Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

Abstract

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

“…We used FIRST (Floppy Inclusion and Rigid Substructure Topography) approach (Jacobs et al, 2001; Rader et al, 2002; Chubynsky and Thorpe, 2007) and the Python-based Constraint Network Analysis (CNA) interface (Hespenheide et al, 2002; Kruger et al, 2013; Pfleger et al, 2013a,b) to analyze partition of rigid and flexible regions in a set of protein kinases with the predicted cancer driver mutations. The employed parameters are consistent with our previous studies of protein kinases (Stetz et al, 2017). Protein stability computations that evaluated the effect of cancer driver mutations on the functional forms of the ErbB kinases were performed using CUPSAT (Cologne University Protein Stability Analysis Tool) (Parthiban et al, 2006, 2007).…”

Dissecting Structure-Encoded Determinants of Allosteric Cross-Talk between Post-Translational Modification Sites in the Hsp90 Chaperones

“…PRS formalism is rooted in the linear response theory to estimate residue response to external forces applied systematically to each residue in the protein system 74 , 75 . This approach was successfully used in probing allosteric mechanisms in single protein domains and large multi-domain assemblies 76 – 79 . The average perturbation response maps are matrices in which the ij th element refers to the average response (displacement) of residue j to external perturbation at residue i .…”

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

“…These studies included experiment-guided structural modeling and protein folding analysis (Levinson et al, 2006 ; Zhang et al, 2006 ; Kornev et al, 2008 ; Han et al, 2011 ; Jura et al, 2011 ; Shan et al, 2011 , 2012 , 2013 ; Taylor and Kornev, 2011 ; Tzeng and Kalodimos, 2011 ; Levinson and Boxer, 2012 , 2014 ; Taylor et al, 2012a , b ; Meharena et al, 2013 ; Shaw et al, 2014 ; Shukla et al, 2014 ; Kornev and Taylor, 2015 ; Schulze et al, 2016 ; Narayanan et al, 2017 ; Levinson, 2018 ; Ruff et al, 2018 ), molecular simulations and free energy computations (Yang and Roux, 2008 ; Dixit and Verkhivker, 2009 , 2011a , b ; Yang et al, 2009 ; Arkhipov et al, 2013 ; Lin and Roux, 2013 ; Lin et al, 2013 , 2014 ; Dixit and Verkhivker, 2014 ; Meng and Roux, 2014 ; Fajer et al, 2017 ; Kim et al, 2017 ; Meng et al, 2017 ), and network modeling (James and Verkhivker, 2014 ; Tse and Verkhivker, 2015a , b , c ; Czemeres et al, 2017 ; Stetz et al, 2017 ; Astl and Verkhivker, 2019a , b ). By examining residue interaction networks in protein kinases a unifying mechanistic model of allosteric coupling between the ATP-binding and substrate binding sites conserved among kinases was proposed (Tse and Verkhivker, 2015a , b , c ; Stetz et al, 2017 ). A theoretical framework for rationalizing binding preferences of the kinase inhibitors was developed establishing the relationships between ligand binding and modulation of the residue interaction networks (Tse and Verkhivker, 2015a , b , c ).…”

“…Computational studies of allosteric regulation in molecular chaperones Hsp90 and Hsp70 have also been instrumental to the progress in the field by complementing biochemical experiments and providing a detailed dynamic view of the functional cycle and mechanisms (Colombo et al, 2008 ; Morra et al, 2009 , Verkhivker et al, 2009 ; Morra et al, 2010 , 2012 ; Matts et al, 2011a , b ; Chiappori et al, 2012 , 2016 ; Dixit and Verkhivker, 2012 ; Lawless et al, 2013 ; Verkhivker, 2014 , 2018a , b ; Paladino et al, 2015 ; Stetz and Verkhivker, 2015 , 2016 , 2017 , 2018 ; Czemeres et al, 2017 ; Stetz et al, 2017 ). Using a network-based formalism of allostery, computational studies have captured NMR-observed direction-specific nature of signal propagation pathways in the Hsp70 chaperone (Stetz and Verkhivker, 2015 , 2017 ).…”