Computational Methods for Studying Conformational Behaviors of Cyclic Peptides

Exploring the Efficacy of Noncovalent SARS-CoV-2 Main Protease Inhibitors: A Computational Simulation Analysis Study

The SARS-CoV-2 main protease, as a key target for antiviral therapeutics, is instrumental in maintaining virus stability, facilitating translation, and enabling the virus to evade innate immunity. Our research focused on designing non-covalent inhibitors to counteract the action of this protease. Utilizing a 3D-QSAR model and contour map, we successfully engineered eight novel non-covalent inhibitors. Further evaluation and comparison of these novel compounds through methodologies including molecular docking, ADMET analysis, frontier molecular orbital studies, molecular dynamics simulations, and binding free energy revealed that the inhibitors N02 and N03 demonstrated superior research performance (N02 ΔGbind=-206.648 kJ/mol, N03 ΔGbind=-185.602 kJ/mol). These findings offer insightful guidance for the further refinement of molecular structures and the development of more efficacious inhibitors. Consequently, future investigations can draw upon these findings to unearth more potent inhibitors, thereby amplifying their impact in the treatment and prevention of associated diseases.

Cyclosporin Structure and Permeability: From A to Z and Beyond

Cyclosporins are natural or synthetic undecapeptides with a wide range of actual and potential pharmaceutical applications. Several members of the cyclosporin compound family have remarkably high passive membrane permeabilities that are not well-described by simple structural metrics. Here we review experimental studies of cyclosporin structure and permeability, including cyclosporin-metal complexes. We also discuss models for the conformation-dependent permeability of cyclosporins and similar compounds. Finally, we identify current knowledge gaps in the literature and provide recommendations regarding future avenues of exploration.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Docking Flexible Cyclic Peptides with AutoDock CrankPep

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.