KFC2: A knowledge‐based hot spot prediction method based on interface solvation, atomic density, and plasticity features

Zhu, Xiaolei; Mitchell, Julie C.

doi:10.1002/prot.23094

Cited by 214 publications

(229 citation statements)

References 50 publications

(81 reference statements)

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“…(C) iNOS FMN subdomain (yellow) and calmodulin (blue) surfaces that interact with the iNOS heme domain in the Rosetta models. For B and C, residues were colored if they were present within the interface in at least two of the three iNOS Rosetta models or for calmodulin in the iNOS-3 model (Table S2) as analyzed using the KFC2 server (27). electron acceptors such as cytochrome c. Second, calmodulin stabilizes the interaction of the FMN subdomain with the heme domain to increase the rate of interdimer electron transfer from FMN to heme.…”

Section: Discussionmentioning

confidence: 99%

“…For simplicity, only one of the iNOS models is discussed below. To ensure that the iNOS models matched the HDX-MS data, the interfaces determined by HDX-MS were then compared with the interfaces in the models determined using the Knowledge-based FADE and Contacts (KFC2) server (27). The observed HDX-MS interfaces of the heme domain result from: (i) interaction with the FMN subdomain, (ii) interaction with calmodulin, and (iii) stabilization of the heme domain dimer interface (Fig.…”

Section: Computational Modeling Of the Inos Heme Domain And Fmn Subdomentioning

confidence: 99%

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Nitric oxide synthase domain interfaces regulate electron transfer and calmodulin activation

Smith¹,

Underbakke²,

Kulp

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

136

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Nitric oxide (NO) produced by NO synthase (NOS) participates in diverse physiological processes such as vasodilation, neurotransmission, and the innate immune response. Mammalian NOS isoforms are homodimers composed of two domains connected by an intervening calmodulin-binding region. The N-terminal oxidase domain binds heme and tetrahydrobiopterin and the arginine substrate. The C-terminal reductase domain binds FAD and FMN and the cosubstrate NADPH. Although several highresolution structures of individual NOS domains have been reported, a structure of a NOS holoenzyme has remained elusive. Determination of the higher-order domain architecture of NOS is essential to elucidate the molecular underpinnings of NO formation. In particular, the pathway of electron transfer from FMN to heme, and the mechanism through which calmodulin activates this electron transfer, are largely unknown. In this report, hydrogendeuterium exchange mass spectrometry was used to map critical NOS interaction surfaces. Direct interactions between the heme domain, the FMN subdomain, and calmodulin were observed. These interaction surfaces were confirmed by kinetic studies of site-specific interface mutants. Integration of the hydrogen-deuterium exchange mass spectrometry results with computational docking resulted in models of the NOS heme and FMN subdomain bound to calmodulin. These models suggest a pathway for electron transfer from FMN to heme and a mechanism for calmodulin activation of this critical step.iNOS | NO signaling | flavin | hemoprotein N itric oxide (NO) has several essential functions in mammalian physiology. NO produced by the neuronal and endothelial nitric oxide synthase isoforms (nNOS and eNOS, respectively) initiates diverse signaling processes including vasodilation, myocardial function, and neurotransmission (1). The eNOS and nNOS isoforms are constitutively expressed and their activity responds to intracellular calcium concentrations. The inducible NOS isoform (iNOS) is transcriptionally controlled and produces NO as a cytotoxin at sites of inflammation or infection. Aberrant NO signaling contributes to a variety of diseases including stroke, hypertension, and neurodegeneration (2).Mammalian NOS isoforms are homodimeric and composed of two principal domains: the N-terminal oxidase domain and C-terminal reductase domain, which are connected by an intervening calmodulin (CaM) binding region (Fig. 1A). The N-terminal oxidase domain contains the heme and tetrahydrobiopterin cofactors and the binding site for the substrate arginine. The reductase domain is further divided into the FMN-binding subdomain and the FAD/NADPH-binding subdomains. This array of cofactors works in concert to catalyze the conversion of arginine to the intermediate N-hydroxyarginine and, ultimately, citrulline and NO. NADPH and oxygen are consumed in the process. During catalysis, electrons are shuttled from the reductase domain of one monomer to the heme domain of the opposite monomer in the homodimer (Fig. 1B) (1, 3). Electron transfer is initiat...

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Section: Discussionmentioning

confidence: 99%

Section: Computational Modeling Of the Inos Heme Domain And Fmn Subdomentioning

confidence: 99%

Nitric oxide synthase domain interfaces regulate electron transfer and calmodulin activation

Smith¹,

Underbakke²,

Kulp

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

136

View full text Add to dashboard Cite

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“…The binding site of antigen in the antibody was determined by using KFC (Knowledge-based FADE and Contacts) web server [24], [25]. The protein structures of antibodies were visualized with Vega ZZ software then the antigen was cleared from the antibody.…”

Section: Protein Structure Of Antibodiesmentioning

confidence: 99%

Polytope Prediction for Dengue Vaccine Candidate Based on Conserved Envelope Glycoprotein of Four Serotypes of Dengue Virus and Its Antigenicity

Himmah

Fitriyah

Ardyati

et al. 2016

J. Pure App. Chem. Res.

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Dengue fever reported endemic in tropical and sub-tropical country. Dengue fever caused by dengue virus, has Envelope protein that often used for vaccine development to prevent the virus infection. Vaccine development to prevent four serotype dengue virus infection still unavailable. This study aims to design polytope from four conserved epitopes of dengue virus envelope glycoprotein to prevent infection of heterotypic dengue virus and predict its antigenic challenge by molecular docking. We investigate molecular modeling of polytope, immunoinformatics analysis of polytope, protein structure of antibodies, molecular docking and protein-protein docking assessment. The polytope categorized as a stabil protein with index 29.72, has molecular weight 6,139 kDa, has three exposed antigenic determinants region and has estimated half-life is: 3.5 hours (mammalian reticulocytes, in vitro),10 min (yeast, in vivo), and >10 hours (Escherichia coli, in vivo). The Polytope binds with four broadly neutralizing antibodies of B7, C8, A11, and C10 (bnAbs) which estimated that four bnAbs can recognize four serotypes of dengue virus. The designed polytope has prospect to produce in Escherichia coli and can be applied as vaccine of heterotypic dengue virus serotype. Polytope is potentially able to generate humoral and cellular immunity.

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“…To evaluate the performance of the proposed PredHS, eight existing hot spot prediction methods, Robetta (Kortemme and Baker, 2002), FOLDEF (Guerois et al, 2002), KFC (Darnell et al, 2007), MINERVA2 (Tuncbag et al, 2009), APIS (Xia et al, 2010), KFC2a, and KFC2b (Zhu and Mitchell, 2011) are implemented and evaluated on both Dataset I and Dataset II with 10-fold crossvalidation. The performance of each model is measured by six metrics: accuracy (Accu), sensitivity (Sen), specificity (Spe), precision (Pre), CC, and F1 score.…”

Section: Performance Comparison With the State-of-the-art Approachesmentioning

confidence: 99%

“…On the other hand, empirical functions or simple physical methods, such as FOLDEF (Guerois et al, 2002) and Robetta (Kortemme and Baker, 2002), which use experimentally calibrated knowledge-based simplified models to evaluate the binding free energy, provide an alternative way to probe hot spots with much less computation. Recently, there has been considerable interest applying machine-learning methods to predict hot spots such as neural networks (Ofran and Rost, 2007), decision trees (Darnell et al, 2007), support vector machines (Cho et al, 2009;Xia et al, 2010;Zhu and Mitchell, 2011), Bayesian networks (Assi et al, 2009), minimum cut trees (Tuncbag et al, 2010), and random forests (Wang et al, 2012).…”

mentioning

confidence: 99%

Boosting Prediction Performance of Protein–Protein Interaction Hot Spots by Using Structural Neighborhood Properties

Deng

Guan²,

Wei³

et al. 2013

Journal of Computational Biology

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Binding of one protein to another in a highly specific manner to form stable complexes is critical in most biological processes, yet the mechanisms involved in the interaction of proteins are not fully clear. The identification of hot spots, a small subset of binding interfaces that account for the majority of binding free energy, is becoming increasingly important in understanding the principles of protein interactions. Despite experiments like alanine scanning mutagenesis and a variety of computational methods that have been applied to this problem, comparative studies suggest that the development of accurate and reliable solutions is still in its infant stage. We developed PredHS (Prediction of Hot Spots), a computational method that can effectively identify hot spots on protein-binding interfaces by using 38 optimally chosen properties. The optimal combination of features was selected from a set of 324 novel structural neighborhood properties by a two-step feature selection method consisting of a random forest algorithm and a sequential backward elimination method. We evaluated the performance of PredHS using a benchmark of 265 alanine-mutated interface residues (Dataset I) and a trimmed subset (Dataset II) with 10-fold cross-validation. Compared with the state-of-the art approaches, PredHS achieves a significant improvement on the prediction quality, which stems from the new structural neighborhood properties, the novel way of feature generation, as well as the selection power of the proposed two-step method. We further validated the capability of our method by an independent test and obtained promising results.

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KFC2: A knowledge‐based hot spot prediction method based on interface solvation, atomic density, and plasticity features

Cited by 214 publications

References 50 publications

Nitric oxide synthase domain interfaces regulate electron transfer and calmodulin activation

Nitric oxide synthase domain interfaces regulate electron transfer and calmodulin activation

Polytope Prediction for Dengue Vaccine Candidate Based on Conserved Envelope Glycoprotein of Four Serotypes of Dengue Virus and Its Antigenicity

Boosting Prediction Performance of Protein–Protein Interaction Hot Spots by Using Structural Neighborhood Properties

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