Center for Computational Biology Events

Glycans, complex sugars covalently attached to proteins, play crucial roles in protein stability and function, participate in ""self"" recognition, and modulate protein-protein interactions.Unlike proteins, glycans typically do not form secondary structures and remain highly mobile, posing challenges for traditional structural biology techniques. This high mobility, coupled with glycan heterogeneity, complicates the elucidation of complete glycoprotein structures and hinders research on the role of glycans in protein function. Although computer simulations can help address these challenges, they often require millions of hours on specialized supercomputers, highlighting the need for approximate methods that can beintegrated into protein-solving pipelines. To overcome these obstacles, we developed a simplified, open-source method for rapidly predicting the span and shape of glycans with minimal computing power. Using this approach, we can accurately predict SARS-CoV-2 spike protein epitopes that are not shielded by glycans, as well as assess the impact of glycans on protein flexibility. We also demonstrate that this method can be integrated with structural biology methods and glycoproteomics pipelines, offering a comprehensive tool for glycoprotein analysis.

This two-day workshop aims to unite researchers with a specific focus on studying cellular processes by leveraging omics datasets. The participants will bring expertise from diverse fields such as statistical inference, optimal transport, statistical and biological physics, and machine learning. The objective of the gathering is to foster the exchange of ideas across various disciplines and perspectives, aiming to develop interpretable biophysical models that can be tested against high-throughput datasets.

This two-day workshop aims to unite researchers with a specific focus on studying cellular processes by leveraging omics datasets. The participants will bring expertise from diverse fields such as statistical inference, optimal transport, statistical and biological physics, and machine learning. The objective of the gathering is to foster the exchange of ideas across various disciplines and perspectives, aiming to develop interpretable biophysical models that can be tested against high-throughput datasets.