Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

https://www.mdpi.com/1422-0067/20/18/4331/pdf

Molecular Docking: Shifting Paradigms in Drug Discovery.

https://www.jstage.jst.go.jp/article/jjsth/26/1/26_17/_pdf

Seven-transmembrane receptors

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking towards enhanced molecular dynamics strategies.

/pdf/bridging-molecular-docking-to-molecular-dynamics-in-2fn4gn6510.pdf

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview.

The recent pandemic of COVID-19, caused by SARS-CoV-2, is unarguably the most fearsome compared with the earlier outbreaks caused by other coronaviruses, SARS-CoV and MERS-CoV. Human ACE2 is now established as a receptor for the SARS-CoV-2 spike protein. Where variations in the viral spike protein, in turn, lead to the cross-species transmission of the virus, genetic variations in the host receptor ACE2 may also contribute to the susceptibility and/or resistance against the viral infection. This study aims to explore the binding of the proteins encoded by different human ACE2 allelic variants with SARS-CoV-2 spike protein. Briefly, coding variants of ACE2 corresponding to the reported binding sites for its attachment with coronavirus spike protein were selected and molecular models of these variants were constructed by homology modeling. The models were then superimposed over the native ACE2 and ACE2-spike protein complex, to observe structural changes in the ACE2 variants and their intermolecular interactions with SARS-CoV-2 spike protein, respectively. Despite strong overall structural similarities, the spatial orientation of the key interacting residues varies in the ACE2 variants compared with the wild-type molecule. Most ACE2 variants showed a similar binding affinity for SARS-CoV-2 spike protein as observed in the complex structure of wild-type ACE2 and SARS-CoV-2 spike protein. However, ACE2 alleles, rs73635825 (S19P) and rs143936283 (E329G) showed noticeable variations in their intermolecular interactions with the viral spike protein. In summary, our data provide a structural basis of potential resistance against SARS-CoV-2 infection driven by ACE2 allelic variants.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.25832

Structural variations in human ACE2 may influence its binding with SARS-CoV-2 spike protein.

Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand–receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand–protein recognition pathways of six different case studies that...

Deciphering the Complexity of Ligand–Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations

Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach

Unquestionably, water appears to be an active player in the noncovalent protein-ligand binding process, as it can either bridge interactions between protein and ligand or can be replaced by the bound ligand. Accordingly, in the last decade, alternative computational methodologies have been sought with the aim of predicting the position and thermodynamic profile of water molecules (i.e., hydration sites) in the binding site using either the ligand-bound or ligand-free protein conformation. Herein, we present an alternative approach, named AquaMMapS, that provides a three-dimensional sampling of putative hydration sites. Interestingly, AquaMMapS can post-inspect molecular dynamics (MD) trajectories obtained from different MD engines using indifferently crystallographic or docking-driven structures as a starting point. Moreover, AquaMMapS is naturally integrated into supervised molecular dynamics (SuMD) simulations, presenting the possibility to inspect hydration sites during the ligand-protein association process. Finally, a penalty scoring method, named AquaMMapScoring(AMS), was developed to evaluate the number and nature of the water molecules displaced by a ligand approaching its binding site during the binding event, guiding a medicinal chemist to explore the most suitable regions of a ligand that can be decorated either with or without interfering with the interaction networks mediated by water molecules with specific recognition regions of the protein.

https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cmdc.201700564

AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association.

Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions.

Veronica Salmaso

Papers

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview.

Deciphering the Complexity of Ligand–Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations

Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach

AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association.

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2