner, 2009), "Attachment" of water molecules to ligands as additional torsions (Lie, Thomsen, Pedersen, Schiøtt & Christensen, 2011), QM/ MM hybrid methods (Xu & Lill, 2013), COSMO solvation, and semi-empirical charges for ligands (Oferkin et al, 2015). Additional methods are "hydrated docking" scripts for Autodock (Forli et al, 2016), protein-centric and ligand centric hydration as implemented by Rossetta (Lemmon & Meiler, 2013), Water docking using Vina (Ross, Morris & Biggin, 2012;Sridhar et al, 2017), WScore (Murphy et al, 2016), and grid inhomogeneous solvation theory applied by Autodock (Uehara & Tanaka, 2016).…”

Accurate modeling of cation–π interactions in enzymes: a case study on the CDPCho:phosphocholine cytidylyltransferase complex

“…However with the development of computing resources one is able to treat larger and larger QM region and now there are advocates of both cluster-based QM calculations on enzymes and QM/MM methods as well, both of which are finding their entries into the pharmaceutical industry as well. [48] Therefore e.g. in the course of modeling enzymatic reactions, if possible, it is worth increasing the size of the QM region to be as large as to have a small effect on the energetic of the reaction, thereby ensuring that all significant interactions are treated at a high level.…”