Docking of protein molecular surfaces with evolutionary trace analysis

Kanamori, Eiji; Murakami, Yoichi; Tsuchiya, Yuko; Standley, Daron M.; Nakamura, Haruki; Kinoshita, Kengo

doi:10.1002/prot.21737

Cited by 23 publications

(48 citation statements)

References 25 publications

(27 reference statements)

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“…We sought to model the ERO1␣-PDI interaction in silico employing the currently available crystal structure of full-length ERO1␣ (PDB code 3AHQ) and the solution structure of the b-bЈ domain fragment of human PDI (PDB code 2K18). Our docking simulation was carried out on-line (sysimm.ifrec.osaka-u.ac.jp/surFit) by analyzing the molecular surface, electrostatic potential, and hydrophobicity complementarity, weighted by the conservation of interacting residues (41,42). Numerous complex models were predicted and ranked: FIGURE 2.…”

Section: Docking Modeling Of Ero1␣-pdi Non-covalent Complexes-mentioning

confidence: 99%

Molecular Bases of Cyclic and Specific Disulfide Interchange between Human ERO1α Protein and Protein-disulfide Isomerase (PDI)

Masui

Vavassori

Fagioli

et al. 2011

Journal of Biological Chemistry

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In the endoplasmic reticulum (ER) of human cells, ERO1␣ and protein-disulfide isomerase (PDI) constitute one of the major electron flow pathways that catalyze oxidative folding of secretory proteins. Specific and limited PDI oxidation by ERO1␣ is essential to avoid ER hyperoxidation. To investigate how ERO1␣ oxidizes PDI selectively among more than 20 ERresident PDI family member proteins, we performed docking simulations and systematic biochemical analyses. Our findings reveal that a protruding ␤-hairpin of ERO1␣ specifically interacts with the hydrophobic pocket present in the redox-inactive PDI b-domain through the stacks between their aromatic residues, leading to preferred oxidation of the C-terminal PDI a-domain. ERO1␣ associated preferentially with reduced PDI, explaining the stepwise disulfide shuttle mechanism, first from ERO1␣ to PDI and then from oxidized PDI to an unfolded polypeptide bound to its hydrophobic pocket. The interaction of ERO1␣ with ERp44, another PDI family member protein, was also analyzed. Notably, ERO1␣-dependent PDI oxidation was inhibited by a hyperactive ERp44 mutant that lacks the C-terminal tail concealing the substrate-binding hydrophobic regions. The potential ability of ERp44 to inhibit ERO1␣ activity may suggest its physiological role in ER redox and protein homeostasis.

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Section: Docking Modeling Of Ero1␣-pdi Non-covalent Complexes-mentioning

confidence: 99%

Molecular Bases of Cyclic and Specific Disulfide Interchange between Human ERO1α Protein and Protein-disulfide Isomerase (PDI)

Masui

Vavassori

Fagioli

et al. 2011

Journal of Biological Chemistry

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“…The procedure terminates if k = 10 consecutive moves fail to lower the interaction energy. These parameters have been carefully tuned and adapted from previous work by us and others (Hashmi and Shehu, 2013a;Kanamori et al, 2007).…”

Section: Local Improvement Operator In Iddock +mentioning

confidence: 99%

“…These methods, which can be considered hybrid, include Kanamori et al (2007), Hashmi et al (2011, and Dominguez et al (2003). While work in Dominguez et al (2003) incorporates distance constraints obtained from the wet laboratory, work in Kanamori et al (2007) and Hashmi et al (2011 ranks configurations based on known features of native interaction interfaces, such as evolutionary conservation, prior to energy optimization. In this article, we advance work on hybrid methods for rigid-body protein-protein docking.…”

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confidence: 99%

idDock+: Integrating Machine Learning in Probabilistic Search for Protein–Protein Docking

Hashmi

Shehu

2015

Journal of Computational Biology

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Predicting the three-dimensional native structures of protein dimers, a problem known as protein-protein docking, is key to understanding molecular interactions. Docking is a computationally challenging problem due to the diversity of interactions and the high dimensionality of the configuration space. Existing methods draw configurations systematically or at random from the configuration space. The inaccuracy of scoring functions used to evaluate drawn configurations presents additional challenges. Evidence is growing that optimization of a scoring function is an effective technique only once the drawn configuration is sufficiently similar to the native structure. Therefore, in this article we present a method that employs optimization of a sophisticated energy function, FoldX, only to locally improve a promising configuration. The main question of how promising configurations are identified is addressed through a machine learning method trained a priori on an extensive dataset of functionally diverse protein dimers. To deal with the vast configuration space, a probabilistic search algorithm operates on top of the learner, feeding to it configurations drawn at random. We refer to our method as idDock+, for informatics-driven Docking. idDock+is tested on 15 dimers of different sizes and functional classes. Analysis shows that on all systems idDock+finds a near-native structure and is comparable in accuracy to other state-of-the-art methods. idDock+ represents one of the first highly efficient hybrid methods that combines fast machine learning models with demanding optimization of sophisticated energy scoring functions. Our results indicate that this is a promising direction to improve both efficiency and accuracy in docking.

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Section: Introductionmentioning

confidence: 99%

“…However, guidance of these algorithms by an energy function presents a problem, as all current energy functions, even physics-based ones, contain errors and distort the true underlying energy surface. To address this issue, a complementary direction of research focuses on learning aspects of native interaction interface and encoding them either explicitly in the search process itself [5,6,7] or implicitly in a pseudo-energy function [10,1,2].In this preliminary investigation we present a hybrid approach that employs a probabilistic search algorithm of high exploration capability but guides the algorithm in a computationally efficient manner towards native interaction interfaces. Rather than employ a costly energy function, the algorithm is guided in two steps, first ranking configurations with a machine learning model, then refining promising configurations with a sophisticated force-field.…”

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confidence: 99%

Protein-protein Docking Using Information from Native Interaction Interfaces

Hashmi

Shehu

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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We present a probabilistic search algorithm for rigid-body protein-protein docking. The algorithm is a realization of the basin hopping framework for sampling low-energy local minima of a given energy function. To save computational resources, the algorithm employs a machine learning model to score bound configurations prior to subjecting promising configurations to a local optimization with a sophisticated force field. The machine learning model is a decision tree trained on 138 known native dimeric interactions to learn features that constitute a true interaction interface. The FoldX force field is employed only on dimeric configurations sampled by the algorithm that are determined by the decision tree model to contain true interaction interfaces. The preliminary results are promising and motivate us for further investigation of such an informatics-driven approach to protein-protein docking. Categories and Subject Descriptors General Terms Algorithms KeywordsProtein-protein rigid docking; machine learning; native interaction; basin hopping BACKGROUNDProteins take specific three dimensional shapes that they use to bind with other molecules and so perform specific cellular tasks. Modeling these bound complexes is key to char- * corresponding author Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. acterizing supramolecular assemblies and so understanding the molecular basis of biological function. Template-free structural characterization of protein assemblies entails searching a high-dimensional configuration space. Simplifying the problem of protein-protein docking to its rigid-body dimeric version brings the dimensionality of the search space down to the 6 parameters needed to encode spatial arrangements of the moving unit around the reference unit.Though significant computational efforts are devoted to pairwise rigid-body protein-protein docking, the problem remains challenging. Primarily, the difficulty lies with either search algorithms of limited exploration capability or with the accuracy of the criterion used to guide these algorithms to the true native assembly, or a combination of both. Lately, a lot of work has resulted in probabilistic search algorithms with high exploration capability [13,14,7,8]. However, guidance of these algorithms by an energy function presents a problem, as all current energy functions, even physics-based ones, contain errors and distort the true underlying energy surface. To address this issue, a complementary direction of research focuses on learning aspects of native interaction interface and encoding them either explicitly in the search process itself [5,6,7] or implicitly in a pseudo-energy function [10,1,2]....

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Docking of protein molecular surfaces with evolutionary trace analysis

Cited by 23 publications

References 25 publications

Molecular Bases of Cyclic and Specific Disulfide Interchange between Human ERO1α Protein and Protein-disulfide Isomerase (PDI)

Molecular Bases of Cyclic and Specific Disulfide Interchange between Human ERO1α Protein and Protein-disulfide Isomerase (PDI)

idDock+: Integrating Machine Learning in Probabilistic Search for Protein–Protein Docking

Protein-protein Docking Using Information from Native Interaction Interfaces

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