1,721,490 research outputs found
Determinantes del tamaño de los sistemas partidarios provinciales en Argentina (1983 y 2003)
No full textFil: Cavalli, Andrea. Universidad de San Andrés. Departamento de Ciencias Sociales; Argentina
Multitarget Drug Discovery and Polypharmacology
No full textPromising promiscuity: Guest editors Maria Laura Bolognesi and Andrea Cavalli set the stage for this ChemMedChem Special Issue on Polypharmacology and Multitarget Drugs. They highlight cases in which a departure from the "one target, one drug" paradigm has proven advantageous, with single molecules that can engage two or more targets to address a given pathology. Articles in this issue underscore the importance of focusing ever more attention to polypharmacology in drug discovery efforts
Elucidating the BRCA2-RAD51 interaction through an integrated structural biophysics approach
No full textDifferent kinds of lesions can occur to DNA, and among them, one of the most dangerous is the double strand break (DSB). DSBs can result in mutations, chromosome translocation or deletion. For this kind of lesions, depending on cell cycle phase as well as DNA-end resection, cells have developed specific repair pathways. Among these the error-free homologous recombination (HR) plays a crucial role. HR takes place during S/G2 phases, since the sister chromatids can be used as homologous templates. In this process, hRAD51 and BRCA2 are key players. hRAD51 is a recombinase of 339 amino-acids highly conserved through evolution that displays an intrinsic tendency to form oligomeric structures. BRCA2 is a very large protein of 3418 amino-acids, essential for the recruitment and accumulation of hRAD51 in the nucleus repairing-foci. BRCA2 interacts with hRAD51 through eight, so-called, BRC repeats, composed of 35-40 amino acids. Mutations within this region have been linked to an increased risk of ovarian cancer development. Several reports highlighted that missense mutations within one BRC repeat can hamper BRCA2 activity. Considering the close homology between the BRC repeats, it is striking how these mutations cannot be counterbalanced by the other non-mutated repeats preserving the function and the interactions of BRCA2 with hRAD51. To date the only interaction that has been structurally elucidated, is the one taking place amid the fourth BRC repeat and hRAD51. Nevertheless, due to the structural complexity and dynamics of RAD51, the mechanistic details of each step of RAD51 recruitment and DNA repair remain elusive.
To shed light on the mechanism of hRAD51 defibrillation driven by BRC4, in presence or absence of co-factors, negative staining transmission electron microscopy experiments were combined with size exclusion chromatography data revealing that BRC4 erodes hRAD51 fibrils from their termini and does not attack the fibril at random positions. Nevertheless, the propensity to oligomerization of the WT protein hampered further biophysical studies. A novel stabilized fully human monomeric hRAD51 allowed us to investigate its interaction with BRC4 through orthogonal biophysical methods. SAXS experiments were also carried out on the hRAD51-BRC4 complex to provide novel structural insights on their behavior in complex. Atomistic modelling of generated Alphafold2 models revealed that both proteins display flexible N-terminal domains. These results, along with previous evidence on hRAD51 WT fibrils, suggest that BRC4 binding triggers a conformational rearrangement on the hRAD51 N-terminal domain from a more ordered to an intrinsically disordered state
OBIWAN: An Element-Wise Scalable Feed-Forward Neural Network Potential
No full textEstimating the potential energy of a molecular system at a quantum level of theory is a task of paramount importance in computational chemistry. The often employed density functional theory approach allows one to accomplish this task, yet most often at significant computational costs. This prompted the community to develop so-called machine learning potentials to achieve near-quantum accuracy at molecular mechanics computational cost. In this paper, we introduce OBIWAN, a feed-forward neural network that bears some relevant structural properties that also led to the definition of a new kind of general-purpose neural network layer. Its featurization process scales efficiently with newly added atomic species. This allows one to seamlessly add new atom types without requiring to change the topology of the network. Also, this allows one to train on new data sets leveraging a previously trained OBIWAN, hence converging very quickly. This avoids training from scratch and renders the approach more compliant with a green computing perspective
AMCG: a graph dual atomic-molecular conditional molecular generator
No full textDrug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A straightforward way to represent such structures is via their molecular graph, which in turn can be naturally processed by graph neural networks. This paper introduces AMCG, a dual atomic-molecular, conditional, latent-space, generative model built around graph processing layers able to support both unconditional and conditional molecular graph generation. Among other features, AMCG is a one-shot model allowing for fast sampling, explicit atomic type histogram assignation and property optimization via gradient ascent. The model was trained on the Quantum Machines 9 (QM9) and ZINC datasets, achieving state-of-the-art performances. Together with classic benchmarks, AMCG was also tested by generating large-scale sampled sets, showing robustness in terms of sustainable throughput of valid, novel and unique molecules
Il Crocifisso ligneo della chiesa di San Gaetano a Padova
No full textIl saggio indaga sulla paternità e sulla storia sociale e culturale del Crocifisso della chiesa di San Gaetano di Padova e sulla diffusione del modello del Cristo Vivente nell'area venet
Recent advances in dynamic docking for drug discovery
Full text linkMolecular docking allows the evaluation of ligand-target complementarity. This is the crucial first step in small-molecule drug discovery. Over the last decade, increasing computer power and more efficient molecular dynamics (MD) software have prompted the use of MD for molecular docking. The resulting dynamic docking offers major improvements by (1) taking full account of the structural flexibility of the drug-target system and (2) allowing the computation of the free energy and kinetics associated with drug binding. Here, we examine the recent advances in dynamic docking, while also considering the challenges and limitations that this powerful approach must overcome to impact fast-paced drug discovery
Enzymatic and Inhibition Mechanism of Human Aromatase (CYP19A1) Enzyme. A Computational Perspective from QM/MM and Classical Molecular Dynamics Simulations
No full textThe enzyme human aromatase (HA), a member of the cytochrome P450 family, catalyses in a highly specific and peculiar manner the conversion of estrogens to androgens. Thus, this enzyme is a relevant target for inhibitor design for the treatment of breast cancer and currently there are several HA inhibitors employed in clinical practice. The HA crystal structure was solved only in 2009 and, since then, several studies have been done to characterize a variety of its structural, dynamical and mechanistic properties. In the last decade, the predictive power and the accuracy of computer simulations techniques, either relying on force field or on "ab initio" description of the system, has enormously increased. This was mainly due to the development of more accurate algorithms, which allow accelerating the time-scale accessible by simulations techniques, and to the increase of computer power. Hence, computer simulations can now accurately paint an atomistic picture to the molecular mechanism of biomolecules providing also an estimate of the kinetic and thermodynamic properties of the enzyme at increasingly quantitative level. In this review, on the basis of selected examples taken from our work, we summarize current active research topics concerning HA enzyme, with a focus on computational studies. In particular, we will illustrate current results and novel hypothesis concerning the final (rate-determining) aromatization step promoted by this enzyme, on how the structural/dynamics/functional properties of HA are modulated in a membrane lipophilic environment, and finally on novel possible (allosteric) inhibition mechanisms which may modulate estrogen production in HA
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
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