EventsMOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published
This submission belongs to the session 01. CHEMBIOINFO-03: Chem-Bioinformatics Congress Cambridge, UK-Chapel Hill and Richmond, USA, 2017 of the event MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published date
15 Dec, 2017
Citation
José G. Almeida, Alexandre M. J. J. Bonvin, Irina S. Moreira, Using big-data to understand the protein interface landscape, in Proceedings of MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed., 15 January–15 December 2017, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-03-05087
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Using big-data to understand the protein interface landscape

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1. CNC - Center for Neuroscience and Cell Biology; Rua Larga, FMUC, Polo I, 1ºandar, Universidade de Coimbra, 3004-517, Coimbra, Portugal.
2. Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, 3584CH, the Netherlands
Abstract

Protein-protein interactions (PPIs) are the foundation of basic organism functions and understanding them is key in determining the importance of different proteins in a wide array of complex networks and processes [1]. The variety underlying PPIs is immense and some residues are more essential in interface stabilization than others [2]. Such is the case of hot-spots (HS), residues whose mutation to alanine is detrimental for the stability of the PPI, as opposed to null-spots (NS), which constitute the remaining interfacial residues [3]. Considering the complex landscape in protein interfaces, some patterns and characteristics arise when a high amount of data is considered, by minimizing the effect of less prevalent interactions and characteristics. In this work, the SpotOn pipeline [4] - developed by our group - custom scripts and conservation servers were used to determine structural features of interfacial residues and to classify them as HS and NS in the PPI4DOCK database [5], comprising over 1400 non-redundant complexes. This study allowed us to further understand the structural differences between HS and NS and will be available in a web-server in the near future.

References

  1. Jones, S. and J.M. Thornton, Principles of protein-protein interactions. Proc Natl Acad Sci USA, 1996. 93.
  2. Stites, W.E., Protein-Protein Interactions: Interface Structure, Binding Thermodynamics, and Mutational Analysis. Chem Rev, 1997. 97(5): p. 1233-1250.
  3. Bogan, A.A. and K.S. Thorn, Anatomy of hot spots in protein interfaces. J Mol Biol, 1998. 280(1): p. 1-9.
  4. Moreira, I.S., et al., SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots. Scientific Reports, 2017. 7(1): p. 8007.
  5. Yu, J. and R. Guerois, PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets. Bioinformatics, 2016. 32(24): p. 3760-3767.
Keywords
big-data
protein interfaces
hot-spots
Poster
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