LAS: a software platform to support oncological data management

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit

Community-Contributed Media Collections: Knowledge at Our Fingertips

Abstract The widespread popularity of the Web has supported collaborative efforts to build large collections of community-contributed media. For example, social video-sharing communities like YouTube are incorporating ever-increasing amounts of user-contributed media, or photo-sharing communities like Flickr are managing a huge photographic database at a large scale. The variegated abundance of multimodal, user-generated material opens new and exciting research perspectives and contextually introduces novel challenges. This chapter reviews different collections of user-contributed media, such as YouTube, Flickr, and Wikipedia, by presenting the main features of their online social networking sites. Different research efforts related to community-contributed media collections are presented and discussed. The works described in this chapter aim to (a) improve the automatic understanding of this multimedia data and (b) enhance the document classification task and the user searching activity on media collections

Screening of world approved drugs against highly dynamical spike glycoprotein of SARS-CoV-2 using CaverDock and machine learning

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer's three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology

Identification of a novel polyfluorinated compound as a lead to inhibit human enzymes aldose reductase and AKR1B10 : structure determination of both ternary complexes and implications for drug design

Aldo-keto reductases (AKRs) are mostly monomeric enzymes which fold into a highly conserved ([alpha]/[beta])8 barrel, while their substrate specificity and inhibitor selectivity are determined by interaction with residues located in three highly variable external loops. The closely related human enzymes aldose reductase (AR or AKR1B1) and AKR1B10 are of biomedical interest because of their involvement in secondary diabetic complications (AR) and in cancer, e.g. hepatocellular carcinoma and smoking-related lung cancer (AKR1B10). After characterization of the IC50 values of both AKRs with a series of polyhalogenated compounds, 2,2',3,3',5,5',6,6'-octafluoro-4,4'-biphenyldiol (JF0064) was identified as a lead inhibitor of both enzymes with a new scaffold (a 1,1'-biphenyl-4,4'-diol). An ultrahigh-resolution X-ray structure of the AR-­NADP+-JF0064 complex has been determined at 0.85 Å resolution, allowing it to be observed that JF0064 interacts with the catalytic residue Tyr48 through a negatively charged hydroxyl group (i.e. the acidic phenol). The non-competitive inhibition pattern observed for JF0064 with both enzymes suggests that this acidic hydroxyl group is also present in the case of AKR1B10. Moreover, the combination of surface lysine methylation and the introduction of K125R and V301L mutations enabled the determination of the X-ray crystallo­graphic structure of the corresponding AKR1B10-NADP+-JF0064 complex. Comparison of the two structures has unveiled some important hints for subsequent structure-based drug-design efforts

Measurement of the t-channel single top quark production cross section in pp collisions at √s =7 TeV

Peer reviewe

Calibration of the CMS Drift Tube Chambers and Measurement of the Drift Velocity with Cosmic Rays

Peer reviewe

Search for microscopic black holes in pp collisions at √s̅ = 7 TeV

Peer reviewe