Editorial Article

Acquisition of Complete Information in the Analysis of Protein-Protein Interactions

Tim Xing 1,* , Xiu-Qing Li 2,*
1Department of Biology and Institute of Biochemistry, Carleton University, Ottawa, ON,Canada
2Fredericton Research and Development Centre, Agriculture and Agri-Food Canada, Fredericton, NB, Canada

*Corresponding author:

Tim Xing, Department of Biology and Institute of Biochemistry, Carleton University, Ottawa, ON,Canada, Email: tim.xing@carleton.ca
Xiu-Qing Li, Fredericton Research and Development Centre, Agriculture and Agri-Food Canada, Fredericton, NB, Canada, Email: xiu-qing.li@agr.gc.ca

Proteins and their intricate network of interactions are the mainstay of any cellular process. Historically, yeast two-hybrid (Y2H), bimolecular fluorescence complementation (BiFC) assays and bioluminescence resonance energy transfer (BRET) greatly contributed to our understanding of biomolecule interactions in early days. With technology development and the massive influx of information in human, medical, and model organism studies, there is a growing need in the understanding of how the proteins are put together and why they are put together in the way that they are. Such a desire to make it biologically coherent has forced us to think not in terms of two, three, four or more proteins but in terms of ‘protein systems’ or protein network in a real-time cellular context.

In such a cellular context, one could assume that this protein system is arranged in a hierarchy of increasing organizational complexity. In this sense, dissection of molecular interactions at atomic detail is invaluable, as this paves the route to a mechanistic understanding of biological function. Some other quantitative measures are also fundamental, e.g. rate of binding and binding affinity. We have to know the driving forces of intermolecular interactions and specificity of intermolecular interactions.

Challenge comes when moving from atomic level to the structural dimension. It requires us to resort to a combination of various experimental and computational methods. Together with structural information and structural models of protein complexes, it is also critical to obtain information on the dynamics, stoichiometry, thermodynamics and kinetic parameters of the biologically relevant assemblies.

While structural biology help us with 3-dimensional details of molecular machines and bridging biology, chemistry and physics, we are still far from a complete understanding of how these molecules work and are regulated. It is apparent that experimental techniques alone would not suffice to provide such information and that theoretical approaches will play an instrumental role. In the study of protein kinases, not only atomistic simulation methods can explore dynamics and conformational transition as key mechanisms in kinase function, but also theoretical methods can capture the effects of single point mutations on the wild-type dynamics of proteins leading to disorders. Keep in mind that a single mutation, post-translational modifications, environmental factors or conformational dynamics may all play governing roles in the selectivity of interactions. Such disordered or modified regions may lead to the alternation of structured and unstructured regions and the possible poly disperse nature of the assembly. The integrated approaches provide an important information on proteins of clinical interest and help the design of allosterically targeted drugs.

Next, how they are put into work. It is time to consider the communication and control within biological systems. Biological systems, like all systems, are composed of networks of interdependent components that integrate the system into a unified whole. Linkages are demonstrated by modifying the level of one component and observing the communicated effects on others. The strength (sensitivity) of the linkage can be ascertained by measuring the extent of the response. Communicated information in cells and organisms is usually context-dependent and user-specific. The various forms of communication that operate within the hierarchy of a system are therefore essential to understanding overall systems behavior. In addition to all the above, various types of external and internal inputs produce time-dependent changes in molecular concentrations, association states, cellular localization or activity, that critically affect the state of the cell or the organism.

At the very basic level, one could do live-cell imaging, involving genetically encoded biosensors based on fluorescent proteins (FP), for probing the dynamic properties of signalling assemblies within individual living cells. Fluorescence Resonance Energy Transfer (FRET) and BRET techniques have allowed us to monitor the timing of receptor clustering events, or to probe the dynamics of local interactions between components of signaling complexes. Considering the short-range and long-range interactions in which multi-protein signalling modules engage, allostery is definitely at the heart of almost every signalling and regulatory process, and the understanding of the fundamental mechanisms that underlie allosteric regulation is key to the analysis of multi-component signalling networks. Computational approaches are applied to the elucidation of allosteric mechanisms in biological systems. We should learn more on how to extract the gained knowledge for the engineering of regulatory modules.

Increasingly, we are going to have to depend on computational biologists to construct models that can then be tested back in laboratory conditions. Computational modeling has become a routine just as the centrifuge, SDS-PAGE, and other laboratory tools in research programs. Understanding of the macromolecular machines is at the heart of understanding living cells. Our ability has advanced on every front over the past a few decades. Higher spatial resolution, larger macromolecular complexes, less symmetry-constrained approaches, and computational integration of multiple types of data continue to lead us to more insightful questions and answers that will eventually give us an even more complete picture about protein-protein interaction. We should always keep in mind that we are studying a living and changing biological system and understanding the complexity of biological systems represents the greatest intellectual and experimental challenge yet faced by any biologist. The ultimate goal of understanding a protein network is to generate a model of the cell, tissue, and organism as a whole that describes processes across all layers of underlying biological activities (molecular, biochemical, cellular, physiological, and organismal). Such an approach embodies an iterative process of experimentation at a global level, data integration, network modeling, and generation of hypotheses. These hypotheses lead to the design of new experiments that start a new round of the cycle. Each iteration refines the model and deepens our biological understanding of the network. Ultimately, such models could be used in a predictive mode and lead to engineering and applications.

Published: 10 July 2017


© 2017 Xing et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.