1,720,983 research outputs found
A rational design of α-helix-shaped peptides employing the hydrogen-bond surrogate approach: A modulation strategy for ras-rasgrf1 interaction in neuropsychiatric disorders
No full textIn the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation has been proposed as a potential target mechanism to attenuate ERK signalling in the brain. Previously, we showed that a cell penetrating peptide, RB3, was able to inhibit downstream signalling by preventing RasGRF1 (Ras guanine nucleotide-releasing factor 1), a neuronal specific GDP/GTP exchange factor, to bind Ras proteins, both in brain slices and in vivo, with an IC50 value in the micromolar range. The aim of this work was to mutate and improve this peptide through computer-aided techniques to increase its inhibitory activity against RasGRF1. The designed peptides were built based on the RB3 peptide structure corresponding to the α-helix of RasGRF1 responsible for Ras binding. For this purpose, the hydrogen-bond surrogate (HBS) approach was exploited to maintain the helical conformation of the designed peptides. Finally, residue scanning, MD simulations, and MM-GBSA calculations were used to identify 18 most promising α-helix-shaped peptides that will be assayed to check their potential activity against Ras-RasGRF1 and prevent downstream molecular events implicated in brain disorders
Convolutional architectures for virtual screening
Full text linkBackground: A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. Results: A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination). Conclusion: The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised
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
EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening
Full text linkIn recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands’ bioactivity on a data set containing twenty protein kinases with similar binding sites to CDK1. The data set itself is presented, and the architecture is explained in detail along with its training procedure. We report experimental results and an explainability analysis to assess the contribution of each fingerprint to different targets
Exploring the SARS-CoV-2 proteome in the search of potential inhibitors via structure-based pharmacophore modeling/docking approach
Full text linkTo date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries
Resolving a guanine-quadruplex structure in the SARS-CoV-2 genome through circular dichroism and multiscale molecular modeling
Full text linkThe genome of SARS-CoV-2 coronavirus is made up of a single-stranded RNA fragment that can assume a specific secondary structure, whose stability can influence the virus's ability to reproduce. Recent studies have identified putative guanine quadruplex sequences in SARS-CoV-2 genome fragments that are involved in coding for both structural and non-structural proteins. In this contribution, we focus on a specific G-rich sequence referred to as RG-2, which codes for the non-structural protein 10 (Nsp10) and assumes a guanine-quadruplex (G4) arrangement. We provide the secondary structure of RG-2 G4 at atomistic resolution by molecular modeling and simulation, validated by the superposition of experimental and calculated electronic circular dichroism spectra. Through both experimental and simulation approaches, we have demonstrated that pyridostatin (PDS), a widely recognized G4 binder, can bind to and stabilize RG-2 G4 more strongly than RG-1, another G4 forming sequence that was previously proposed as a potential target for antiviral drug candidates. Overall, this study highlights RG-2 as a valuable target to inhibit the translation and replication of SARS-CoV-2, paving the way towards original therapeutic approaches against emerging RNA viruses.Parallel or hybrid? A combination of multiscale molecular modeling and circular dichroism is used to predict a G-quadruplex structure at atomistic resolution in the SARS-CoV-2 genome
ENO1/Hsp70 Interaction Domains: In Silico and In Vitro Insight for a Putative Therapeutic Target in Cancer
Full text linkAlpha-enolase (ENO1) is a multifunctional protein with oncogenic roles. First described as a glycolytic enzyme, the protein performs different functions according to its cellular localization, post-translational modifications, and binding partners. Cell surface-localized ENO1 serves as a plasminogen-binding receptor, and it has been detected in several cell types, including various tumor cells. The plasminogen system plays a crucial role in pathological events, such as tumor cell invasion and metastasis. We have previously demonstrated that the interaction of ENO1 with the multifunctional chaperone Hsp70 increases its surface localization and the migratory and invasive capacity of breast cancer cells, thus representing a novel potential target to counteract the metastatic potential of tumors. Here, we have used computational approaches to map the putative binding region of ENO1 to Hsp70 and predict the key anchoring amino acids, also called hot spots. In vitro coimmunoprecipitation experiments were then used to validate the in silico prediction of the protein-protein interaction. This work outcome will be further used as a guide for the design of potential ENO1/HSP70 inhibitors
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