In Silico and in Vitro Investigation of Non-Conserved Interaction Characteristics


Principal Investigator: David A Liberles
Abstract: DESCRIPTION (provided by applicant): Compared to other areas of computational annotation and prediction, it is very difficult to predict reliably what protein-protein interactions (PPI) an individual protein engages in (e.g. in disease development) and what the characteristics of the interaction are. This is often the case even if knowledge of PPI is available from homologous proteins;while the inference seems true more often than not, instances of differing behavior of homologous proteins in model organisms have often led to surprises when inferences to humans turned out invalid (e.g. in the case of leptin (Gaucher et al 2003)). There are indications both from theory (grounding in redundancy and robustness of pathways) and practice that some of these instances may be reflected in detectable signal of adaptive (or positive) selection pressure. In this project we will systematically screen chordate protein families using their protein sequences;the encoding gene sequences;and modelled structures of hypothetical PPI observed between homologous partners. We will use previously developed data bases and software developed in our groups, most importantly The Adaptive Evolution Database TAED (Liberles et al 2001) and the Binary SubComplex Database BISC (Juettemann &Gerloff 2011). Signals of evolutionary adaptation will be sought in the protein surface properties and the encoding genes, in context of the 3D-structure of the target proteins. Specifically we will screen for gene families where we find indications for possible changes among orthologs to human proteins closely related species, and closely similar paralogous domains e.g. in surface receptors. Using high-end computational biology methods we will produce refined complex models and attempt to validate the predicted not-conserved PPI computationally. Due to currently largely lacking benchmarks we will also validate a subset in the laboratory and further characterize the nature of the change in PPI (e.g. whether a new interaction has evolved;binding specificity has changed, or an the interaction has been lost altogether). Our approach integrates (data-driven) discovery and (hypothesis-driven) validation of examples of evolutionary change within protein families. We surmise that (i) non-conserved/individual PPI characteristics are more common than is currently assumed in PPI prediction and annotation, even among orthologs;and that (ii) if we can devise strategies to identify instances that challenge the paradigm, this indicates an angle for further improving PPI prediction for individual proteins. Perhaps most excitingly if we are correct, this should be considered in the modelling and analysis of PPI networks, ultimately for applications in personalized medicine and the design of potentially interaction-disrupting drugs. Similarly important this approach may help flag instances where model organism-based inferences to human diseases are unlikely to hold true.
Funding Period: 2012-09-01 - 2014-08-31
more information: NIH RePORT

Top Publications

  1. pmc MaGnET: Malaria Genome Exploration Tool
    Joanna L Sharman
    The University BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK
    Bioinformatics 29:2350-2. 2013

Research Grants

Detail Information


  1. pmc MaGnET: Malaria Genome Exploration Tool
    Joanna L Sharman
    The University BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK
    Bioinformatics 29:2350-2. 2013
    ..Any selection of genes to explore made by the user is easily carried over between the different viewers for different datasets, and can be changed interactively at any point (without returning to a search)...

Research Grants31

  1. JCIMPT-Complexes
    Raymond C Stevens; Fiscal Year: 2013
    ..These proteins (e.g. human G-protein coupled receptors) are the target of the majority of therapeutic drug targets. There is a critical need to develop breakthrough technologies to enable better characterization of this protein family. ..
  2. Center for Structure of Membrane Proteins
    Robert M Stroud; Fiscal Year: 2013
    ..3.1, one of the world's most productive protein crystallography facilities. Overall, the combined expertise of principal investigators provides a unique environment to achieve the proposed aims. ..
  3. The Virtual Physiological Rat Project
    Daniel A Beard; Fiscal Year: 2013
    ..This proposal targets the grand challenge of understanding complex multi-faceted disease phenotypes through experiments and simulations that capture the complex genotype-environment-phenotype relationship. ..
  4. Center for the Spatiotemporal Modeling of Cell Signaling (STMC)
    Bridget S Wilson; Fiscal Year: 2013
    ..The Center will strongly support translation of new technical and computational tools to other signaling systems linked to human disease, especially other immune diseases and cancer. ..
  5. Improving Cancer Outcomes for Underserved Populations in SE North Carolina
    PATRICK DAVID MAGUIRE; Fiscal Year: 2013
    ..abstract_text> ..
  6. The Transmembrane Protein Center
    Brian G Fox; Fiscal Year: 2013
    ..Expanded options for crystallization screening, access to synchrotrons, and improvements in software used to solve structures will also contribute to the increased throughput necessary to achieve PSI:Biology goals. ..
  7. Deconstructing and Reconstructing the T Cell Signaling Network
    Arthur Weiss; Fiscal Year: 2013
    ..We will use modeling to compare simple to more complex systems but also study how these simple systems deviate from those obtainable in solution or in more complex cellular systems. ..
  8. Oklahoma Center for Respiratory and Infectious Diseases
    Lin Liu; Fiscal Year: 2013
    ..The completion of the goals of the present COBRE will have a major impact on research programs on respiratory infectious diseases in the State of Oklahoma. ..
  9. Molecular Analyses and Interventions for Biodefense and Emerging Pathogens
    Olaf Schneewind; Fiscal Year: 2013
    ..Research and training at the GLRCE is governed by a mechanism involving ongoing review of scientific excellence and translational goals, inter-institutional advisory boards and external scientific advisory bodies. ..
  10. PAHs: New Technologies and Emerging Health Risks
    David E Williams; Fiscal Year: 2013
    ..Accomplishing these goals will provide significant scientific advancement and improve the quality of life for impacted communities. ..
  11. IkB/NF-kB Recognition In Silico, In Vitro and In Vivo
    Elizabeth A Komives; Fiscal Year: 2013
  12. New Computational Methods for Data-driven Protein Structure Prediction
    Jinbo Xu; Fiscal Year: 2013
    ..Protein modeling is also widely applied in the pharmaceutical industry and integrated into most stages of pharmaceutical research. ..
  13. National Biomedical EPR Center
    James S Hyde; Fiscal Year: 2013
    ..The NIH RePORTER database reveals 124 R0I research grants that use EPR. We serve the holders of these grants to enhance the nation's biomedical research. Progress in the current funding period includes 220 papers. ..
    Kenneth H Cowan; Fiscal Year: 2013
  15. Human Genome Sructural Variation
    Evan Eichler; Fiscal Year: 2013
    ..abstract_text> ..
  16. Computational methods for structural-functional studies of proteins
    Nick V Grishin; Fiscal Year: 2013
    ..We will improve alignment accuracy and using the new method will analyze kinases, which are a medically important group of enzymes attracting high interest because of their relevance to many diseases, cancer in particular. ..
    Sudhir Kumar; Fiscal Year: 2013
    ..As always, MEGA and its source code will be made available free of charge for all uses, including research, education, and training. ..