What are GPCR’s and why are they important?
Mentored by Peter Stadler, Sayan Mukherjee
at Interdisciplinary Centre for Bioinformatics, Leipzig University
G protein-coupled receptors (GPCR’s), are the largest subgroup of cell receptors in humans. They also are one of the largest subgroups of receptors, that interact with pharmaceutics. GPCR’s are part of many diﬀerent signal cascades. A ligand molecule is binding on the extracellular side of the cell to the receptor. This binding induces a conformation change of the receptor. Because of this, a special G protein can bind to the intracellular side of the receptor. This way, signals are transmitted from the outside of a cell to the inside of the cell. Modiﬁcations in the process of conformational changes have a major inﬂuence on the signalling cascade. Drugs can use this behavior. The drugs bind as ligands at the extracellular side, change the conformation and with this also the signal cascade. This can result in an interruption of the cascade, but can also increase the signals and thus induce a certain behavior in the cell. [1, 2]
Understanding the conformational changes of the receptors and the interaction behavior with extracellular ligands, is a promising task. It can help, to understand better, how signal cascades are working at the cell interface and how drugs can inﬂuence this behavior. It is probably an essential task in the ﬁeld of drug discovery. But this is not only important for the development of new drugs. It was found for example, that GPCR’s are also involved in the post-COVID syndrome. It was found, that antibodies inﬂuence the behavior of GPCR’s. The change in the signal cascades results in the common symptoms. 
This shows that understanding the conformational change of GPCRs is also crucial for medicine in general and could lead to the improvement of millions of lives.
PhD Topic - What is the Task?On the base of structural data from cryo-EM, MD simulations should be done. These simulating the conformational changes of the receptors. One big question is, if one can ﬁnd stable conformations of the receptor, which are the result of the binding of a certain ligand. The problem is that a few microseconds of simulation lead to a huge amount of data. This makes it hard, to interpret them. Another problem is, that speciﬁc structures, found for example with MSM’s, deliver some stable structures. But whether this structure inhibits or promotes a certain behaviour is not necessarily a given. Here Machine Learning can be helpful. Modern methods of artiﬁcial intelligence could be used to face some of the following questions in the context of a PhD-project:
- How can the data of MD-simulations be prepared and represented, to ﬁnd useful information in them?
- How can Machine Learning be used, to ﬁnd stable structures in MD-data of GPCR’s and observe, which binding behavior leads to which structural changes?
- Can Machine Learning be used, to analyse structural changes and classify them in speciﬁc categories? For example, a speciﬁc binding leads to a blockage of the receptor.
- How can Machine Learning be used to improve the analysing pipelines of MD-simulations in general?
- How can Machine Learning be used, to improve the accuracy of MD-simulations directly, for example by ﬁnding better force ﬁelds?
- How can Machine Learning be used, to face biased data or select unimportant dynamics.
The questions are not ﬁxed. Science is a dynamic ﬁeld, same as our own biological system. The main idea is, to use modern methods of machine learning and artiﬁcial intelligence, to improve the understanding of GPCR’s and their dynamic behavior. The base of the models should be data obtained by MD-simulations. You can ﬁnd more inspiration in the reference list at the end.
You will work at the Interdisciplinary Center for Bioinformatics in Leipzig and cooperate with the machine learning and AI experts at the scaDS.AI in Leipzig and Dresden. You will have access to machine-learning HPC resources of the ScaDS.AI Center for Scalable Data Analytics and AI Dresden/Leipzig, offering state-of-the-art CPU (Intel and IBM POWER 9) and GPU (Nvidia A100 and V100) resources, as well as high-performance computing and storage systems for computational experiments.
- Master’s Degree in Bioinformatics, Computational Sciences, applied mathematics or related ﬁelds of expertise
- Excellent programming skills (e.g. C/C++, Python)
- Excellent skills and experience in one or more of the following research areas are beneﬁcial:
- Machine Learning
- Markov Models
- Structural Biology
- Protein-Ligand Interactions
- Drug Design
- Very good language skills in English
- Ability to work in an highly dynamic and interdisciplinary ﬁeld and working environment
- The will to learn new things and to develop further
 Naomi R. Latorraca, A. J. Venkatakrishnan, and Ron O. Dror. “GPCR Dynamics: Structures in Motion”. In: Chemical Reviews 117.1 (2017). PMID: 27622975, pp. 139–155. DOI: 0.1021/acs.chemrev.6b00177. eprint: https://doi.org/10.1021/acs.chemrev.6b00177. URL: https://doi.org/10.1021/acs.chemrev.6b00177.
 Christofer Tautermann, Daniel Seeliger, and Jan Kriegl. “What can we learn from molecular dynamics simulations for GPCR drug design?” In: Computational and Structural Biotechnology Journal 13 (Dec. 2015), pp. 111–121. DOI: 10.1016/j.csbj.2014.12.002.
 Sanisha Das and Suresh Kumar. “Long COVID: G Protein-Coupled Receptors (GPCRs) responsible for persistent post-COVID symptoms”. In: bioRxiv (2022). DOI: 10.1101/2022.12.12.520110. eprint: https://www.biorxiv.org/content/early/2022/12/13/2022.12.12.520110.full.pdf. URL: https://www.biorxiv.org/content/early/2022/12/13/2022.12.12.520110.
 Yihang Wang, Joao Marcelo Lamim Ribeiro, and Pratyush Tiwary. “Machine learning approaches for analyzing and enhancing molecular dynamics simulations”. In: Current opinion in structural biology 61 (2020), pp. 139–145.
 Adria Pérez, Gerard Martınez-Rosell, and Gianni De Fabritiis. “Simulations meet machine learning in structural biology”. In: Current opinion in structural biology 49 (2018), pp. 139–144.
 Frank Noé et al. “Machine learning for molecular simulation”. In: Annual review of physical chemistry 71 (2020), pp. 361–390.