20 results on '"Genki Terashi"'
Search Results
2. Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
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Aashish Jain, Genki Terashi, Yuki Kagaya, Sai Raghavendra Maddhuri Venkata Subramaniya, Charles Christoffer, and Daisuke Kihara
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Medicine ,Science - Abstract
Abstract Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.
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- 2021
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3. Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning
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Xiao Wang, Eman Alnabati, Tunde W. Aderinwale, Sai Raghavendra Maddhuri Venkata Subramaniya, Genki Terashi, and Daisuke Kihara
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Science - Abstract
It is challenging to extract structural information from EM density maps at intermediate or low resolutions. Here, the authors present Emap2sec+, a program for detecting nucleotides and protein secondary structures in EM density maps at 5 to 10 Å resolution.
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- 2021
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4. VESPER: global and local cryo-EM map alignment using local density vectors
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Xusi Han, Genki Terashi, Charles Christoffer, Siyang Chen, and Daisuke Kihara
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Science - Abstract
Here, the authors present VESPER, a program for EM density map search and alignment. Using benchmark datasets, they demonstrate that VESPER performs accurate global and local alignments and comparisons of EM maps.
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- 2021
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5. MarkovFit: Structure Fitting for Protein Complexes in Electron Microscopy Maps Using Markov Random Field
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Eman Alnabati, Juan Esquivel-Rodriguez, Genki Terashi, and Daisuke Kihara
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protein modeling ,cryo-EM ,Markov random field ,structure fitting ,protein structure prediction ,Biology (General) ,QH301-705.5 - Abstract
An increasing number of protein complex structures are determined by cryo-electron microscopy (cryo-EM). When individual protein structures have been determined and are available, an important task in structure modeling is to fit the individual structures into the density map. Here, we designed a method that fits the atomic structures of proteins in cryo-EM maps of medium to low resolutions using Markov random fields, which allows probabilistic evaluation of fitted models. The accuracy of our method, MarkovFit, performed better than existing methods on datasets of 31 simulated cryo-EM maps of resolution 10 Å, nine experimentally determined cryo-EM maps of resolution less than 4 Å, and 28 experimentally determined cryo-EM maps of resolution 6 to 20 Å.
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- 2022
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6. De novo main-chain modeling for EM maps using MAINMAST
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Genki Terashi and Daisuke Kihara
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Science - Abstract
Main-chain tracing remains a time-consuming task for medium resolution cryo-EM maps. Here the authors describe MAINMAST, a computational approach for building main-chain structure models of proteins from EM maps of 4-5 Å resolution that builds main-chain models of the protein by tracing local dense points in the density distribution.
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- 2018
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7. Modeling the assembly order of multimeric heteroprotein complexes.
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Lenna X Peterson, Yoichiro Togawa, Juan Esquivel-Rodriguez, Genki Terashi, Charles Christoffer, Amitava Roy, Woong-Hee Shin, and Daisuke Kihara
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Biology (General) ,QH301-705.5 - Abstract
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.
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- 2018
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8. Modeling disordered protein interactions from biophysical principles.
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Lenna X Peterson, Amitava Roy, Charles Christoffer, Genki Terashi, and Daisuke Kihara
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Biology (General) ,QH301-705.5 - Abstract
Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.
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- 2017
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9. CAB-Align: A Flexible Protein Structure Alignment Method Based on the Residue-Residue Contact Area.
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Genki Terashi and Mayuko Takeda-Shitaka
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Medicine ,Science - Abstract
Proteins are flexible, and this flexibility has an essential functional role. Flexibility can be observed in loop regions, rearrangements between secondary structure elements, and conformational changes between entire domains. However, most protein structure alignment methods treat protein structures as rigid bodies. Thus, these methods fail to identify the equivalences of residue pairs in regions with flexibility. In this study, we considered that the evolutionary relationship between proteins corresponds directly to the residue-residue physical contacts rather than the three-dimensional (3D) coordinates of proteins. Thus, we developed a new protein structure alignment method, contact area-based alignment (CAB-align), which uses the residue-residue contact area to identify regions of similarity. The main purpose of CAB-align is to identify homologous relationships at the residue level between related protein structures. The CAB-align procedure comprises two main steps: First, a rigid-body alignment method based on local and global 3D structure superposition is employed to generate a sufficient number of initial alignments. Then, iterative dynamic programming is executed to find the optimal alignment. We evaluated the performance and advantages of CAB-align based on four main points: (1) agreement with the gold standard alignment, (2) alignment quality based on an evolutionary relationship without 3D coordinate superposition, (3) consistency of the multiple alignments, and (4) classification agreement with the gold standard classification. Comparisons of CAB-align with other state-of-the-art protein structure alignment methods (TM-align, FATCAT, and DaliLite) using our benchmark dataset showed that CAB-align performed robustly in obtaining high-quality alignments and generating consistent multiple alignments with high coverage and accuracy rates, and it performed extremely well when discriminating between homologous and nonhomologous pairs of proteins in both single and multi-domain comparisons. The CAB-align software is freely available to academic users as stand-alone software at http://www.pharm.kitasato-u.ac.jp/bmd/bmd/Publications.html.
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- 2015
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10. Bioinformatic Approaches for Characterizing Molecular Structure and Function of Food Proteins
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Harrison Helmick, Anika Jain, Genki Terashi, Andrea Liceaga, Arun K. Bhunia, Daisuke Kihara, and Jozef L. Kokini
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Food Science - Abstract
Structural bioinformatics analyzes protein structural models with the goal of uncovering molecular drivers of food functionality. This field aims to develop tools that can rapidly extract relevant information from protein databases as well as organize this information for researchers interested in studying protein functionality. Food bioinformaticians take advantage of millions of protein amino acid sequences and structures contained within these databases, extracting features such as surface hydrophobicity that are then used to model functionality, including solubility, thermostability, and emulsification. This work is aided by a protein structure–function relationship framework, in which bioinformatic properties are linked to physicochemical experimentation. Strong bioinformatic correlations exist for protein secondary structure, electrostatic potential, and surface hydrophobicity. Modeling changes in protein structures through molecular mechanics is an increasingly accessible field that will continue to propel food science research.
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- 2023
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11. Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps
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Daisuke Kihara, Takaharu Mori, Genki Terashi, Yuji Sugita, and Daisuke Matsuoka
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010304 chemical physics ,Protein Conformation ,Computer science ,Cryo-electron microscopy ,General Chemical Engineering ,Cryoelectron Microscopy ,Structure (category theory) ,Proteins ,General Chemistry ,Molecular Dynamics Simulation ,Library and Information Sciences ,Overfitting ,01 natural sciences ,0104 chemical sciences ,Computer Science Applications ,Progressive refinement ,010404 medicinal & biomolecular chemistry ,Molecular dynamics ,Structural biology ,0103 physical sciences ,Simulated annealing ,Protein structure modeling ,Algorithm - Abstract
Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.
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- 2021
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12. Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning
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Tunde Aderinwale, Xiao Wang, Eman Alnabati, Daisuke Kihara, Sai Raghavendra Maddhuri Venkata Subramaniya, and Genki Terashi
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0301 basic medicine ,Models, Molecular ,Cryo-electron microscopy ,Science ,Biophysics ,General Physics and Astronomy ,computer.software_genre ,01 natural sciences ,Convolutional neural network ,General Biochemistry, Genetics and Molecular Biology ,Article ,Protein Structure, Secondary ,03 medical and health sciences ,Computational biophysics ,Protein structure ,Deep Learning ,Voxel ,Cryoelectron microscopy ,Protein secondary structure ,Physics ,Multidisciplinary ,010405 organic chemistry ,business.industry ,Deep learning ,Resolution (electron density) ,RNA ,Computational Biology ,General Chemistry ,DNA ,0104 chemical sciences ,030104 developmental biology ,Nucleic Acid Conformation ,Artificial intelligence ,Biological system ,business ,computer ,Software ,Macromolecule - Abstract
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps., It is challenging to extract structural information from EM density maps at intermediate or low resolutions. Here, the authors present Emap2sec+, a program for detecting nucleotides and protein secondary structures in EM density maps at 5 to 10 Å resolution.
- Published
- 2021
13. Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST
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Eman Alnabati, Genki Terashi, and Daisuke Kihara
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Models, Molecular ,Models, Structural ,Medical Laboratory Technology ,Microscopy, Electron ,General Immunology and Microbiology ,General Neuroscience ,Cryoelectron Microscopy ,Proteins ,Health Informatics ,General Pharmacology, Toxicology and Pharmaceutics ,General Biochemistry, Genetics and Molecular Biology - Abstract
An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.
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- 2022
14. De novo main-chain modeling with MAINMAST in 2015/2016 EM Model Challenge
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Daisuke Kihara and Genki Terashi
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0301 basic medicine ,Map interpretation ,Computer science ,Protein Conformation ,Minimum spanning tree ,Article ,Interpretation (model theory) ,03 medical and health sciences ,0302 clinical medicine ,Software ,Chain (algebraic topology) ,Structural Biology ,Position (vector) ,MAINMAST ,Rosetta ,Electron microscopy ,Confidence score ,Protocol (object-oriented programming) ,Cryo-EM ,business.industry ,Cryoelectron Microscopy ,Proteins ,Longest path problem ,030104 developmental biology ,Main-chain trace ,CryoEM Model Challenge ,Protein structure modeling ,confidence score ,business ,Algorithm ,030217 neurology & neurosurgery ,Mean shifting algorithm - Abstract
Protein tertiary structure modeling is a critical step for the interpretation of three dimensional (3D) election microscopy density. Our group participated the 2015/2016 EM Model Challenge using the MAINMAST software for a de novo main chain modeling. The software generates local dense points using the mean shifting algorithm, and connects them into Cα models by calculating the minimum spanning tree and the longest path. Subsequently, full atom structure models are generated, which are subject to structural refinement. Here, we summarize the qualities of our submitted models and examine successful and unsuccessful models, including 3D models we did not submit to the Challenge. Our protocol using the MAINMAST software was sometimes able to build correct conformations with 3.4–5.1 A RMSD. Unsuccessful models had failure of chain traces, however, their Cα positions and some local structures were quite correctly built. For evaluate the quality of the models, the MAINMAST software provides a confidence score for each Cα position from the consensus of top 100 scoring models.
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- 2018
15. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
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Howook Hwang, Shiyong Liu, Xiaoqin Zou, Huan-Xiang Zhou, Hideaki Umeyama, Paul A. Bates, Hahnbeom Park, Yangyu Huang, Xiaolei Zhu, Marianne Rooman, Rudi Agius, David Baker, Sarel J. Fleishman, Dimitri Gillis, Eiji Kanamori, Yuko Tsuchiya, Sandor Vajda, Panagiotis L. Kastritis, Brian Jimenez, Thom Vreven, Xiufeng Yang, Hiromitsu Shimoyama, Nan Zhao, Zhiping Weng, Sheng-You Huang, Mikael Trellet, Chaok Seok, Samuel C. Flores, Miguel Romero-Durana, Sanbo Qin, Michael S. Pacella, Julie C. Mitchell, Mayuko Takeda-Shitaka, Dmitri Beglov, Jeffrey J. Gray, Shoshana J. Wodak, Rocco Moretti, Martin Zacharias, Dmitry Korkin, Dima Kozakov, João P. G. L. M. Rodrigues, Haruki Nakamura, Juan Esquivel-Rodríguez, Mieczyslaw Torchala, Yves Dehouck, Alexandre M. J. J. Bonvin, David R. Hall, Mitsuo Iwadate, Krishna Praneeth Kilambi, Jamica Sarmiento, Daron M. Standley, Joël Janin, Omar N. A. Demerdash, Brian G. Pierce, Chiara Pallara, Meng Cui, Shusuke Teraguchi, Petr Popov, Hasup Lee, Haotian Li, Juan Fernández-Recio, Laura Pérez-Cano, Sergei Grudinin, Sameer Velankar, Daisuke Kihara, Xiaofeng Ji, Genki Terashi, Yi Xiao, Shide Liang, and Iain H. Moal
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Genetics ,0303 health sciences ,Mutation ,010304 chemical physics ,Fitness landscape ,Stability (learning theory) ,Computational biology ,Yeast display ,Biology ,medicine.disease_cause ,01 natural sciences ,Biochemistry ,Deep sequencing ,Protein–protein interaction ,03 medical and health sciences ,Structural Biology ,0103 physical sciences ,medicine ,CASP ,Saturated mutagenesis ,Molecular Biology ,030304 developmental biology - Abstract
Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.
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- 2013
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16. The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation
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Mitsuo Iwadate, Mayuko Takeda-Shitaka, Kazuhiko Kanou, Hideaki Umeyama, Daisuke Takaya, and Genki Terashi
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Energy estimation ,business.industry ,Computer science ,education ,computer.software_genre ,Biochemistry ,Software ,Knowledge base ,Structural Biology ,X ray methods ,DOCK ,Critical assessment ,Macromolecular docking ,Data mining ,business ,Molecular Biology ,computer ,Combined method - Abstract
We participated in rounds 6-12 of the critical assessment of predicted interaction (CAPRI) contest as the SKE-DOCK server and human teams. The SKE-DOCK server is based on simple geometry docking and a knowledge base scoring function. The procedure is summarized in the following three steps: (1) protein docking according to shape complementarity, (2) evaluating complex models, and (3) repacking side-chain of models. The SKE-DOCK server did not make use of biological information. On the other hand, the human team tried various intervention approaches. In this article, we describe in detail the processes of the SKE-DOCK server, together with results and reasons for success and failure. Good predicted models were obtained for target 25 by both the SKE-DOCK server and human teams. When the modeled receptor proteins were superimposed on the experimental structures, the smallest Ligand-rmsd values corresponding to the rmsd between the model and experimental structures were 3.307 and 3.324 A, respectively. Moreover, the two teams obtained 4 and 2 acceptable models for target 25. The overall result for both the SKE-DOCK server and human teams was medium accuracy for one (Target 25) out of nine targets.
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- 2007
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17. Fams-ace: A combined method to select the best model after remodeling all server models
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Mitsuo Iwadate, Kazuhiro Ohta, Kazuhiko Kanou, Daisuke Takaya, Hideaki Umeyama, Akio Hosoi, Genki Terashi, and Mayuko Takeda-Shitaka
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Models, Molecular ,Protein Conformation ,Computer science ,Computational Biology ,Proteins ,Models, Theoretical ,computer.software_genre ,Biochemistry ,Expert group ,Fully automated ,Structural Homology, Protein ,Structural Biology ,Model quality ,Data mining ,CASP ,Molecular Biology ,computer ,Algorithms ,Combined method - Abstract
During Critical Assessment of Protein Structure Prediction (CASP7, Pacific Grove, CA, 2006), fams-ace was entered in the 3D coordinate prediction category as a human expert group. The procedure can be summarized by the following three steps. (1) All the server models were refined and rebuilt utilizing our homology modeling method. (2) Representative structures were selected from each server, according to a model quality evaluation, based on a 3D1D profile score (like Verify3D). (3) The top five models were selected and submitted in the order of the consensus-based score (like 3D-Jury). Fams-ace is a fully automated server and does not require human intervention. In this article, we introduce the methodology of fams-ace and discuss the successes and failures of this approach during CASP7. In addition, we discuss possible improvements for the next CASP.
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- 2007
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18. Protein structure prediction in CASP6 using CHIMERA and FAMS
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Mayuko Takeda-Shitaka, Kazuhiko Kanou, Mitsuo Iwadate, Hideaki Umeyama, Genki Terashi, and Daisuke Takaya
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Models, Molecular ,Proteomics ,Protein Folding ,Protein Conformation ,Archaeal Proteins ,Sequence alignment ,Biology ,Machine learning ,computer.software_genre ,Biochemistry ,Protein Structure, Secondary ,Bacterial protein ,Software ,Protein structure ,Bacterial Proteins ,Structural Biology ,Computer Simulation ,Homology modeling ,Databases, Protein ,CASP ,Molecular Biology ,Internet ,Computers ,business.industry ,fungi ,Computational Biology ,Reproducibility of Results ,Protein structure prediction ,Protein Structure, Tertiary ,Artificial intelligence ,business ,Sequence Alignment ,Model building ,computer ,Algorithms - Abstract
In CASP6, the CHIMERA-group predicted full-atom models of all targets using SKE-CHIMERA, a Web-user interface system for protein structure prediction that allows human intervention at necessary stages; we used a lot of information from our own data and from publicly available data. Using SKE-CHIMERA, we iterated manual step (template selection and alignment by the in-house program CHIMERA) and automatic step (three-dimensional model building by the in-house program FAMS). The official CASP6 assessment showed that CHIMERA-group was one of the most successful predictors in homology modeling, especially for FR/H (Fold Recognition/Homologous). In this article, we introduce the method of CHIMERA-group and discuss its successes and failures in CASP6.
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- 2005
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19. LB3D: a protein three-dimensional substructure search program based on the lower bound of a root mean square deviation value
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Genki Terashi, Tetsuo Shibuya, and Mayuko Takeda-Shitaka
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Proteomics ,Protein Folding ,Protein Conformation ,Value (computer science) ,Proteins ,Function (mathematics) ,Upper and lower bounds ,Combinatorics ,Root mean square ,Search Engine ,Computational Mathematics ,Protein structure ,Computational Theory and Mathematics ,Modeling and Simulation ,Genetics ,Substructure ,Databases, Protein ,Molecular Biology ,Time complexity ,Root-mean-square deviation ,Algorithms ,Software ,Mathematics - Abstract
Searching for protein structure-function relationships using three-dimensional (3D) structural coordinates represents a fundamental approach for determining the function of proteins with unknown functions. Since protein structure databases are rapidly growing in size, the development of a fast search method to find similar protein substructures by comparison of protein 3D structures is essential. In this article, we present a novel protein 3D structure search method to find all substructures with root mean square deviations (RMSDs) to the query structure that are lower than a given threshold value. Our new algorithm runs in O(m + N/m0.5) time, after O(N log N) preprocessing, where N is the database size and m is the query length. The new method is 1.8–41.6 times faster than the practically best known O(N) algorithm, according to computational experiments using a huge database (i.e., >20,000,000 C-alpha coordinates).
- Published
- 2012
20. Comprehensive analysis of the Co-structures of dipeptidyl peptidase IV and its inhibitor.
- Author
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Hiroyuki Nojima, Kazuhiko Kanou, Genki Terashi, Mayuko Takeda-Shitaka, Gaku Inoue, Koichiro Atsuda, Chihiro Itoh, Chie Iguchi, and Hajime Matsubara
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CD26 antigen ,CRYSTAL structure ,SUBSTITUENTS (Chemistry) ,COMPOSITION of water ,GLUCAGON-like peptide 1 ,POLYPEPTIDES - Abstract
Background: We comprehensively analyzed X-ray cocrystal structures of dipeptidyl peptidase IV (DPP-4) and its inhibitor to clarify whether DPP-4 alters its general or partial structure according to the inhibitor used and whether DPP-4 has a common rule for inhibitor binding. Results: All the main and side chains in the inhibitor binding area were minimally altered, except for a few side chains, despite binding to inhibitors of various shapes. Some residues (Arg125, Glu205, Glu206, Tyr662 and Asn710) in the area had binding modes to fix a specific atom of inhibitor to a particular spatial position in DPP-4. We found two specific water molecules that were common to 92 DPP-4 structures. The two water molecules were close to many inhibitors, and seemed to play two roles: maintaining the orientation of the Glu205 and Glu206 side chains through a network via the water molecules, and arranging the inhibitor appropriately at the S2 subsite. Conclusions: Our study based on high-quality resources may provide a necessary minimum consensus to help in the discovery of a novel DPP-4 inhibitor that is commercially useful. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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