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A benchmark study of loop modeling methods applied to G protein-coupled receptors

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2019年第6期33卷 573-595页

作者： Wink, Lee H. Baker, Daniel L. Cole, Judith A. Parrill, Abby L. Univ Memphis Dept Chem Memphis TN 38152 USA Univ Memphis Dept Biol Sci Memphis TN 38152 USA

G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1 angstrom on the S-S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1-4 on average between 0.33 and 1.27 angstrom. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality.

关键词： Comparative modeling Homology modeling loop modeling GPCR

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New Deep Learning Methods for Protein loop modeling

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第2期16卷 596-606页

作者： Nguyen, Son P. Li, Zhaoyu Xu, Dong Shang, Yi Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Christopher S Bond Life Sci Ctr Columbia MO 65211 USA

Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called Nearlooper, Conlooper, Reslooper, Hylooper1, and Hylooper2 are proposed. Nearlooper is based on the nearest neighbor technique. Conlooper applies deep convolutional neural networks to predict C-alpha atoms distance matrix as an orientation-independent representation of protein structure. Reslooper uses residual neural networks instead of deep convolutional neural networks. Hylooper1 combines the results of Nearlooper and Conlooper while Hylooper2 combines Nearlooper and Reslooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.

关键词： Protein structure prediction loop modeling machine learning deep learning convolutional neural networks residual neural networks

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A fold-recognition approach to loop modeling

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JOURNAL OF MOLECULAR modeling 2006年第2期12卷 125-139页

作者： Levefelt, C Lundh, D Univ Skovde Sch Humanities & Informat S-54128 Skovde Sweden

A novel approach is proposed for modeling loop regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create an alignment between the target sequence and several structures, where gaps in the main structural template are covered by local structural templates. This one-to-many (1:N) alignment is used to create a protein model by existing protein-modeling techniques. Several alternative approaches were evaluated using a set of ten proteins. One approach was selected and evaluated using another set of 31 proteins. The most promising result was for gap regions not located at the C-terminus or N-terminus of a protein, where the method produced an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant.

关键词： protein structure prediction loop modeling fold recognition threading structurally variable regions

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loop modeling, SEQUENCE AND STRUCTURE BASED MUTATIONAL ANALYSIS AND MOLECULAR DOCKING OF CLASS C-GPCR PROTEIN (GRM 3)

LOOP MODELING, SEQUENCE AND STRUCTURE BASED MUTATIONAL ANALY...

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作者： Harichandan, Rutuparna Punjab Agricultural University

学位级别：硕士

Recent studies suggest that agonists of group II metabotropic glutamate (mGlu) receptors (mGlu2/3) have potential utility as novel therapeutic agents for treatment of psychiatric disorders such as anxiety, Bipolar disorder and schizophrenia. Agonists of mGlu2/3 receptors block amphetamine- and phencyclidine (PCP)-induced hyperlocomotor activity in rodents, two actions that may predict potential antipsychotic activity of these compounds. We focused on the metabotropic glutamate receptor type 3 gene (GRM3) which is a candidate for schizophrenia and Bipolar disorder susceptibility. In this study the normal and mutated protein structures are prepared through discovery studio. The mutations were analysed using several computational tools out of which the most damaging disease causing SNPs were considered to construct the mutant protein. We have used Dcgiv (full Agonist), LY341495 (Antagonist), MGS0039 (Antagonist), NAAG (full Agonist) etc. as the selective drugs found from Guide to Pharmacologyto study their interaction pattern with wild and mutant protein *** our study, LY341495 acts as a potent and selective orthosteric antagonist for the group II metabotropic glutamate receptors (mGlu2/3).The finding could be utility as a novel approach for the treatment of schizophrenia and Bipolar Disorder.

关键词： Metabotropic glutamate receptor class C-GPCR protein Sequence based mutation Structure based mutation loop modeling Molecular Docking

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Effective protein model structure refinement by loop modeling and overall relaxation

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2016年第S1期84卷 293-301页

作者： Lee, Gyu Rie Heo, Lim Seok, Chaok Seoul Natl Univ Dept Chem Seoul 151747 South Korea

Protein structures predicted by state-of-the-art template-based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. loop modeling is the most useful when the initial model structure is high quality, with GDT-HA >60. The method that used multiple initial structures further refined the already refined models;the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. (C) 2015 Wiley Periodicals, Inc.

关键词： CASP refinement loop modeling ANM MD relaxation scoring function

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Protein loop modeling Using Deep Generative Adversarial Network 29

Protein Loop Modeling Using Deep Generative Adversarial Netw...

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29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Li, Zhaoyu Nguyen, Son P. Xu, Dong Shang, Yi Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Bond Life Sci Ctr Dept Elect Engn & Comp Sci Columbia MO USA

ISBN: (纸本)9781538638767

Biology and medicine have a long-standing interest in computational structure prediction and modeling of proteins. There are often missing regions or regions that need to be remodeled in protein structures. The process of predicting particular missing regions in a protein structure is called loop modeling. In this paper, we propose a generative adversarial network (GAN) in deep learning for loop modeling using the idea of image inpainting. The generative network is to capture the context of the loop region and predict the missing area. The adversarial network is to make the prediction look real and provide gradients to the generative network. The proposed network was evaluated on a common benchmark for loop modeling. Experiments show that our method can successfully predict the loop region and has achieved better performance than the state-of-the-art tools. To our knowledge, this work represents the first attempt of using GAN for any bioinformatics studies.

关键词： protein structure prediction loop modeling deep learning generative adversarial network

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Current approaches to flexible loop modeling

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CURRENT RESEARCH IN STRUCTURAL BIOLOGY 2021年 3卷 187-191页

作者： Barozet, Amelie Chacon, Pablo Cortes, Juan Univ Toulouse CNRS LAAS CNRS Toulouse France CSIC Dept Biol Phys Chem Rocasolano Phys Chem Inst Madrid Spain

loops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology.

关键词： loop modeling Protein flexibility Conformational sampling Structure prediction Energy landscapes

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Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency

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BRIEFINGS IN BIOINFORMATICS 2024年第1期25卷 bbad486页

作者： Wang, Tianyue Wang, Langcheng Zhang, Xujun Shen, Chao Zhang, Odin Wang, Jike Wu, Jialu Jin, Ruofan Zhou, Donghao Chen, Shicheng Liu, Liwei Wang, Xiaorui Hsieh, Chang-Yu Chen, Guangyong Pan, Peichen Kang, Yu Hou, Tingjun Zhejiang Univ Coll Pharmaceut Sci Hangzhou Peoples R China NYU New York NY USA Zhejiang Univ Coll Life Sci Hangzhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Huawei Technol Co Ltd Labs 2012 Shenzhen Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Macau Peoples R China Zhejiang Lab Hangzhou Peoples R China Zhejiang Univ Coll Pharmaceut Sci 446 Yuhangtang Rd 866 Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Coll Pharmaceut Sci 205 Yuhangtang Rd 866 Hangzhou 310058 Zhejiang Peoples R China

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

关键词： protein loop loop modeling deep learning AlphaFold2 artificial intelligence

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Application of biasing-potential replica-exchange simulations for loop modeling and refinement of proteins in explicit solvent

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2010年第13期78卷 2809-2819页

作者： Kannan, Srinivasaraghavan Zacharias, Martin Jacobs Univ Bremen Sch Sci & Engn D-28759 Bremen Germany

Comparative or homology modeling of a target protein based on sequence similarity to a protein with known structure is widely used to provide structural models of proteins. Depending on the target-template similarity these model structures may contain regions of limited structural accuracy. In principle, molecular dynamics (MD) simulations can be used to refine protein model structures and also to model loop regions that connect structurally conserved regions but it is limited by the currently accessible simulation time scales. A recently developed biasing potential replica exchange (BP-REMD) method was used to refine loops and complete decoy protein structures at atomic resolution including explicit solvent. In standard REMD simulations several replicas of a system are run in parallel at different temperatures allowing exchanges at preset time intervals. In a BP-REMD simulation replicas are controlled by various levels of a biasing potential to reduce the energy barriers associated with peptide backbone dihedral transitions. The method requires much fewer replicas for efficient sampling compared with T-REMD. Application of the approach to several protein loops indicated improved conformational sampling of backbone dihedral angle of loop residues compared to conventional MD simulations. BP-REMD refinement simulations on several test cases starting from decoy structures deviating significantly from the native structure resulted in final structures in much closer agreement with experiment compared to conventional MD simulations.

关键词： conformational sampling molecular dynamics simulation loop modeling refinement protein structure prediction

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Protein loop modeling by using fragment assembly and analytical loop closure

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2010年第16期78卷 3428-3436页

作者： Lee, Julian Lee, Dongseon Park, Hahnbeom Coutsias, Evangelos A. Seok, Chaok Soongsil Univ Dept Bioinformat & Life Sci Seoul 156743 South Korea Univ Calif San Francisco Dept Pharmaceut Chem San Francisco CA 94158 USA Seoul Natl Univ Dept Chem Seoul 151747 South Korea Univ New Mexico Dept Math & Stat Albuquerque NM 87131 USA

Protein loops are often involved in important biological functions such as molecular recognition, signal transduction, or enzymatic action. The three dimensional structures of loops can provide essential information for understanding molecular mechanisms behind protein functions. In this article, we develop a novel method for protein loop modeling, where the loop conformations are generated by fragment assembly and analytical loop closure. The fragment assembly method reduces the conformational space drastically, and the analytical loop closure method finds the geometrically consistent loop conformations efficiently. We also derive an analytic formula for the gradient of any analytical function of dihedral angles in the space of closed loops. The gradient can be used to optimize various restraints derived from experiments or databases, for example restraints for preferential interactions between specific residues or for preferred backbone angles. We demonstrate that the current loop modeling method outperforms previous methods that employ residue-based torsion angle maps or different loop closure strategies when tested on two sets of loop targets of lengths ranging from 4 to 12.

关键词： loop modeling protein structure prediction fragment assembly method analytical loop closure loop ensemble

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