1,721,028 research outputs found
Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms.
No full textThis Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug-target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed
Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics
No full textRecently, we have introduced a new method, metadynamics, which is able to sample rarely occurring transitions and to reconstruct the free energy as a function of several variables with a controlled accuracy. This method has been successfully applied in many different fields, ranging from chemistry to biophysics and ligand docking and from material science to crystal structure prediction. We present an important development that speeds up metadynamics calculations by orders of magnitude and renders the algorithm much more robust. We use multiple interacting simulations, walkers, for exploring and reconstructing the same free energy surface. Each walker contributes to the history-dependent potential that, in metadynamics, is an estimate of the free energy. We show that the error on the reconstructed free energy does not depend on the number of walkers, leading to a fully linear scaling algorithm even on inexpensive loosely coupled clusters of PCs. In addition, we show that the accuracy and stability of the method are much improved by combining it with a weighted histogram analysis. We check the validity of our new method on a realistic application
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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Investigation of biologically relevant proteins using the combination of magnetic tweezers experiments and a novel molecular dynamics method
Full text linkWe have developed a novel metric for the path collective variables. This metric measures the distance from reference configurations using a combination of simple collective variables, with their contributions optimised for maximising the path entropy. We have called this approach COMet-Path (computational optimisation of a metric for path collective variables). We have validated it using transitions between the conformational minima of trialanine peptide. To compare it to earlier RMSD metric, we have also applied it to the computation of the absolute binding free energy for the Dasatinib binding to c-Src kinase, and we have obtained good agreement with our previous results. We have then applied this approach also to the problem of predicting absolute binding free energies. As a test system, we used a set of epoxide hydrolase inhibitors. We have developed a technique that allows us to predict their binding free energy values with high accuracy at low computational cost. It has also allowed us to understand more about which collective variables and optimisation settings offer better results for difficult problems. In the next part, we investigated the WW domain of PQBP1, where a Y65C single point mutation is responsible for the emergence of the Golabi-Ito-Hall syndrome. Through the use of magnetic tweezers, we showed that the mutant exhibits unexpected behaviour and sensitivity to redox conditions, unlike the wild type. We have also attempted to model the behaviour of these domains using several molecular dynamics approaches, including COMet-Path. Finally, we applied magnetic tweezers also to the problem of the binding of talin to integrin. We have been able to determine the geometry of this interaction and were able to quantify the force required to break it, along with the unfolding force of the individual components of the talin head domain
Investigation of ligand selectivity and activation dynamics of G protein-coupled receptors using enhanced sampling simulations
Full text linkG protein-coupled receptors (GPCRs) are a large superfamily of transmembrane proteins found in eukaryotes. They play a crucial role in the transduction of signals across the plasma membrane of cells, and are involved in the regulation of a plethora of processes. Due to their function in countless biological pathways they have a primary role in many pathological conditions, and are thus therapeutic targets of great importance. Notwithstanding the growing availability of X-ray and cryo-EM structures and the intense involvement of the scientific community, many gaps are still present in our understanding of the mechanisms of ligand binding, receptor activation and allostery. Computational methods open the possibility for the study of the dynamics of such processes at atomistic resolution, complementing experimental findings. In this work key processes of a number of different GPCRs are explored with the use of computational approaches. Molecular dynamics and enhanced sampling methods are leveraged for sampling rare events of great interest and for the calculation of the associated free energy landscapes. In the first place our study of ligand binding and the selectivity mechanism in adenosine A2a and A1 receptors is reported, elucidating how selectivity arises from an interplay of structural factors. The activation mechanism of glucagon receptor and the coupling with a G protein is then investigated, highlighting the cooperative action of glucagon and G protein in the process. A detailed overview of allosteric antagonism in chemokine receptors is built by mining databases of experimental data and complementing this picture with insights on the dynamics of these receptors. Finally, the performance of TS-PPTIS (Transition State-Partial Path Transition State Sampling), a method for the calculation of kinetic rate constants, is studied for the prediction of ligand binding kinetic rates. The findings of this study add to the understanding of the mechanism of signal transduction through GPCRs, and detail this process from its origin outside the cell to the intracellular medium
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