Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on the hTRPM8 Channel

Abstract

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Preventing the Interaction between Coronaviruses Spike Protein and Angiotensin I Converting Enzyme 2: An In Silico Mechanistic Case Study on Emodin as a Potential Model Compound

“…In the last two decades, computational methods, also as stand-alone studies, supported the characterization of small molecules' activity as well as proteins behavior, and the use of molecular modeling approaches led to advances in their mechanistic understanding [14][15][16]. In this context, the present study relies on a structure-based computational workflow that has previously demonstrated successful as a means to investigate protein-protein and protein-ligand interactions [17,18].…”

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays

“…Previous comparative studies emphasized the efficacy of the EFO approach to develop consensus models by linearly combining diverse scoring functions and/or different docking engines [ 18 , 19 ]. The obtained results revealed that EFO-based equations including three docking scores are able to provide enhanced predictive powers outperforming most of the available consensus and scoring strategies.…”

“…In the first preliminary part, an efficient VS strategy was developed by considering and combining two representative resolved hM 2 structures and a purposely collected database including 30 known allosteric modulators which were dispersed among 2970 suitably selected decoys. As already validated in previous analyses [ 18 , 19 ], the VS strategy involved an extensive rescoring of the computed docking results combined with the development of linear consensus predictive models by applying the enrichment factor optimization algorithm (EFO) [ 20 ]. Then, the so tuned in silico procedures were utilized to screen a DrugBank-based database [ 21 ] including about 6000 known drugs and the resulting six most promising ligands (see Figure 1 ) were experimentally tested.…”

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

“…As detailed under Methods, the study investigates both the binding and isomeric spaces by considering four possible combinations: (1) without including the parameters of both the binding and isomeric spaces but considering only the best scores for each compound (namely, the canonical conditions); (2) by including the parameters of the sole binding space to confirm the encouraging results already pursued in previous studies; (3) by including the parameters of the sole isomeric space to assess its specific relevance; and (4) by variously combining the parameters of both spaces to investigate their synergistic role. All so-computed score parameters were then utilized to develop consensus models by using the enrichment factor optimization (EFO) approach [21], which generates linear combinations of the considered descriptors and proved particularly efficient in previous VS benchmarking studies [22,23].…”