Transmembrane Peptides as Sensors of the Membrane Physical State

Cell membranes are commonly considered fundamental structures having multiple roles such as confinement, storage of lipids, sustain and control of membrane proteins. In spite of their importance, many aspects remain unclear. The number of lipid types is orders of magnitude larger than the number of amino acids, and this compositional complexity is not clearly embedded in any membrane model. A diffused hypothesis is that the large lipid palette permits to recruit and organize specific proteins controlling the formation of specialized lipid domains and the lateral pressure profile of the bilayer. Unfortunately, a satisfactory knowledge of lipid abundance remains utopian because of the technical difficulties in isolating definite membrane regions. More importantly, a theoretical framework where to fit the lipidomic data is still missing. In this work, we wish to utilize the amino acid sequence and frequency of the membrane proteins as bioinformatics sensors of cell bilayers. The use of an alignment-free method to find a correlation between the sequences of transmembrane portion of membrane proteins with the membrane physical state (MPS) suggested a new approach for the discovery of antimicrobial peptides

COVID-19 in rheumatic diseases in Italy: first results from the Italian registry of the Italian Society for Rheumatology (CONTROL-19)

OBJECTIVES: Italy was one of the first countries significantly affected by the coronavirus disease 2019 (COVID-19) epidemic. The Italian Society for Rheumatology promptly launched a retrospective and anonymised data collection to monitor COVID-19 in patients with rheumatic and musculoskeletal diseases (RMDs), the CONTROL-19 surveillance database, which is part of the COVID-19 Global Rheumatology Alliance. METHODS: CONTROL-19 includes patients with RMDs and proven severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) updated until May 3rd 2020. In this analysis, only molecular diagnoses were included. The data collection covered demographic data, medical history (general and RMD-related), treatments and COVID-19 related features, treatments, and outcome. In this paper, we report the first descriptive data from the CONTROL-19 registry. RESULTS: The population of the first 232 patients (36% males) consisted mainly of elderly patients (mean age 62.2 years), who used corticosteroids (51.7%), and suffered from multi-morbidity (median comorbidities 2). Rheumatoid arthritis was the most frequent disease (34.1%), followed by spondyloarthritis (26.3%), connective tissue disease (21.1%) and vasculitis (11.2%). Most cases had an active disease (69.4%). Clinical presentation of COVID-19 was typical, with systemic symptoms (fever and asthenia) and respiratory symptoms. The overall outcome was severe, with high frequencies of hospitalisation (69.8%), respiratory support oxygen (55.7%), non-invasive ventilation (20.9%) or mechanical ventilation (7.5%), and 19% of deaths. Male patients typically manifested a worse prognosis. Immunomodulatory treatments were not significantly associated with an increased risk of intensive care unit admission/mechanical ventilation/death. CONCLUSIONS: Although the report mainly includes the most severe cases, its temporal and spatial trend supports the validity of the national surveillance system. More complete data are being acquired in order to both test the hypothesis that RMD patients may have a different outcome from that of the general population and determine the safety of immunomodulatory treatments

Fragment based molecular dynamics for drug design

Molecular docking is a computationally efficient method used to predict the conformations adopted by the ligand within a target-binding site. A positive aspect of conventional docking is the possibility of easily distributing the calculation on dedicated grid or cluster. The receptor is usually kept rigid, therefore the changes in the binding pocket geometry induced by the ligand is overlooked. Here we present a new docking approach (DynDock) that exploits molecular dynamics to preserve the flexibility of the receptor. To maintain high computational efficiency, DynDock has been developed to be distributed on a grid. The main advantages of this method are the full flexible molecular docking achieved during the simulation and the reduced number of compounds collected

Artificial Chemical Neural Network for Drug Discovery Applications

The drug design aims to generate chemical species that meet specific criteria, in-cluding efficacy against a pharmacological target, good safety profile, appropriate chemical and biological properties, sufficient novelty to ensure intellectual proper-ty rights for commercial success, etc. Using new algorithms to design and evalu-ate molecules in silicon de novo drug design is increasingly seen as an effective means of reducing the size of the chemical space to something more manageable for identifying chemogenomic research tool compounds and for use as starting points for hit-to-lead optimization

YAMACS: A Python Based Tool Kit for GROMACS

Molecular dynamics (MD) is a powerful tool used to study the evolution of molecular systems and predict their properties from the inherent interactions. GROMACS is a famous tool for MD and developed as open-source software. GROMACS is run from the command line with userprovided configuration files. However, the absence of a graphical user interface (GUI) of GROMACS and proper protocol to develop the input files (Ex: itp files, topology files, etc.) prevent the researcher from visualizing the MD trajectory in a real-time manner as well as addressing the structural problem. This issue was addressed by developing a graphical user interface of GROMACS as plugins for the YASARA molecular graphics suite, called YAMACS. YAMACS is an open-source project and is available on GitHub. The tool can perform MD simulations for protein, protein–ligand complexes, membrane–protein complexes, membrane–protein complexes, and small molecule systems. Easily YAMACS automatizes several steps of input file preparation and allows visualizing the MD trajectory in real-time. At this conference, I will present the application of YAMACS to simulate the complex sphingomyelin/POPC embedded in a membrane of POPC. I will also introduce a collaborative platform to create an open community of users and developers, extend the functionalities of YAMACS, and improve the quality of computational drug design studies

Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study

BackgroundDuring the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. ObjectiveWe aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2–positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data. MethodsWe collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2–positive cases). ResultsWe found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2–positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions. ConclusionsOur data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis

YAMACS: a graphical interface for GROMACS

A graphical user interface for the GROMACS program has been developed as plugins for YASARA molecular graphics suite. The most significant GROMACS methods can be run entirely via a windowed menu system, and the results are shown on screen in real time

SENECA: A Pedagogical Tool Supporting Remote Teaching and Learning

In this paper, we suggest SENECA, a tool that attempts to assist students who follow remote classes in maintaining/capturing attention, allowing them to focus on context-driven learning. Distance education has a number of disadvantages, including a lack of physical interaction between students and teachers, emotional and motivational isolation as a result of this strategy, and a reduction in active engagement. All of these things have an impact on student learning abilities. The largest distractions at home are considered among these disadvantages of distant education, particularly for subjects with low awareness. These distractions cause a movement of the student’s attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool’s technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and browses them to eliminate repetitive portions after each survey. We looked at two different sorts of filters, semantic and summary, to see if the first could distinguish between topics and the second could evaluate the topic’s highlights. Natural Language Processing algorithms are used to extract categories and keywords from the general generated summary. The latter will emphasize the most important points of the speech, while the keywords will be utilized to extract the candidate literature about the discussed topics

GRIMD: distributed computing for chemists and biologists

Motivation: Biologists and chemists are facing problems of high computational complexity that require the use of several computers organized in clusters or in specialized grids. Examples of such problems can be found in molecular dynamics (MD), in silico screening, and genome analysis. Grid Computing and Cloud Computing are becoming prevalent mainly because of their competitive performance/cost ratio. Regrettably, the diffusion of Grid Computing is strongly limited because two main limitations: it is confined to scientists with strong Computer Science background and the analyses of the large amount of data produced can be cumbersome it. We have developed a package named GRIMD to provide an easy and flexible implementation of distributed computing for the Bioinformatics community. GRIMD is very easy to install and maintain, and it does not require any specific Computer Science skill. Moreover, permits preliminary analysis on the distributed machines to reduce the amount of data to transfer. GRIMD is very flexible because it shields the typical computational biologist from the need to write specific code for tasks such as molecular dynamics or docking calculations. Furthermore, it permits an efficient use of GPU cards whenever is possible. GRIMD calculations scale almost linearly and, therefore, permits to exploit efficiently each machine in the network. Here, we provide few examples of grid computing in computational biology (MD and docking) and bioinformatics (proteome analysis)