1,721,684 research outputs found
Optimal Transport for Free Energy Estimation
No full textOptimal transport theory is a growing field of mathematics, which has recently found many applications. Here we take advantage of optimal transport for computational free energy estimation. We show analytically, and then via simulation, that this approach is effective in terms of optimizing the barriers of an alchemical transformation
Acetylcholinesterase inhibitors in the context of therapeutic strategies to combat alzheimer's disease
No full textAcetylcholinesterase inhibitors (AChEIs) are a class of drugs useful in the treatment of Alzheimer's disease (AD) as a result of their indirect cholinomimetic effect. In this review, patents claiming AChEIs that have appeared from the late 1990s (after the marketing of second generation compounds) to the present day will be discussed. The patents filed in this period fall into two categories of AChEIs, new products and combinations of drugs. Most of the new compounds are modifications of known drugs, although some novel structures have been claimed. The association of AChEIs with other pharmacological agents is hoped to improve efficacy of treatment by combining effects from the different pharmacological mechanisms of action. To put this discussion of AChEIs into perspective, some observations on the clinical uses of the anticholinesterases are also briefly summarised
Fast and Memory-Efficient Import Vector Domain Description
No full textOne-class learning is a classical and hard computational intelligence task. In the literature, there are some effective and powerful solutions to address the problem. There are examples in the kernel machines realm, Support Vector Domain Description, and the recently proposed Import Vector Domain Description (IVDD), which directly delivers the sample probability of belonging to the class. Here, we propose and discuss two optimization techniques for IVDD to significantly improve the memory footprint and consequently to scale to datasets that are larger than the original formulation. We propose two strategies. First, we propose using random features to approximate the gaussian kernel together with a primal optimization algorithm. Second, we propose a Nyström-like approximation of the functional together with a fast converging and accurate self-consistent algorithm. In particular, we replace the a posteriori sparsity of the original optimization method of IVDD by randomly selecting a priori landmark samples in the dataset. We find this second approximation to be superior. Compared to the original IVDD with the RBF kernel, it achieves high accuracy, is much faster, and grants huge memory savings
Looking for selectivity among cytochrome P450s inhibitors
No full textCytochrome P450s 19 and 17 are very important pharmacological targets in two different fields of cancer chemotherapy. We present here a theoretical study aimed at explaining the molecular basis of inhibitor affinity and selectivity for either P450 19 or P450 17. Docking simulations of two compounds pointed out the major physicochemical features associated with inhibitory activity. Our results, in agreement with site-directed mutagenesis experiments, could be of relevant utility when designing new P450 19 and P450 17 inhibitors
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Protein-ligand binding free energy and kinetics
No full textPredicting drug efficacy with computational tools remains one of the major challenges in drug discovery. Drug efficacy depends on the affinity of a small molecule for its biological counterpart. In addition, it has recently been reported that drug efficacy can also be related to the time that a small molecule spends in contact with its biological target (i.e. the kinetics of unbinding, koff).1 From a computational standpoint, several approaches to the free energy estimation have been reported, while only a few examples have appeared in the literature aimed at predicting the koff of binary complexes.
Recently, we used steered molecular dynamics (SMD) to pull a series of inhibitors out of their complexes with a target enzyme.2 In particular, we investigated a series of flavonoid derivatives, and we computed the force that was required to extract inhibitors from complexes with the FabZ enzyme, a promising anti-malarial target. Although we were not able to determine the flavonoids-FabZ binding free energy, we could clearly distinguish active from inactive inhibitors, by estimating the kinetics stability of these complexes. This was accomplished by calculating the forces required for pulling each ligand out of the protein embrace.3 In a subsequent study, we investigated the full unbinding pathway of a potent and selective COX-2 inhibitor, showing that the binding kinetics can play a role in COX-2 inhibition efficacy and selectivity.4
References
[1] Copeland, R.A., Pompliano, D.R. and Meek, T.D. Nat. Rev. Drug Discov. 2006, 5, 21-42.
[2] Colizzi, F., Perozzo, R., Scapozza, L., Recanatini, M. and Cavalli, A. J. Am. Chem. Soc. 2010, 132, 7361-7371.
[3] Jorgensen, W.L. Nature 2010, 466, 42-43.
[4] Limongelli, V., Bonomi, M., Marinelli, L., Gervasio, F.L., Cavalli, A., Novellino, E., Parrinello, M. Proc. Natl. Acad. Sci. USA 2010, 107, 5411-6
Applications of metadynamics to drug design-related issues
No full textApplications of metadynamics to drug design-related issues
Andrea Cavalli
1Department of Pharmaceutical Sciences, University of Bologna,
Via Belmeloro 6, I-40126 Bologna, Italy
The methadynamics method1 is a relatively new molecular dynamics technique aimed at both enhancing the sampling of regions separated in the phase space, and mapping out the free energy landscape as a function of a small number of parameters commonly referred to as collective variables. The metadynamics algorithm uses repulsive potentials that allow the system to escape from local minima over the lowest transition state, and discourage the system from revisiting the same points in the configurational space. In such a way, the method not only accelerates the simulation of rare events, but also maps out the free energy landscape as a function of chemically relevant collective variables.
This lecture reports on applications of metadynamics to drug design-related issues. To illustrate the potential of metadynamics as docking tool, the dynamics of the tetramethylamonium (TMA) penetration of human acetylcholinesterase gorge will be discussed.2 Then, applications of metadynamics to docking experiments will be presented. In particular, we will show the use of metadynamics as a post-docking tool to discriminate among different minima identified by means of conventional docking software (GOLD, AutoDock, etc.) and cluster analysis performed with the algorithm AClAP.3 Applications of metadynamics as a post-docking tool to the GSK3 and FabI enzyme-inhibitor complexes will be discussed.
[1] Laio A, Parrinello M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 2002, 99, 12562-12566.
[2] Branduardi, D.; Gervasio, F.L.; Cavalli, A.; Recanatini, M.; Parrinello, M. The role of the peripheral anionic site and cation-π interactions in the ligand penetration of the human AChE gorge. J. Am. Chem. Soc. 2005, 127, 9147-9155.
[3] Bottegoni, G.; Rocchia, W.; Recanatini, M.; Cavalli, A. AClAP, Autonomous hierarchical agglomerative Cluster Analysis based Protocol to partition conformational datasets. Bioinformatics 2006, 22, e58-e65
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