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 toward enhanced molecular dynamics strategies.

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 we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand-protein recognition pathways.

Various heteroaryl and bicyclo-aliphatic analogues of zwitterionic biaryl P2Y 14 receptor (P2Y 14 R) antagonists were synthesized, and affinity was measured in P2Y 14 R-expressing Chinese hamster ovary cells by flow cytometry. Given this series' low water solubility, various polyethylene glycol derivatives of the distally binding piperidin-4-yl moiety of moderate affinity were synthesized. Rotation of previously identified 1,2,3-triazole attached to the central m-benzoic acid core (25) provided moderate affinity but not indole and benzimidazole substitution of the aryl-triazole. The corresponding P2Y 14 R region is predicted by homology modeling as a deep, sterically limited hydrophobic pocket, with the outward pointing piperidine moiety being the most flexible. Bicyclic-substituted piperidine ring derivatives of naphthalene antagonist 1, e.g., quinuclidine 17 (MRS4608, IC 50 ≈ 20 nM at hP2Y 14 R/mP2Y 14 R), or of triazole 2, preserved affinity. Potent antagonists 1, 7a, 17, and 23 (10 mg/kg) protected in an ovalbumin/Aspergillus mouse asthma model, and PEG conjugate 12 reduced chronic pain. Thus, we expanded P2Y 14 R antagonist structure−activity relationship, introducing diverse physical−chemical properties.

Peptides have gained increased interest as therapeutic agents during recent years. The high specificity and relatively low toxicity of peptide drugs derive from their extremely tight binding to their targets. Indeed, understanding the molecular mechanism of protein-peptide recognition has important implications in the fields of biology, medicine, and pharmaceutical sciences. Even if crystallography and nuclear magnetic resonance are offering valuable atomic insights into the assembling of the protein-peptide complexes, the mechanism of their recognition and binding events remains largely unclear. In this work we report, for the first time, the use of a supervised molecular dynamics approach to explore the possible protein-peptide binding pathways within a timescale reduced up to three orders of magnitude compared with classical molecular dynamics. The better and faster understating of the protein-peptide recognition pathways could be very beneficial in enlarging the applicability of peptide-based drug design approaches in several biotechnological and pharmaceutical fields.