8th and Final ERNEST Meeting to Take Place in Crete with Participation from GPCR Drug Discovery Group

The 8th and final meeting of the European Research Network on Signal Transduction (ERNEST) is set to take place from May 3-7, 2023 in Crete. This event promises to be an exciting gathering of scientists from around the world who are interested in the latest developments in signal transduction research. Among the attendees will be several members of the G protein-coupled receptor (GPCR) Drug Discovery Group from the GRIB in Barcelona...Show all

The 8th and final meeting of the European Research Network on Signal Transduction (ERNEST) is set to take place from May 3-7, 2023 in Crete. This event promises to be an exciting gathering of scientists from around the world who are interested in the latest developments in signal transduction research. Among the attendees will be several members of the G protein-coupled receptor (GPCR) Drug Discovery Group from the GRIB in Barcelona.

ERNEST is a collaborative network of European research institutions that was established to promote scientific excellence and cooperation in the field of signal transduction. The network has been in operation for eight years, during which time it has fostered numerous successful collaborations and research projects. The final meeting of ERNEST promises to be a fitting conclusion to this productive period, and will feature presentations from some of the leading lights in the field of signal transduction research. Hide

In a groundbreaking new publication, researchers have unveiled the latest advancements in the field of protein folding. The publication, titled "Alpha Fold: a solution to a 50-year-old grand challenge in biology," details the creation of a powerful algorithm that can predict the three-dimensional structures of proteins with unprecedented accuracy. This breakthrough has the potential to revolutionize drug discovery, as it provides a more efficient way to design and develop new drugs that can target specific proteins...Show all

In a groundbreaking new publication, researchers have unveiled the latest advancements in the field of protein folding. The publication, titled "Alpha Fold: a solution to a 50-year-old grand challenge in biology," details the creation of a powerful algorithm that can predict the three-dimensional structures of proteins with unprecedented accuracy. This breakthrough has the potential to revolutionize drug discovery, as it provides a more efficient way to design and develop new drugs that can target specific proteins.

The research was carried out by a team at the University of Cambridge, led by Dr. Demis Hassabis, co-founder of DeepMind, and Dr. John Jumper. The team used artificial intelligence to analyze the genetic code of proteins and predict their structure. This is a significant development because, until now, scientists have struggled to determine the structure of proteins, which has limited their ability to understand how they function and develop drugs that can target them.

The publication has already generated a great deal of excitement in the scientific community, with many experts hailing it as a major breakthrough in the field. The ability to predict protein structures with such accuracy has the potential to revolutionize drug discovery and lead to the development of more effective treatments for a range of diseases. The research has been widely praised for its innovative use of AI and for the significant advances it represents in our understanding of protein folding. Hide

Second simulation round

After our first successful publication in Nature Methods, we are back to increase the data coverage of GPCRmd in a 2nd simulation round...Show all

After our first successful publication in Nature Methods, we are back to increase the data coverage of GPCRmd in a 2nd simulation round.

In this round, we will focus on simulating all remaining PDB IDs which correspond to GPCRs which are not in complex with G proteins, arrestins, or nanobodies. Note that GPCR-G protein/arrestin/nanobody complexes will be addressed in the 3rd simulation round starting in January 2021. Hide

Nature publication: GPCRmd uncovers the dynamics of the 3D-GPCRom

A groundbreaking paper titled "GPCRmd uncovers the dynamics of the 3D-GPCRome" has just been published, offering new insights into the dynamics of G protein-coupled receptors (GPCRs), a class of proteins that play a crucial role in signaling pathways. The paper details the findings of the GPCRmd project, a collaborative effort between several research institutions to create a comprehensive database of GPCR molecular dynamics simulations...Show all

A groundbreaking paper titled "GPCRmd uncovers the dynamics of the 3D-GPCRome" has just been published, offering new insights into the dynamics of G protein-coupled receptors (GPCRs), a class of proteins that play a crucial role in signaling pathways. The paper details the findings of the GPCRmd project, a collaborative effort between several research institutions to create a comprehensive database of GPCR molecular dynamics simulations.

The GPCRmd project used state-of-the-art molecular dynamics simulations to explore the behavior of GPCRs in 3D space. The project was led by researchers from the Institute of Institut Hospital del Mar d'Investigacions Mèdiques (IMIM) and the University Pompeu Fabra of Barcelona, in collaboration with several other institutions. The resulting database provides a wealth of information about the behavior of GPCRs, including their interactions with ligands and their structural changes over time.

The publication of this paper is a significant milestone in the field of GPCR research, offering new insights into the dynamics of these important proteins. The findings of the GPCRmd project have the potential to drive the development of new drugs that target GPCRs, which are involved in a wide range of physiological processes. The database created by the project is freely available in our website to the scientific community, providing a valuable resource for future research into GPCR dynamics and signaling pathways. Hide