Biology subjects

Hospital, A.

Publications and source records attributed to Hospital, A..

4 recordsLinked to original sources

bioRxiv2024

Systematic study of hybrid triplex topology and stability suggests a general triplex-mediated regulatory mechanism

By combining in-silico, biophysical and in-cellulo experiments, we decipher the topology, physical and potential biological properties of hybrid-parallel nucleic acids triplexes; an elusive structure at the basis of life. We found that hybrid triplex topology follows a stability order: r(Py)-d(Pu){middle dot}r(Py)> r(Py)-d(Pu){middle dot}d(Py)> d(Py)-d(Pu){middle dot}d(Py)> d(Py)-d(Pu){middle dot}r(Py). The r(Py)-d(Pu){middle dot}d(Py) triplex is expected to be the preferred in the cell as it avoids the need to open the duplex reducing the torsional stress required for triplex formation in the r(Py)-d(Pu){middle dot}r(Py) topology. Upon a massive collection of melting data, we have created the first predictor for hybrid triplex stability. Leveraging this predictor, we conducted a comprehensive scan to assess the likelihood of the human genome and transcriptome to engage in triplex formation. Our findings unveil a remarkable inclination - of both the human genome and transcriptome - to generate hybrid triplex formation, particularly within untranslated (UTRs) and regulatory regions, thereby corroborating the existence of a triplex-mediated regulatory mechanism. Furthermore, we found a correlation between nucleosome linkers and TFS which agree with a putative role of triplexes in arranging chromatin structure and local/global level.

molecular biology↗

bioRxiv2022

Prediction Of The Impact Of Genetic Variability On Drug Sensitivity For Clinically Relevant EGFR Mutations

Mutations in the kinase domain of the Epidermal Growth Factor Receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients under chemotherapy treatment based on kinase inhibitors use. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and to design new drugs effective against resistant variants. To this end, we have explored a variety of in silico methods, from sequence-based to state-of-the-art atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears and what will be the impact in drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to produce alterations in drug activity and can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on non-equilibrium alchemical free energy calculations show predictive power. The integration of these state-of-the-art methods in a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

biochemistry↗

bioRxiv2021

Enabling the execution of large scale workflows for molecular dynamics simulations

The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones. In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends. Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercomputer facility.

molecular biology↗

bioRxiv2019

MuGVRE. A virtual research environment for 3D/4D genomics

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available at https://vre.multiscalegenomics.eu and documentation and general information at https://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

bioinformatics↗