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      Computational Modeling of Allosteric Communication Reveals Organizing Principles of Mutation-Induced Signaling in ABL and EGFR Kinases

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      1 , 1 , 2 , *
      PLoS Computational Biology
      Public Library of Science

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          Abstract

          The emerging structural information about allosteric kinase complexes and the growing number of allosteric inhibitors call for a systematic strategy to delineate and classify mechanisms of allosteric regulation and long-range communication that control kinase activity. In this work, we have investigated mechanistic aspects of long-range communications in ABL and EGFR kinases based on the results of multiscale simulations of regulatory complexes and computational modeling of signal propagation in proteins. These approaches have been systematically employed to elucidate organizing molecular principles of allosteric signaling in the ABL and EGFR multi-domain regulatory complexes and analyze allosteric signatures of the gate-keeper cancer mutations. We have presented evidence that mechanisms of allosteric activation may have universally evolved in the ABL and EGFR regulatory complexes as a product of a functional cross-talk between the organizing αF-helix and conformationally adaptive αI-helix and αC-helix. These structural elements form a dynamic network of efficiently communicated clusters that may control the long-range interdomain coupling and allosteric activation. The results of this study have unveiled a unifying effect of the gate-keeper cancer mutations as catalysts of kinase activation, leading to the enhanced long-range communication among allosterically coupled segments and stabilization of the active kinase form. The results of this study can reconcile recent experimental studies of allosteric inhibition and long-range cooperativity between binding sites in protein kinases. The presented study offers a novel molecular insight into mechanistic aspects of allosteric kinase signaling and provides a quantitative picture of activation mechanisms in protein kinases at the atomic level.

          Author Summary

          Despite recent progress in computational and experimental studies of dynamic regulation in protein kinases, a mechanistic understanding of long-range communication and mechanisms of mutation-induced signaling controlling kinase activity remains largely qualitative. In this study, we have performed a systematic modeling and analysis of allosteric activation in ABL and EGFR kinases at the increasing level of complexity - from catalytic domain to multi-domain regulatory complexes. The results of this study have revealed organizing structural and mechanistic principles of allosteric signaling in protein kinases. Although activation mechanisms in ABL and EGFR kinases have evolved through acquisition of structurally different regulatory complexes, we have found that long-range interdomain communication between common functional segments (αF-helix and αC-helix) may be important for allosteric activation. The results of study have revealed molecular signatures of activating cancer mutations and have shed the light on general mechanistic aspects of mutation-induced signaling in protein kinases. An advanced understanding and further characterization of molecular signatures of kinase mutations may aid in a better rationalization of mutational effects on clinical outcomes and facilitate molecular-based therapeutic strategies to combat kinase mutation-dependent tumorigenesis.

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          Most cited references161

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          Detection of mutations in EGFR in circulating lung-cancer cells.

          The use of tyrosine kinase inhibitors to target the epidermal growth factor receptor gene (EGFR) in patients with non-small-cell lung cancer is effective but limited by the emergence of drug-resistance mutations. Molecular characterization of circulating tumor cells may provide a strategy for noninvasive serial monitoring of tumor genotypes during treatment. We captured highly purified circulating tumor cells from the blood of patients with non-small-cell lung cancer using a microfluidic device containing microposts coated with antibodies against epithelial cells. We performed EGFR mutational analysis on DNA recovered from circulating tumor cells using allele-specific polymerase-chain-reaction amplification and compared the results with those from concurrently isolated free plasma DNA and from the original tumor-biopsy specimens. We isolated circulating tumor cells from 27 patients with metastatic non-small-cell lung cancer (median number, 74 cells per milliliter). We identified the expected EGFR activating mutation in circulating tumor cells from 11 of 12 patients (92%) and in matched free plasma DNA from 4 of 12 patients (33%) (P=0.009). We detected the T790M mutation, which confers drug resistance, in circulating tumor cells collected from patients with EGFR mutations who had received tyrosine kinase inhibitors. When T790M was detectable in pretreatment tumor-biopsy specimens, the presence of the mutation correlated with reduced progression-free survival (7.7 months vs. 16.5 months, P<0.001). Serial analysis of circulating tumor cells showed that a reduction in the number of captured cells was associated with a radiographic tumor response; an increase in the number of cells was associated with tumor progression, with the emergence of additional EGFR mutations in some cases. Molecular analysis of circulating tumor cells from the blood of patients with lung cancer offers the possibility of monitoring changes in epithelial tumor genotypes during the course of treatment. 2008 Massachusetts Medical Society
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            A small molecule-kinase interaction map for clinical kinase inhibitors.

            Kinase inhibitors show great promise as a new class of therapeutics. Here we describe an efficient way to determine kinase inhibitor specificity by measuring binding of small molecules to the ATP site of kinases. We have profiled 20 kinase inhibitors, including 16 that are approved drugs or in clinical development, against a panel of 119 protein kinases. We find that specificity varies widely and is not strongly correlated with chemical structure or the identity of the intended target. Many novel interactions were identified, including tight binding of the p38 inhibitor BIRB-796 to an imatinib-resistant variant of the ABL kinase, and binding of imatinib to the SRC-family kinase LCK. We also show that mutations in the epidermal growth factor receptor (EGFR) found in gefitinib-responsive patients do not affect the binding affinity of gefitinib or erlotinib. Our results represent a systematic small molecule-protein interaction map for clinical compounds across a large number of related proteins.
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              AP24534, a pan-BCR-ABL inhibitor for chronic myeloid leukemia, potently inhibits the T315I mutant and overcomes mutation-based resistance.

              Inhibition of BCR-ABL by imatinib induces durable responses in many patients with chronic myeloid leukemia (CML), but resistance attributable to kinase domain mutations can lead to relapse and a switch to second-line therapy with nilotinib or dasatinib. Despite three approved therapeutic options, the cross-resistant BCR-ABL(T315I) mutation and compound mutants selected on sequential inhibitor therapy remain major clinical challenges. We report design and preclinical evaluation of AP24534, a potent, orally available multitargeted kinase inhibitor active against T315I and other BCR-ABL mutants. AP24534 inhibited all tested BCR-ABL mutants in cellular and biochemical assays, suppressed BCR-ABL(T315I)-driven tumor growth in mice, and completely abrogated resistance in cell-based mutagenesis screens. Our work supports clinical evaluation of AP24534 as a pan-BCR-ABL inhibitor for treatment of CML.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                October 2011
                October 2011
                6 October 2011
                : 7
                : 10
                : e1002179
                Affiliations
                [1 ]Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, Lawrence, Kansas, United States of America
                [2 ]Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America
                National Cancer Institute, United States of America and Tel Aviv University, Israel
                Author notes

                ¤: Current address: School of Computational Sciences, Crean School of Health and Life Sciences, Chapman University, Orange, California, United States of America

                Conceived and designed the experiments: GMV. Performed the experiments: AD GMV. Analyzed the data: AD GMV. Contributed reagents/materials/analysis tools: AD GMV. Wrote the paper: GMV.

                Article
                PCOMPBIOL-D-11-00654
                10.1371/journal.pcbi.1002179
                3188506
                21998569
                307b286e-a587-4a15-8620-649af0844d6f
                Dixit, Verkhivker. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 11 May 2011
                : 16 July 2011
                Page count
                Pages: 19
                Categories
                Research Article
                Biology
                Biophysics
                Computational Biology
                Chemistry
                Computational Chemistry
                Computer Science
                Algorithms
                Computer Modeling
                Physics
                Biophysics
                Statistical Mechanics

                Quantitative & Systems biology
                Quantitative & Systems biology

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