1,721,003 research outputs found
Exploring protein flexibility during docking to investigate ligand-target recognition
Full text linkLigand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue.
The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline.
Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes
Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview
Full text linkComputational 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
Could Adenosine Recognize its Receptors with a Stoichiometry Other than 1 : 1?
No full textOne of the most largely accepted concepts in the G protein-coupled receptors (GPCRs) field is that the ligand, either agonist or antagonist, recognizes its receptor with a stoichiometry of 1 : 1. Recent experimental evidence, reporting ternary complexes formed by GPCR:orthosteric: allosteric ligands, has complicated the ligand-receptor 1 : 1 binding scenario. Molecular modeling simulations have been used to retrieve insights on the whole ligand-receptor recognition process, beyond information on the final bound state provided by experimental techniques. The simulation of adenosine binding pathways towards the A 2A adenosine receptor highlighted the presence of alternative binding sites (meta-binding sites) beside the canonical orthosteric one, mainly in proximity to the extracellular vestibule. In light of all these considerations, we investigated the possibility that a second molecule of adenosine engages its receptor when this is already in the holo form, generating a ternary complex with a stoichiometry of 2 : 1. Unexpectedly, supervised molecular dynamics (SuMD) simulations showed that the A 2A adenosine receptor could bind the second molecule of adenosine in one of the possible meta-binding sites as well as into its orthosteric site. The formation of this ternary complex, which favored the formation of the intracellular “ionic lock” between R102 (3.50) and E228 (6.30), could putatively be framed in the context of a negative allosteric regulation
Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach
No full textPeptides 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
Supervised Molecular Dynamics (SuMD) Approaches in Drug Design
No full textSupervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand-protein complexes and can provide high-quality protein-ligand models of for which yet experimental confirmation of binding mode is not available
Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2
No full textAbstract
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
AquaMMapS: an alternative tool to monitor the role of water molecules during protein-ligand association
No full textUnquestionably water appears to be an active player in noncovalent protein-ligand association processes, 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, that guess 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 trajectories obtained from different molecular dynamics engines and using indifferently crystallographic or docking-driven structures as a starting point. Moreover, AquaMMapS is naturally integrated to supervised molecular dynamics (SuMD) simulations Finally, a penalty scoring method, named AquaMMapScoring,(AMS), has been developed to evaluate, during the binding event, the number and the nature of the water molecules displaced by a ligand approaching its binding site, 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
Adenosine Receptors in Neuroinflammation and Neurodegeneration
No full textAdenosine plays a crucial role in various pathophysiological conditions, including neuroinflammation and neurodegeneration. Neuroinflammation can be either beneficial or detrimental to the central nervous system, depending on the intensity and duration of the inflammatory response. Across a wide range of brain disorders, neuroinflammation contributes to both the onset and progression of disease. Notably, neuroinflammation is not limited to conditions primarily classified as neuroinflammatory but is also a key factor in other neurological disorders, including life-threatening neurodegenerative diseases. All four adenosine receptor subtypes (A1, A2A, A2B, and A3) are implicated, to varying degrees, in these conditions. This review aims to summarize the roles of individual adenosine receptor subtypes in neuroinflammation and neurodegenerative diseases, emphasizing their therapeutic potential. While some therapeutic applications are well-established with clinically approved drugs, others warrant further investigation due to their promising potential
Post-Docking Refinement of Peptide or Protein-RNA Complexes Using Thermal Titration Molecular Dynamics (TTMD): A Stability Insight
No full textRNA-protein interactions drive and regulate fundamental cellular processes like transcription and translation. Despite being still limited, the growing body of structural data significantly contributes to the characterization of these interactions. However, RNA complexes involving proteins or peptides are not always available due to the structural determination challenges that this biopolymer entails. Consequently, modeling approaches like molecular docking are exploited to generate complexes relevant to structural and pharmaceutical purposes, including analysis of putative drug targets. Docking methods, despite their widespread adoption, are often hindered by limitations in scoring accuracy, which affects the ranking of the generated poses. Postdocking refining methods, including molecular dynamics (MD) approaches, have been developed to tackle this issue. Thermal Titration Molecular Dynamics (TTMD) is an enhanced sampling molecular dynamics technique that has been previously effectively applied to refine protein or RNA-small-molecule docking poses. This study presents the first application of TTMD to RNA-peptide complexes, validating this method on more complex systems and extending its applicability domain. Our findings showcase the capability of this technique to refine peptide-RNA docking poses, correctly identifying native binding modes among decoys for different pharmaceutically relevant targets
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