Publications by authors named "Xin-Qiu Yao"

27 Publications

  • Page 1 of 1

p53 Is Potentially Regulated by Cyclophilin D in the Triple-Proline Loop of the DNA Binding Domain.

Biochemistry 2021 Mar 16;60(8):597-606. Epub 2021 Feb 16.

The multifunctional protein p53 is the central molecular sensor of cellular stresses. The canonical function of p53 is to transcriptionally activate target genes in response to, for example, DNA damage that may trigger apoptosis. Recently, p53 was also found to play a role in the regulation of necrosis, another type of cell death featured by the mitochondrial permeability transition (mPT). In this process, p53 directly interacts with the mPT regulator cyclophilin D, the detailed mechanism of which however remains poorly understood. Here, we report a comprehensive computational investigation of the p53-cyclophilin D interaction using molecular dynamics simulations and associated analyses. We have identified the specific cyclophilin D binding site on p53 that is located at proline 151 in the DNA binding domain. As a peptidyl-prolyl isomerase, cyclophilin D binds p53 and catalyzes the isomerization of the peptide bond preceding proline 151. We have also characterized the effect of such an isomerization and found that the p53 domain in the state is overall more rigid than the state except for the local region around proline 151. Dynamical changes upon isomerization occur in both local and distal regions, indicating an allosteric effect elicited by the isomerization. We present potential allosteric communication pathways between proline 151 and distal sites, including the DNA binding surface. Our work provides, for the first time, a model for how cyclophilin D binds p53 and regulates its activity by switching the configuration of a specific site.
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http://dx.doi.org/10.1021/acs.biochem.0c00946DOI Listing
March 2021

The Bio3D packages for structural bioinformatics.

Protein Sci 2021 01 17;30(1):20-30. Epub 2020 Aug 17.

Department of Chemistry, Georgia State University, Atlanta, Georgia, USA.

Bio3D is a family of R packages for the analysis of biomolecular sequence, structure, and dynamics. Major functionality includes biomolecular database searching and retrieval, sequence and structure conservation analysis, ensemble normal mode analysis, protein structure and correlation network analysis, principal component, and related multivariate analysis methods. Here, we review recent package developments, including a new underlying segregation into separate packages for distinct analysis, and introduce a new method for structure analysis named ensemble difference distance matrix analysis (eDDM). The eDDM approach calculates and compares atomic distance matrices across large sets of homologous atomic structures to help identify the residue wise determinants underlying specific functional processes. An eDDM workflow is detailed along with an example application to a large protein family. As a new member of the Bio3D family, the Bio3D-eddm package supports both experimental and theoretical simulation-generated structures, is integrated with other methods for dissecting sequence-structure-function relationships, and can be used in a highly automated and reproducible manner. Bio3D is distributed as an integrated set of platform independent open source R packages available from: http://thegrantlab.org/bio3d/.
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http://dx.doi.org/10.1002/pro.3923DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737766PMC
January 2021

Comparative Protein Structure Analysis with Bio3D-Web.

Methods Mol Biol 2020 ;2112:15-28

Department of Chemistry, Georgia State University, Atlanta, GA, USA.

Bio3D-web is an online application for the interactive analysis of sequence-structure-dynamics relationships in user-defined protein structure sets. Major functionality includes structure database searching, sequence and structure conservation assessment, inter-conformer relationship mapping and clustering with principal component analysis (PCA), and flexibility prediction and comparison with ensemble normal mode analysis (eNMA). Collectively these methods allow users to start with a single sequence or structure and characterize the structural, conformational, and internal dynamic properties of homologous proteins for which there are high-resolution structures available. Functionality is also provided for the generation of custom PDF, Word, and HTML analysis reports detailing all user-specified analysis settings and corresponding results. Bio3D-web is available at http://thegrantlab.org/bio3d/webapps , as a Docker image https://hub.docker.com/r/bio3d/bio3d-web/ , or downloadable source code https://bitbucket.org/Grantlab/bio3d-web .
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http://dx.doi.org/10.1007/978-1-0716-0270-6_2DOI Listing
January 2021

Detecting Functional Dynamics in Proteins with Comparative Perturbed-Ensembles Analysis.

Acc Chem Res 2019 12 3;52(12):3455-3464. Epub 2019 Dec 3.

Department of Chemistry , Georgia State University , Atlanta , Georgia 30302-3965 , United States.

Recent advances have made all-atom molecular dynamics (MD) a powerful tool to sample the conformational energy landscape. There are still however three major challenges in the application of MD to biological systems: accuracy of force field, time scale, and the analysis of simulation trajectories. Significant progress in addressing the first two challenges has been made and extensively reviewed previously. This Account focuses on strategies of analyzing simulation data of biomolecules that also covers ways to properly design simulations and validate simulation results. In particular, we examine an approach named , which we developed to efficiently detect dynamics in protein MD simulations that can be linked to biological functions. In our recent studies, we implemented this approach to understand allosteric regulations in several disease-associated human proteins. The central task of a comparative perturbed-ensembles analysis is to compare two or more conformational ensembles of a system generated by MD simulations under distinct perturbation conditions. Perturbations can be different sequence variations, ligand-binding conditions, and other physical/chemical modifications of the system. Each simulation is long enough (e.g., microsecond-long) to ensure sufficient sampling of the local substate. Then, sophisticated bioinformatic and statistical tools are applied to extract function-related information from the simulation data, including principal component analysis, residue-residue contact analysis, difference contact network analysis (dCNA) based on the graph theory, and statistical analysis of side-chain conformations. Computational findings are further validated with experimental data. By comparing distinct conformational ensembles, functional micro- to millisecond dynamics can be inferred. In contrast, such a time scale is difficult to reach in a single simulation; even when reached for a single condition of a system, it is elusive as to what dynamical motions are related to functions without, for example, comparing free and substrate-bound proteins at the minimum. We illustrate our approach with three examples. First, we discuss using the approach to identify allosteric pathways in cyclophilin A (CypA), a member of a ubiquitous class of peptidyl-prolyl isomerase enzymes. By comparing side-chain torsion-angle distributions of CypA in wild-type and mutant forms, we identified three pathways: two are consistent with recent nuclear magnetic resonance experiments, whereas the third is a novel pathway. Second, we show how the approach enables a dynamical-evolution analysis of the human cyclophilin family. In the analysis, both conserved and divergent conformational dynamics across three cyclophilin isoforms (CypA, CypD, and CypE) were summarized. The conserved dynamics led to the discovery of allosteric networks resembling those found in CypA. A residue wise determinant underlying the unique dynamics in CypD was also detected and validated with additional mutational MD simulations. In the third example, we applied the approach to elucidate a peptide sequence-dependent allosteric mechanism in human Pin 1, a phosphorylation-dependent peptidyl-prolyl isomerase. We finally present our outlook of future directions. Especially, we envisage how the approach could help open a new avenue in drug discovery.
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http://dx.doi.org/10.1021/acs.accounts.9b00485DOI Listing
December 2019

Synergistic mutations in soluble guanylyl cyclase (sGC) reveal a key role for interfacial regions in the sGC activation mechanism.

J Biol Chem 2019 11 23;294(48):18451-18464. Epub 2019 Oct 23.

Department of Chemistry and Biochemistry, University of Maryland Baltimore County, Baltimore, Maryland 21250. Electronic address:

Soluble guanylyl cyclase (sGC) is the main receptor for nitric oxide (NO) and a central component of the NO-cGMP pathway, critical to cardiovascular function. NO binding to the N-terminal sensor domain in sGC enhances the cyclase activity of the C-terminal catalytic domain. Our understanding of the structural elements regulating this signaling cascade is limited, hindering structure-based drug design efforts that target sGC to improve the management of cardiovascular diseases. Conformational changes are thought to propagate the NO-binding signal throughout the entire sGC heterodimer, via its coiled-coil domain, to reorient the catalytic domain into an active conformation. To identify the structural elements involved in this signal transduction cascade, here we optimized a cGMP-based luciferase assay that reports on heterologous sGC activity in and identified several mutations that activate sGC. These mutations resided in the dorsal flaps, dimer interface, and GTP-binding regions of the catalytic domain. Combinations of mutations from these different elements synergized, resulting in even greater activity and indicating a complex cross-talk among these regions. Molecular dynamics simulations further revealed conformational changes underlying the functional impact of these mutations. We propose that the interfacial residues play a central role in the sGC activation mechanism by coupling the coiled-coil domain to the active site via a series of hot spots. Our results provide new mechanistic insights not only into the molecular pathway for sGC activation but also for other members of the larger nucleotidyl cyclase family.
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http://dx.doi.org/10.1074/jbc.RA119.011010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6885636PMC
November 2019

Establishing a Framework of Using Residue-Residue Interactions in Protein Difference Network Analysis.

J Chem Inf Model 2019 07 3;59(7):3222-3228. Epub 2019 Jul 3.

Department of Chemistry , Georgia State University , Atlanta , Georgia 30302-3965 , United States.

Detailed understanding of interactions between amino acid residues is critical in using promising difference network analysis approaches to map allosteric communication pathways. Using experimental data as benchmarks, we scan values of two essential residue-residue contact parameters: the distance cutoff () and the cutoff of residue separation in sequence (). The optimal = 4.5 Å is revealed, which defines the upper bound of the first shell of residue-residue packing in proteins, whereas is found to have little effects on performance. We also develop a new energy-based contact method for network analyses and find an equivalency between the energy network using the optimal energy cutoff = 1.0 and the structure network using = 4.5 Å. The simple 4.5-Å contact method is further shown to have comparable prediction accuracy to a contact method using amino acid type-specific distance cutoffs and chemical shift prediction-based methods. This study provides necessary tools in mapping dynamics to functions.
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http://dx.doi.org/10.1021/acs.jcim.9b00320DOI Listing
July 2019

Comparative structural dynamic analysis of GTPases.

PLoS Comput Biol 2018 11 9;14(11):e1006364. Epub 2018 Nov 9.

Division of Biological Sciences, Section of Molecular Biology, University of California, San Diego, La Jolla, CA, United States of America.

GTPases regulate a multitude of essential cellular processes ranging from movement and division to differentiation and neuronal activity. These ubiquitous enzymes operate by hydrolyzing GTP to GDP with associated conformational changes that modulate affinity for family-specific binding partners. There are three major GTPase superfamilies: Ras-like GTPases, heterotrimeric G proteins and protein-synthesizing GTPases. Although they contain similar nucleotide-binding sites, the detailed mechanisms by which these structurally and functionally diverse superfamilies operate remain unclear. Here we compare and contrast the structural dynamic mechanisms of each superfamily using extensive molecular dynamics (MD) simulations and subsequent network analysis approaches. In particular, dissection of the cross-correlations of atomic displacements in both the GTP and GDP-bound states of Ras, transducin and elongation factor EF-Tu reveals analogous dynamic features. This includes similar dynamic communities and subdomain structures (termed lobes). For all three proteins the GTP-bound state has stronger couplings between equivalent lobes. Network analysis further identifies common and family-specific residues mediating the state-specific coupling of distal functional sites. Mutational simulations demonstrate how disrupting these couplings leads to distal dynamic effects at the nucleotide-binding site of each family. Collectively our studies extend current understanding of GTPase allosteric mechanisms and highlight previously unappreciated similarities across functionally diverse families.
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http://dx.doi.org/10.1371/journal.pcbi.1006364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249014PMC
November 2018

Elucidating Allosteric Communications in Proteins with Difference Contact Network Analysis.

J Chem Inf Model 2018 07 11;58(7):1325-1330. Epub 2018 Jul 11.

Department of Chemistry , Georgia State University , Atlanta , Georgia 30302-3965 , United States.

A difference contact network analysis (dCNA) method is developed for delineating allosteric mechanisms in proteins. The new method addresses limitations of conventional network analysis methods and is particularly suitable for allosteric systems undergoing large-amplitude conformational changes during function. Tests show that dCNA works well for proteins of varying sizes and functions. The design of dCNA is general enough to facilitate analyses of diverse dynamic data generated by molecular dynamics, crystallography, or nuclear magnetic resonance.
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http://dx.doi.org/10.1021/acs.jcim.8b00250DOI Listing
July 2018

Decoding Allosteric Communication Pathways in Cyclophilin A with a Comparative Analysis of Perturbed Conformational Ensembles.

J Phys Chem B 2018 06 13;122(25):6528-6535. Epub 2018 Jun 13.

Department of Chemistry , Georgia State University , Atlanta , Georgia 30302-3965 , United States.

Conformational dynamics plays the key role in allosteric regulation of enzymes. Despite numerous experimental and computational efforts, the mechanism of how dynamics couple enzymatic function is poorly understood. Here, we introduce a new approach to exploring the dynamics-function relationship combining computational mutagenesis, microsecond-long molecular dynamics simulations, and side-chain torsion angle analyses. We apply our approach to elucidate the allosteric mechanism in cyclophilin A (CypA), a peptidyl-prolyl cis-trans isomerase known to participate in diverse biological processes and be associated with many diseases including cancer. Multiple single mutations are performed in CypA at previously discovered hotspot residues distal from the active site, and residues displaying significant dynamical changes upon mutations are then identified. The mutation-responsive residues delineate three distinct pathways potentially mediating allosteric communications between distal sites: two pathways resemble the allosteric networks identified in a recent experimental study, whereas the third represents a novel pathway. A residue-residue contact analysis is also performed to complement the findings. Furthermore, a recently developed difference contact network analysis is employed to explain mutation-specific allosteric effects. Our results suggest that comparing multiple conformational ensembles generated under various mutational conditions is a powerful tool to gain novel insights into enzymatic functions that are difficult to obtain through examining a single system such as the wild-type. Our approach is easy to extend for other systems. The results can also be utilized to facilitate the design of potent therapeutics targeting CypA.
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http://dx.doi.org/10.1021/acs.jpcb.8b03824DOI Listing
June 2018

Substrate Sequence Determines Catalytic Activities, Domain-Binding Preferences, and Allosteric Mechanisms in Pin1.

J Phys Chem B 2018 06 13;122(25):6521-6527. Epub 2018 Jun 13.

Pin1 is a unique phosphorylation-dependent peptidyl-prolyl isomerase that regulates diverse subcellular processes and an important potential therapeutic target. Functional mechanisms of Pin1 are complicated because of the two-domain structural organization: the catalytic domain both binds the specific pSer/Thr-Pro motif and catalyzes the cis/trans isomerization, whereas the WW domain can only bind the trans configuration and is speculated to be responsible for substrate-binding specificity. Numerous studies of Pin1 have led to two divergent conclusions on the functional role of the WW domain. One opinion states that the WW domain is an allosteric effector, and substrate binding to this domain modulates the binding and catalysis in the distal catalytic domain. The other opinion, however, argues that the WW domain does not have any allosteric role. Here, using molecular dynamics and binding free-energy calculations, we examine catalysis and allosteric mechanisms in Pin1 under various substrate- and WW-binding conditions. Our results reveal a strong substrate sequence dependency of catalysis, domain-binding preferences, and allosteric outputs in Pin1. Importantly, we show that the different opinions about the WW domain can be unified in one framework, in which substrate sequences determine whether a positive, negative, or neural allosteric effect will be elicited. Our work further elucidates detailed mechanisms underlying the sequence-dependent allostery of Pin1 and finds that interdomain contacts are key mediators of intraprotein allosteric communications. Our findings collectively provide new insights into the function of Pin1, which may facilitate the development of novel therapeutic drugs targeting Pin1 in the future.
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http://dx.doi.org/10.1021/acs.jpcb.8b03819DOI Listing
June 2018

Structural Determinants Influencing the Potency and Selectivity of Indazole-Paroxetine Hybrid G Protein-Coupled Receptor Kinase 2 Inhibitors.

Mol Pharmacol 2017 12 25;92(6):707-717. Epub 2017 Oct 25.

Life Sciences Institute (R.B., H.V.W., M.C.C., J.J.G.T.), Departments of Medicinal Chemistry (H.V.W., S.D.L., J.J.G.T.), Pharmacology (R.B., J.J.G.T.), Biological Chemistry (M.C.C., J.J.G.T.), and Vahlteich Medicinal Chemistry Core, College of Pharmacy (H.V.W., S.D.L.), University of Michigan, Ann Arbor, Michigan; Department of Chemistry, Georgia State University, Atlanta, Georgia (X.-Q.Y.); Center for Translational Medicine, Temple University, Philadelphia, Pennsylvania (A.C., J.S., J.Y.C, W.J.K.); and Department of Biological Sciences, Purdue University, West Lafayette Indiana (J.J.G.T.)

G protein-coupled receptor kinases (GRKs) phosphorylate activated receptors to promote arrestin binding, decoupling from heterotrimeric G proteins, and internalization. GRK2 and GRK5 are overexpressed in the failing heart and thus have become therapeutic targets. Previously, we discovered two classes of GRK2-selective inhibitors, one stemming from GSK180736A, a Rho-associated coiled-coil containing kinase 1 (ROCK1) inhibitor, the other from paroxetine, a selective serotonin-reuptake inhibitor. These two classes of compounds bind to the GRK2 active site in a similar configuration but contain different hinge-binding "warheads": indazole and benzodioxole, respectively. We surmised from our prior studies that an indazole would be the stronger hinge binder and would impart increased potency when substituted for benzodioxole in paroxetine derivatives. To test this hypothesis, we synthesized a series of hybrid compounds that allowed us to compare the effects of inhibitors that differ only in the identity of the warhead. The indazole-paroxetine analogs were indeed more potent than their respective benzodioxole derivatives but lost selectivity. To investigate how these two warheads dictate selectivity, we determined the crystal structures of three of the indazole hybrid compounds (CCG224061, CCG257284, and CCG258748) in complex with GRK2-G Comparison of these structures with those of analogous benzodioxole-containing complexes confirmed that the indazole-paroxetine hybrids form stronger interactions with the hinge of the kinase but also stabilize a distinct conformation of the kinase domain of GRK2 compared with previous complexes with paroxetine analogs. This conformation is analogous to one that can be assumed by GRK5, at least partially explaining the loss in selectivity.
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http://dx.doi.org/10.1124/mol.117.110130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5691592PMC
December 2017

Navigating the conformational landscape of G protein-coupled receptor kinases during allosteric activation.

J Biol Chem 2017 09 14;292(39):16032-16043. Epub 2017 Aug 14.

Division of Biological Sciences, University of California San Diego, La Jolla, California 92093

G protein-coupled receptors (GPCRs) are essential for transferring extracellular signals into carefully choreographed intracellular responses controlling diverse aspects of cell physiology. The duration of GPCR-mediated signaling is primarily regulated via GPCR kinase (GRK)-mediated phosphorylation of activated receptors. Although many GRK structures have been reported, the mechanisms underlying GRK activation are not well-understood, in part because it is unknown how these structures map to the conformational landscape available to this enzyme family. Unlike most other AGC kinases, GRKs rely on their interaction with GPCRs for activation and not phosphorylation. Here, we used principal component analysis of available GRK and protein kinase A crystal structures to identify their dominant domain motions and to provide a framework that helps evaluate how close each GRK structure is to being a catalytically competent state. Our results indicated that disruption of an interface formed between the large lobe of the kinase domain and the regulator of G protein signaling homology domain (RHD) is highly correlated with establishment of the active conformation. By introducing point mutations in the GRK5 RHD-kinase domain interface, we show with both and experiments that perturbation of this interface leads to higher phosphorylation activity. Navigation of the conformational landscape defined by this bioinformatics-based study is likely common to all GPCR-activated GRKs.
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http://dx.doi.org/10.1074/jbc.M117.807461DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5625036PMC
September 2017

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web.

J Vis Exp 2017 07 16(125). Epub 2017 Jul 16.

Department of Computational Medicine and Bioinformatics, University of Michigan Medical School;

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.
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http://dx.doi.org/10.3791/55640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5612356PMC
July 2017

Structural and Molecular Mechanisms of Cytokine-Mediated Endocrine Resistance in Human Breast Cancer Cells.

Mol Cell 2017 Mar;65(6):1122-1135.e5

Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA. Electronic address:

Human breast cancers that exhibit high proportions of immune cells and elevated levels of pro-inflammatory cytokines predict poor prognosis. Here, we demonstrate that treatment of human MCF-7 breast cancer cells with pro-inflammatory cytokines results in ERα-dependent activation of gene expression and proliferation, in the absence of ligand or presence of 4OH-tamoxifen (TOT). Cytokine activation of ERα and endocrine resistance is dependent on phosphorylation of ERα at S305 in the hinge domain. Phosphorylation of S305 by IKKβ establishes an ERα cistrome that substantially overlaps with the estradiol (E2)-dependent ERα cistrome. Structural analyses suggest that S305-P forms a charge-linked bridge with the C-terminal F domain of ERα that enables inter-domain communication and constitutive activity from the N-terminal coactivator-binding site, revealing the structural basis of endocrine resistance. ERα therefore functions as a transcriptional effector of cytokine-induced IKKβ signaling, suggesting a mechanism through which the tumor microenvironment controls tumor progression and endocrine resistance.
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http://dx.doi.org/10.1016/j.molcel.2017.02.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5546241PMC
March 2017

Online interactive analysis of protein structure ensembles with Bio3D-web.

Bioinformatics 2016 11 16;32(22):3510-3512. Epub 2016 Jul 16.

Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.

Bio3D-web is an online application for analyzing the sequence, structure and conformational heterogeneity of protein families. Major functionality is provided for identifying protein structure sets for analysis, their alignment and refined structure superposition, sequence and structure conservation analysis, mapping and clustering of conformations and the quantitative comparison of their predicted structural dynamics.

Availability: Bio3D-web is based on the Bio3D and Shiny R packages. All major browsers are supported and full source code is available under a GPL2 license from http://thegrantlab.org/bio3d-web CONTACT: bjgrant@umich.edu or lars.skjarven@uib.no.
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http://dx.doi.org/10.1093/bioinformatics/btw482DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181562PMC
November 2016

Rapid Characterization of Allosteric Networks with Ensemble Normal Mode Analysis.

J Phys Chem B 2016 08 20;120(33):8276-88. Epub 2016 Apr 20.

Department of Computational Medicine and Bioinformatics, University of Michigan , 100 Washtenaw Avenue, 2017 Palmer Commons Building, Ann Arbor, Michigan 48109-2218, United States.

Allosteric regulation is a primary means of controlling protein function. By definition, allostery involves the propagation of structural dynamic changes between distal protein sites that yields a functional change. Gaining improved knowledge of these fundamental mechanisms is important for understanding many biomolecular processes and for guiding protein engineering and drug design efforts. In this work we compare and contrast a range of normal mode analysis (NMA) approaches together with network analysis for the prediction of structural dynamics and allosteric sites. Application to heterotrimeric G proteins, hemoglobin, and caspase 7 indicates that atomistic elastic network models provide improved predictions of experimental allosteric mutation sites. Results for G proteins also display an improved consistency with those derived from more computationally demanding MD simulations. Application of this approach across available experimental structures for a given protein family in a unified manner, that we refer to as ensemble NMA, yields the best overall predictive performance. We propose that this atomistic ensemble NMA approach represents an efficient and powerful tool for guiding the exploration of coupled motions and allosteric mechanisms in cases where multiple structures are available and where MD may prove prohibitively expensive.
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http://dx.doi.org/10.1021/acs.jpcb.6b01991DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5553620PMC
August 2016

Dynamic Coupling and Allosteric Networks in the α Subunit of Heterotrimeric G Proteins.

J Biol Chem 2016 Feb 24;291(9):4742-53. Epub 2015 Dec 24.

From the Department of Computational Medicine and Bioinformatics,

G protein α subunits cycle between active and inactive conformations to regulate a multitude of intracellular signaling cascades. Important structural transitions occurring during this cycle have been characterized from extensive crystallographic studies. However, the link between observed conformations and the allosteric regulation of binding events at distal sites critical for signaling through G proteins remain unclear. Here we describe molecular dynamics simulations, bioinformatics analysis, and experimental mutagenesis that identifies residues involved in mediating the allosteric coupling of receptor, nucleotide, and helical domain interfaces of Gαi. Most notably, we predict and characterize novel allosteric decoupling mutants, which display enhanced helical domain opening, increased rates of nucleotide exchange, and constitutive activity in the absence of receptor activation. Collectively, our results provide a framework for explaining how binding events and mutations can alter internal dynamic couplings critical for G protein function.
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http://dx.doi.org/10.1074/jbc.M115.702605DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813496PMC
February 2016

Mapping the Processivity Determinants of the Kinesin-3 Motor Domain.

Biophys J 2015 Oct;109(8):1537-40

Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan. Electronic address:

Kinesin superfamily members play important roles in many diverse cellular processes, including cell motility, cell division, intracellular transport, and regulation of the microtubule cytoskeleton. How the properties of the family-defining motor domain of distinct kinesins are tailored to their different cellular roles remains largely unknown. Here, we employed molecular-dynamics simulations coupled with energetic calculations to infer the family-specific interactions of kinesin-1 and kinesin-3 motor domains with microtubules in different nucleotide states. We then used experimental mutagenesis and single-molecule motility assays to further assess the predicted residue-wise determinants of distinct kinesin-microtubule binding properties. Collectively, our results identify residues in the L8, L11, and α6 regions that contribute to family-specific microtubule interactions and whose mutation affects motor-microtubule complex stability and processive motility (the ability of an individual motor to take multiple steps along its microtubule filament). In particular, substitutions of prominent kinesin-3 residues with those found in kinesin-1, namely, R167S/H171D, K266D, and R346M, were found to decrease kinesin-3 processivity 10-fold and thus approach kinesin-1 levels.
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http://dx.doi.org/10.1016/j.bpj.2015.08.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624112PMC
October 2015

Integrating protein structural dynamics and evolutionary analysis with Bio3D.

BMC Bioinformatics 2014 Dec 10;15:399. Epub 2014 Dec 10.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

Background: Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.

Results: Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

Conclusions: The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .
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http://dx.doi.org/10.1186/s12859-014-0399-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4279791PMC
December 2014

Domain-opening and dynamic coupling in the α-subunit of heterotrimeric G proteins.

Biophys J 2013 Jul;105(2):L08-10

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

Heterotrimeric G proteins are conformational switches that turn on intracellular signaling cascades in response to the activation of G-protein-coupled receptors. Receptor activation by extracellular stimuli promotes a cycle of GTP binding and hydrolysis on the G protein α-subunit (Gα). Important conformational transitions occurring during this cycle have been characterized from extensive crystallographic studies of Gα. However, the link between the observed conformations and the mechanisms involved in G-protein activation and effector interaction remain unclear. Here we describe a comprehensive principal component analysis of available Gα crystallographic structures supplemented with extensive unbiased conventional and accelerated molecular dynamics simulations that together characterize the response of Gα to GTP binding and hydrolysis. Our studies reveal details of activating conformational changes as well as the intrinsic flexibility of the α-helical domain that includes a large-scale 60° domain opening under nucleotide-free conditions. This result is consistent with the recently reported open crystal structure of Gs, the stimulatory G protein for adenylyl cyclase, in complex with the α2 adrenergic receptor. Sets of unique interactions potentially important for the conformational transition are also identified. Moreover simulations reveal nucleotide-dependent dynamical couplings of distal regions and residues potentially important for the allosteric link between functional sites.
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http://dx.doi.org/10.1016/j.bpj.2013.06.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714883PMC
July 2013

Drug uptake pathways of multidrug transporter AcrB studied by molecular simulations and site-directed mutagenesis experiments.

J Am Chem Soc 2013 May 14;135(20):7474-85. Epub 2013 May 14.

Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan.

Multidrug resistance has been a critical issue in current chemotherapy. In Escherichia coli , a major efflux pump responsible for the multidrug resistance contains a transporter AcrB. Crystallographic studies and mutational assays of AcrB provided much of structural and overall functional insights, which led to the functionally rotating mechanism. However, the drug uptake pathways are somewhat controversial because at least two possible pathways, the vestibule and the cleft paths, were suggested. Here, combining molecular simulations and site-directed mutagenesis experiments, we addressed the uptake mechanism finding that the drug uptake pathways can be significantly different depending on the properties of drugs. First, in the computational free energy analysis of drug movements along AcrB tunnels, we found a ligand-dependent drug uptake mechanism. With the same molecular sizes, drugs that are both strongly hydrophobic and lipophilic were preferentially taken in via the vestibule path, while other drugs favored the cleft path. Second, direct simulations realized totally about 3500 events of drug uptake by AcrB for a broad range of drug property. These simulations confirmed the ligand-dependent drug uptake and further suggested that a smaller drug favors the vestibule path, while a larger one is taken in via the cleft path. Moreover, the direct simulations identified an alternative uptake path which is not visible in the crystal structure. Third, site-directed mutagenesis of AcrB in E. coli verified that mutations of residues located along the newly identified path significantly reduced the efflux efficiency, supporting its relevance in in vivo function.
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http://dx.doi.org/10.1021/ja310548hDOI Listing
May 2013

CafeMol: A Coarse-Grained Biomolecular Simulator for Simulating Proteins at Work.

J Chem Theory Comput 2011 Jun 19;7(6):1979-89. Epub 2011 May 19.

Department of Biophysics, Graduate School of Science, Kyoto University , Kyoto 606-8502, Japan.

For simulating proteins at work in millisecond time scale or longer, we develop a coarse-grained (CG) molecular dynamics (MD) method and software, CafeMol. At the resolution of one-particle-per-residue, CafeMol equips four structure-based protein models: (1) the off-lattice Go model, (2) the atomic interaction based CG model for native state and folding dynamics, (3) the multiple-basin model for conformational change dynamics, and (4) the elastic network model for quasiharmonic fluctuations around the native structure. Ligands can be treated either explicitly or implicitly. For mimicking functional motions of proteins driven by some external force, CafeMol has various and flexible means to "switch" the energy functions that induce active motions of the proteins. CafeMol can do parallel computation with modest sized PC clusters. We describe CafeMol methods and illustrate it with several examples, such as rotary motions of F1-ATPase and drug exports from a transporter. The CafeMol source code is available at www.cafemol.org .
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http://dx.doi.org/10.1021/ct2001045DOI Listing
June 2011

Drug export and allosteric coupling in a multidrug transporter revealed by molecular simulations.

Nat Commun 2010 Nov 16;1:117. Epub 2010 Nov 16.

Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan.

Multidrug resistance is a serious problem in current chemotherapy. The efflux system largely responsible for resistance in Escherichia coli contains the drug transporter, AcrB. The structures of AcrB were solved in 2002 as the symmetric homo-trimer, and then in 2006 as the asymmetric homo-trimer. The latter suggested a functionally rotating mechanism. Here, by molecular simulations of the AcrB porter domain, we uncovered allosteric coupling and the drug export mechanism in the AcrB trimer. Allosteric coupling stabilized the asymmetric structure with one drug molecule bound, which validated the modelling. Drug dissociation caused a conformational change and stabilized the symmetric structure, providing a unified view of the structures reported in 2002 and 2006. A dynamic study suggested that, among the three potential driving processes, only protonation of the drug-bound protomer can drive the functional rotation and simultaneously export the drug.
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http://dx.doi.org/10.1038/ncomms1116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3065909PMC
November 2010

Water dynamics clue to key residues in protein folding.

Biochem Biophys Res Commun 2010 Jan 7;392(1):95-9. Epub 2010 Jan 7.

State Key Laboratory for Turbulence and Complex Systems, and Department of Biomedical Engineering, and Center for Theoretical Biology, and Center for Protein Science, Peking University, Beijing 100871, China.

A computational method independent of experimental protein structure information is proposed to recognize key residues in protein folding, from the study of hydration water dynamics. Based on all-atom molecular dynamics simulation, two key residues are recognized with distinct water dynamical behavior in a folding process of the Trp-cage protein. The identified key residues are shown to play an essential role in both 3D structure and hydrophobic-induced collapse. With observations on hydration water dynamics around key residues, a dynamical pathway of folding can be interpreted.
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http://dx.doi.org/10.1016/j.bbrc.2010.01.003DOI Listing
January 2010

Water-protein interplay reveals the specificity of alpha-lytic protease.

Biochem Biophys Res Commun 2009 Jul 18;385(2):165-9. Epub 2009 May 18.

Department of Biomedical Engineering, State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing, China.

Wild type and mutant alpha-lytic protease, differing by only one amino acid, have distinct specificities. Previous studies have shown that motion patterns of the binding pocket play an important role. However, it is still unclear how these differences are generated from a single amino acid mutation. Based on comparative molecular dynamics simulations using explicit and implicit solvent models, we studied the dynamic properties of both protein and water. The explicit solvent simulations showed specificity related differences in the energy landscapes and the power spectra between the two enzymes, whereas implicit solvent simulations did not. Moreover, the explicit solvent simulations demonstrated obvious distinctions in dynamic behaviors of water, such as their residence behaviors and hydrogen bonding. These results suggest that the interplay between water and enzyme is essential in determining the substrate specificity, and the detail knowledge of such interplay can greatly improve our understanding of bio-molecules.
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http://dx.doi.org/10.1016/j.bbrc.2009.05.032DOI Listing
July 2009

Key residue-dominated protein folding dynamics.

Biochem Biophys Res Commun 2008 Aug 10;373(1):64-8. Epub 2008 Jun 10.

State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, No. 5 Yihe Yuan Street, Haidan, Beijing 100871, China.

A "key-residue" hypothesis that a few residues' characteristics contain the essential dynamics of the whole protein is proposed for the study of side-chain relaxation near native states. Molecular dynamics simulation is performed on the folding of Trp-cage, and four key residues are discovered and shown to be highly sensitive to the change of state of the protein away from the native state. Order parameters that characterize the geometrical properties of key residues are shown to form valuable phase plane on which one distinguishes different reaction pathways. Furthermore, one of the key residues, Trp6, is observed to display two reconfiguration processes, in which one is induced by an unconstrained torsion of the side-chain of Trp6, with a rate faster by almost an order of magnitude than the other one described by Kussell's model. The faster process seems to occur more frequently in our simulation and thus represent a significant mechanism in folding dynamics.
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http://dx.doi.org/10.1016/j.bbrc.2008.05.179DOI Listing
August 2008

A dynamic Bayesian network approach to protein secondary structure prediction.

BMC Bioinformatics 2008 Jan 25;9:49. Epub 2008 Jan 25.

State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, Peking University, Beijing 100871, China.

Background: Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship. However, at present, the prediction accuracy of pure HMM-type methods is much lower than that of machine learning-based methods such as neural networks (NN) or support vector machines (SVM).

Results: In this paper, we report a new method of probabilistic nature for protein secondary structure prediction, based on dynamic Bayesian networks (DBN). The new method models the PSI-BLAST profile of a protein sequence using a multivariate Gaussian distribution, and simultaneously takes into account the dependency between the profile and secondary structure and the dependency between profiles of neighboring residues. In addition, a segment length distribution is introduced for each secondary structure state. Tests show that the DBN method has made a significant improvement in the accuracy compared to other pure HMM-type methods. Further improvement is achieved by combining the DBN with an NN, a method called DBNN, which shows better Q3 accuracy than many popular methods and is competitive to the current state-of-the-arts. The most interesting feature of DBN/DBNN is that a significant improvement in the prediction accuracy is achieved when combined with other methods by a simple consensus.

Conclusion: The DBN method using a Gaussian distribution for the PSI-BLAST profile and a high-ordered dependency between profiles of neighboring residues produces significantly better prediction accuracy than other HMM-type probabilistic methods. Owing to their different nature, the DBN and NN combine to form a more accurate method DBNN. Future improvement may be achieved by combining DBNN with a method of SVM type.
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http://dx.doi.org/10.1186/1471-2105-9-49DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2266706PMC
January 2008