Project Z04-INF – Data management and computational models of structure and dynamics, and evolution of GPCRs

Z04-INF will support the CRC by providing a comprehensive computational analysis of the receptors and their interacting partners, and will also cover the necessary aspects of data management. In an iterative loop, we will build computational models consistent with experimental data, combining Rosetta modeling, molecular dynamics simulation, allosteric network analysis, and evolutionary analysis. From these models, we will derive testable hypothesis for the next iteration of experimental validation in the respective projects. In addition, a dedicated INF component will be added, focusing on metadata collection and the creation of a non-invasive platform for data exchange.


Prof. Dr. Peter Stadler (Project Leader)

Leipzig University, Faculty of Mathematics and Computer Science
Institute of Computer Science
Härtelstraße 16 – 18, D-04107 Leipzig

Phone +49 341 97 16690

Franziska Reinhardt (PhD Student)

Leipzig University, Faculty of Mathematics and Computer Science
Institute of Computer Science
Härtelstraße 16 – 18, D-04107 Leipzig


We focus on: 

  1. Structural intermediates, complexes
  2. Combining MD simulations with evolutionary analysis
  3. Performing MD simulations of the membrane-embedded proteins
  4. Usage of paramagnetic restranst in structre determination


  • computational models
  • MD Simulations: Classical Molecular Dynamics, QM/MM, QMD, DFT, Enhanced Sampling, AMBER, Gromacs, VMD
  • evolutionary analysis


Klemm P, Stadler PF, Lechner M. Proteinortho6: pseudo-reciprocal best alignment heuristic for graph-based detection of (co-)orthologs. Front Bioinform. 2023 Dec 13;3:1322477.

Sala D, Batebi H, Ledwitch K, Hildebrand PW, Meiler J. Targeting in silico GPCR conformations with ultra-large library screening for hit discovery. Trends Pharmacol Sci. 2023; 44:150-61.

Sala D, Hildebrand PW, Meiler J. Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties. Front Mol Biosci. 2023; 10:1121962.

Schmidt P, Vogel A, Schwarze B, Seufert F, Licha K, Wycisk V, Kilian W, Hildebrand PW, Mitschang L. Towards Probing Conformational States of Y2 Re-ceptor Using Hyperpolarized 129Xe NMR. Molecules. 2023; 28:1424

Staritzbichler R, Ristic N, Stapke T, Hildebrand P. SmoothT – a server constructing low energy pathways from conformational ensembles for interactive visualization and enhanced sampling. Bioinformatics. 2023 Apr 5:btad176.

Canzler S, Fischer M, Ulbricht D, Ristic N, Hildebrand PW, Staritzbichler R. ProteinPrompt: a webserver for predicting protein–protein interactions. Bioinformatics Advances. 2022, 2:1 vbac059.
Del Alamo D, Sala D, Mchaourab HS, Meiler J. Sampling alternative conformational states of transporters and receptors with AlphaFold2. Int J Mol Sci. 2022 Sep 8;23(18):10405. doi: 10.3390/ijms231810405.
Sala D, Del Alamo D, Mchaourab HS, Meiler J. Modeling of protein conformational changes with Rosetta guided by limited experimental data. Structure. 2022 May 12:S0969-2126(22)00140-X. doi: 10.1016/j.str.2022.04.013. Online ahead of print. PMID: 35597243.

Schaller D, Lafond M, Stadler PF, Wieseke N, Hellmuth M. Indirect identification of horizontal gene transfer. Journal of Mathematical
2021; 83:10.

Kasmanas JC. Bartholomäus A, Corrêa FB, Tal T, Jehmlich N, Herberth Gunda, von Bergen M, Stadler PF, de Carvalho ACPLF, da Rocha UN. HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes. Nucleic Acids Res. 2021; 49:D743-D750.

Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun. 2021; 12:6947.

Nunn A, Can SN, Otto C, Fasold M, Diez Rodrigues B, Fernandez-Pozo N,Rensing SA, Stadler PF, Langenberger D. EpiDiverse Toolkit: a pipeline suite for the analysis of bisulfite sequencing data in ecological plant epigenetics. Nucleic Acids Res. Genom Bioinf. 2021; 3:lqab106.

Rudolf S, Kaempf K, Vu O, Meiler JBeck-Sickinger AGCoin I. Binding of Natural Peptide Ligands to the Neuropeptide Y5 Receptor. Angew Chem Int Ed Engl. 2021 Nov 25. doi: 10.1002/anie.202108738 . PMID:34822209. 

Staritzbichler R, Ristic N, Goede A, Preissner R, Hildebrand PW. Voronoia 4-ever. Nucleic Acids Res. 2021 Jun 9:gkab466. doi: 10.1093/nar/gkab466. Epub ahead of print. PMID: 34107038.

Koehler Leman J, Weitzner BD, Renfrew PD, Lewis SM, Moretti R, Watkins AM, Mulligan VK, Lyskov S, Adolf-Bryfogle J, Labonte JW, Krys J; RosettaCommons Consortium, Bystroff C, Schief W, Gront D, Schueler-Furman O, Baker D, Bradley P, Dunbrack R, Kortemme T, Leaver-Fay A, Strauss CEM, Meiler J, Kuhlman B, Gray JJ, Bonneau R. Better together: Elements of successful scientific software development in a distributed collaborative community. PLoS computational biology. 2020; 16:e1007507.

Lem Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó’Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RY, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nature methods. 2020; 17:665-80.