Sebastian Pechmann's ORCID logo (https://orcid.org/0000-0002-4356-9470) research laboratory.
Bienvenue! Welcome!

Updated on 3 Sept 2022

We are a multi-disciplinary research laboratory with unquenchable interest and broad expertise in computational biology, protein homeostasis, systems engineering, neuroscience, and evolution. The overarching focus of our research centers on modeling protein homeostasis in health, aging, and neurodegeneration.

We develop and apply computational and systems biology methodology to discover and dissect - across scales from the sequence to the network level - principles of successful protein homeostasis in health, and causes for failure or dysregulation of protein homeostasis in neurodegenerative diseases linked to protein misfolding.

Sebastian trained in engineering, computation, and biology at ETH, the University of Cambridge, and Stanford. He is always most excited to share his enthusiasm for all things proteins and biological systems. Get in touch!


Recent highlights:
  • New paper: "Functional network motifs defined through integration of protein-protein and genetic interactions" - now published in PeerJ !

  • Our 3 recent papers appeared in Structure, Nucl Acids Res, and FEBS J !

  • Sebastian presents on the computational constraint-based modeling of eukaryotic protein homeostasis at the PRinCE Canada Proteostasis Symposium in Toronto



Research: A systems view of protein homeostasis

We study how cells maintain protein homeostasis through the integrated regulation of protein synthesis, folding, trafficking, and degradation pathways. Failure of protein homeostasis is linked to severe so-called “protein misfolding diseases” that include Alzheimer’s and Parkinson’s. A decline in protein homeostasis is associated with aging, and dysregulation of the protein homeostasis network is a common hallmark of tumorigenesis in all cancers.

Computational models that integrate mechanistic biochemical knowledge with large-scale systems biology datasets have proven tremendously fruitful for understanding complex cellular processes. We develop and apply computational and systems biology methodology to discover and dissect - across scales from the sequence to the network level - principles of successful protein homeostasis in health, and causes for failure or dysregulation of protein homeostasis in diseases.

From high-throughput to mechanistic insight

We apply computational and evolutionary approaches to extract novel biological insight and directly testable mechanistic hypotheses from complex genomic data. Of particular interest are the discovery and characterisation of regulatory principles that support proteome integrity. Current projects focus on mechanisms of transcription and translation regulation as well as regulatory functions of targeted protein quality control in maintaining protein homeostasis.

Reconstructing cellular networks from genomic data

We develop novel computational tools for the reconstruction, analysis, and constraints-based modeling of cellular networks. Through targeted perturbation and experimental validation we then aim to refine quantitative models of protein homeostasis systems. Next to providing innovative strategies for harnessing the power of genomic data, we anticipate these efforts to reveal fundamental insights into the functioning and organisation of the protein homeostasis network and its response to perturbations.

Targeting protein misfolding diseases

By applying our gained knowledge and developed framework to paradigms of ageing and neurodegeneration, we seek to discover novel data-driven strategies to detect and target protein misfolding diseases. We are particularly interested in identifying re-wiring events in the protein homeostasis network that may serve as biomarkers for the early onset of pathologies linked to protein homeostasis failure, as well as understand how re-direction of fluxes through the protein homeostasis network may allow the rational re-engineering of the underlying protein quality control systems for therapeutic intervention.



Publications

  • A Sahoo, S Pechmann. Functional network motifs defined through integration of protein-protein and genetic interactions PeerJ 10, e13016 (2022)        
  • P do Couto Bordignon, S Pechmann. Inferring translational heterogeneity from Saccharomyces cerevisiae ribosome profiling. FEBS J 288, 4541-4559 (2021)        
  • LC Aguilar, B Paul, T Reiter, L Gendron, AAN Rajan, R Montpetit, C Trahan, S Pechmann, M Oeffinger, B Montpetit. Altered rRNA processing disrupts nuclear RNA homeostasis via competition for the poly(A)-binding protein Nab2 Nucleic Acids Res 48, 11675-11694 (2020)      
  • S Pechmann. Programmed trade-offs in protein folding networks. Structure 28, 1361-1375 (2020)        
  • ABMSU Hasan, H Kurata, S Pechmann. Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation. BMC Bioinformatics 20, 734 (2019)        
  • Y Draceni, S Pechmann. Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis. Proc Natl Acad Sci USA 116, 20009-20014 (2019)        
  • S Pechmann. Coping with stress by regulating tRNAs. Sci Signal 11, eaau1098 (2018)    
  • R Geller, S Pechmann, A Acevedo, R Andino, J Frydman. Hsp90 dictates viral sequence space by balancing the evolutionary tradeoffs between protein stability, aggregation and translation rate. Nature Communications 9, 1781 (2018)          
  • S Pechmann, JW Chartron, J Frydman. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat Struct Mol Biol 21, 1100-5 (2014)        
  • S Pechmann, J Frydman. Interplay between chaperones and protein disorder promotes the evolution of protein networks. PLoS Comput Biol 10, e1003674 (2014)          
  • S Duttler, S Pechmann, J Frydman. Principles of cotranslational ubiquitination and quality control at the ribosome. Mol Cell 50, 379-93 (2013)        
  • S Pechmann, F Willmund, J Frydman. The ribosome as a hub for protein quality control. Mol Cell 49, 411-21 (2013)      
  • F Willmund, M del Alamo, S Pechmann, T Chen, V Albanèse, EB Dammer, J Peng, J Frydman. The cotranslational function of ribosome-associated Hsp70 in eukaryotic protein homeostasis. Cell 152, 196-209 (2013)      
  • S Pechmann, J Frydman. Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat Struct Mol Biol 20, 237-43 (2013)          
  • A Leitner, LA Joachimiak, A Bracher, L Mönkemeyer, T Walzthoeni, B Chen, S Pechmann, S Holmes, Y Cong, B Ma, S Ludtke, W Chiu, FU Hartl, R Aebersold, J Frydman. The molecular architecture of the eukaryotic chaperonin TRiC/CCT. Structure 20, 814-25 (2012)      
  • M del Alamo, DJ Hogan, S Pechmann, V Albanese, PO Brown, J Frydman. Defining the specificity of cotranslationally acting chaperones by systematic analysis of mRNAs associated with ribosome-nascent chain complexes. PLoS Biol 9, e1001100 (2011)      
  • S Pechmann, M Vendruscolo. Derivation of a solubility condition for proteins from an analysis of the competition between folding and aggregation. Mol Biosyst 6, 2490-7 (2010)      
  • S Pechmann, ED Levy, GG Tartaglia, M Vendruscolo. Physicochemical principles that regulate the competition between functional and dysfunctional association of proteins. Proc Natl Acad Sci USA 106, 10159-64 (2009)      
  • GG Tartaglia, S Pechmann, CM Dobson, M Vendruscolo. A relationship between mRNA expression levels and protein solubility in E. coli. J Mol Biol 388, 381-9 (2009)      
  • S Pechmann, ED Levy, GG Tartaglia, M Vendruscolo. Competition between protein aggregation and protein complex formation. BMC Bioinformatics 9, O2 (2008)    
  • M Nishi, J Kobayashi, S Pechmann, M Yamato, Y Akiyama, A Kikuchi, K Uchida, M Textor, H Yajima, T Okano. The use of biotin-avidin binding to facilitate biomodification of thermoresponsive culture surfaces. Biomaterials 28, 5471-6 (2007)      
  • GG Tartaglia, S Pechmann, CM Dobson, M Vendruscolo. Life on the edge: a link between gene expression levels and aggregation rates of human proteins. Trends Biochem Sci 32, 204-6 (2007)      
  • S Lee, M Müller, R Heeb, S Zürcher, S Tosatti, M Heinrich, F Amstad, S Pechmann, ND Spencer. Self-healing behavior of a polyelectrolyte-based lubricant additive for aqueous lubrication of oxide materials. Tribology Letters 24, 217-223 (2006)