1,721,161 research outputs found
Drug Research Meets Network Science: Where Are We?
Full text linkNetwork theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years
Acetylcholinesterase inhibitors as starting point towards improved Alzheimer’s disease therapeutics
No full textThe knowledge about the pathogenesis and the development of the neurodegeneration associated with Alzheimer's disease (AD) has been organised throughout the years into two theories, namely the cholinergic and the amyloid hypotheses. The loss of cholinergic neurotransmission and the abnormal aggregation and deposition of the amyloid-beta peptide (A beta) in the brain are retained as the central events by the two theories, respectively. These phenomena and their pathological consequences are the main targets of the drug discovery strategies based on each hypothesis. However, the two paradigms share some common aspects as shown by several experimental evidences, such that they might even fit into a unifying scenario of neuropathology and neurodegeneration. In this context, in a perspective of drug discovery, the enzyme acetylcholinesterase (AChE) holds a key position, as it is a main target for cholinomimetic AD drugs being responsible for the breakdown of the neurotransmitter, and it is also involved in the aggregation of A beta and the formation of the neurotoxic fibrils. Following this view, in recent years, a drug design strategy has emerged, directed to finding molecules able to inhibit both of these actions exerted by AChE. In this review, we will briefly introduce the biological basis of this strategy, and then will account for the early results obtained in this field in our and in other laboratories. The main focus will be on potential lead compounds for which some experimental evidence exists supporting the hypothesis of their dual action, as AChE inhibitors and blockers of the AChE-induced A beta aggregation
Protein flexibility in drug discovery: From theory to computation
No full textNowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery
Network modeling helps to tackle the complexity of drug–disease systems
Full text linkFrom the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues.This article is categorized under:Neurological Diseases > Computational ModelsInfectious Diseases > Computational ModelsCardiovascular Diseases > Computational Model
An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis
Full text linkDrug repurposing consists in identifying additional uses for known drugs and, since these new findings are built on previous knowledge, it reduces both the length and the costs of the drug development. In this work, we assembled an automated computational pipeline for drug repurposing, integrating also a network-based analysis for screening the possible drug combinations. The selection of drugs relies both on their proximity to the disease on the protein-protein interactome and on their influence on the expression of disease-related genes. Combined therapies are then prioritized on the basis of the drugs’ separation on the human interactome and the known drug-drug interactions. We eventually collected a number of molecules, and their plausible combinations, that could be proposed for the treatment of Huntington's disease and multiple sclerosis. Finally, this pipeline could potentially provide new suggestions also for other complex disorders
The use of stilbene scaffold in medicinal chemistry and multi-target drug design
No full textThe stilbene scaffold is a basic element for a number of biologically active natural and synthetic compounds, and it is considered as a privileged structure. Stilbenes exemplified by resveratrol, combretastatin A-4 and pterostilbene are of significant interest for drug research and development because of their potential in therapeutic and preventive application. Resveratrol, present in grapes and other food products, plays a role in the prevention of several human pathological processes and has been suggested as an anticancer agent. Moreover, recent evidence has revealed its potential effect on the aging process, diabetes and neurological dysfunction. Combretastatin A-4, from the bark of South African bush willow Combretum caffrum, also shows significant antitumor activity. Pterostilbene is closely related to resveratrol, sharing the same unique therapeutic potential as anti-inflammatory, antineoplastic and antioxidant agent. Therefore, research and development of stilbene-based medicinal chemistry have become rapidly evolving and increasingly active topics covering almost the whole range of therapeutic fields. In the present review, we provide an overview of the role of stilbenes in medicinal chemistry. In this context, we highlight the chemical methodologies adopted for the synthesis of stilbene derivatives, and outline the successful design of novel stilbene based hybrids in the field of cancer, Alzheimer's and other relevant diseases. This information may be useful in further design of stilbene-based molecules as new leads for the development of novel agents with clinical potential or as effective chemical probes to dissect biological processes
Probing the transport of Ni(II) ions through the internal tunnels of the Helicobacter pylori UreDFG multimeric protein complex
No full textThe survival of several pathogenic bacteria, such as Helicobacter pylori (Hp), relies on the activity of the nickel dependent enzyme urease. Nickel insertion into urease is mediated by a multimeric chaperone complex (HpUreDFG) that is responsible for the transport of Ni(II) from a conserved metal binding motif located in the UreG dimer (CPH motif) to the catalytic site of the enzyme. The X-ray structure of HpUreDFG revealed the presence of water-filled tunnels that were proposed as a route for Ni(II) translocation. Here, we probe the transport of Ni(II) through the internal tunnels of HpUreDFG, from the CPH motif to the external surface of the complex, using microsecond-long enhanced molecular dynamics simulations. The results suggest a “bucketbrigade” mechanism whereby Ni(II) can be transported through a series of stations found along these internal pathways
Computational approaches to the study of dual-site and peripheral site binding ache inhibitors
No full textComputational studies on biological macromolecules like AChE can be directed at the comprehension of the basic functions of the system, but also at the interpretation of ligand/macromolecule modes of interaction. The latter, in turn, can be aimed at the discovery of new chemical entities able to bind to the protein for therapeutic purposes. Here, we present some of our studies, where we applied different techniques, i.e. docking simulation, molecular dynamics, and virtual screening, to the study of AChE inhibitors contacting the “peripheral anionic site” of the enzyme supposed to be involved in Alzheimer's amyloid β protein aggregation
Dynamic Docking: A Paradigm Shift in Computational Drug Discovery
Full text linkMolecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress
Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations
No full textPredicting the geometry of protein-ligand binding complexes is of primary importance for structure-based drug discovery. Molecular dynamics (MD) is emerging as a reliable computational tool for use in conjunction with, or an alternative to, docking methods. However, simulating the protein-ligand binding process often requires very expensive simulations. This drastically limits the practical application of MD-based approaches. Here, we propose a general framework to accelerate the generation of putative protein-ligand binding modes using potential-scaled MD simulations. The proposed dynamical protocol has been applied to two pharmaceutically relevant systems (GSK-3β and the N-terminal domain of HSP90α). Our approach is fully independent of any predefined reaction coordinate (or collective variable). It identified the correct binding mode of several ligands and can thus save valuable computational time in dynamic docking simulations
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