Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

  • Xavier DauraEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1163)


With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.


Proteins Allosteric site allosteric pathway computational models molecular dynamics simulation elastic network model covariance mutual information statistical coupling analysis energy coupling perturbation methods 



The Spanish Ministry for Science, Innovation and Universities is acknowledged for financial support under grant BIO2015-66674-R.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Catalan Institution for Research and Advanced Studies (ICREA) and Institute of Biotechnology and BiomedicineUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain

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