分子对接,molecular docking,音标,读音,翻译,英文例句,英语词典
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1)&&molecular docking
tPSODock:A program for molecular docking using chemscore method and two - layer particl
tPSODock:基于化学得分函数和两层粒子群算法的计算机分子对接程序
Drug molecular docking design based on optimal
药物分子对接中的构象搜索策略
A novel anti-influenza drug:molecular docking of trih
新型抗流感病毒药物——三羟基甲氧基黄酮分子对接的研究
2)&&docking
[英][d?k]&&[美][dɑk]
Prediction of the Interaction of HIV-1 Integrase and Inhibitor Aurintricarboxylic Acid Using D
用分子对接方法预测HIV-1整合酶与金精三羧酸抑制剂的相互作用
QSAR and docking studies on a new series of diaryl-substituted-1,2,4-trizole de-rivatives COX-2
新型二芳基取代-1，2，4-三唑类化合物的抗炎构效关系和分子对接研究
Binding Mode Study of Streptolydigin, an HIV-1 Protease Inhibitor via Docking M
HIV-1蛋白酶抑制剂—利迪链菌素的分子对接研究
3)&&molecule docking
So the purpose of molecule docking is to get the conformation in the lowest free energy state.
分子对接方法目前已经成为研究分子间作用关系的重要方法，蛋白质-配体小分子对接方法经过多年的发展已经广泛应用于实际研究中，近年来蛋白质-蛋白质对接的方法也取得了一定的突破。
Under such situation, many theoretical methods and application software have presented in protein structure analysis and prediction and molecule docking.
随着计算机辅助分子模拟技术的蓬勃发展,计算生物学越来越多地应用于生命科学各个领域,包括蛋白质结构分析、预测和分子对接的理论方法和应用软件。
[英][d?k]&&[美][dɑk]
A Quantum and Docking Study of Substituted D
取代脱氧脲苷的量子化学计算及分子对接
3D-QSAR and docking study of a series of 16α-substituted e
16α取代雌二醇衍生物的三维定量构效关系和分子对接研究
Based on the reported crystal structure of complexes of the enzyme ketol-acid reductoisomerase (KARI), 279 molecules were obtained with predicted high affinity for KARI from MDL/ACD 3D-database searching, using program DOCK 4.
以酮醇酸还原异构酶KARI复合物 0 165nm高分辨率晶体结构为基础 ,采用DOCK 4 0分子对接程序通过MDL/ACD三维数据库搜寻 ,找到了 2 79个与KARI结合能较低的小分子 ,讨论了能量打分较高分子同靶酶的作用模式 。
5)&&macromolecular docking
大分子对接
The interaction between Parasporin-2 and its receptor,GPI-anchored protein(CD59),was simulated using macromolecular docking procedures Hex4.
通过大分子对接程序Hex4。
6)&&Multiple docking
多重分子对接
补充资料：高分子分子设计
分子式：CAS号：性质：指根据需要合成具有指定性能或功能的高分子材料。一般包括：(1)研究组成、结构和性能(或功能)之间的关系，找出定性、定量关系。这里所指的结构不仅包括分子结构、大分子结构，还包括超分子结构以及通过填充、共混、复合等形成的复杂结构。对聚集态的研究和设计显得格外重要。(2)按需要合成具有指定链结构的高聚物。这里的链结构包括定链节单元、定聚合度、定枝化度和定向、定序、定交联点等。(3)研究在加工成型时，按需要产生一定的聚集态结构、高次结构以及与成型条件、工艺参数的内在联系和相互关系。(4)高分子材料科学和现代信息处理技术相互结合，开发高分子材料分子设计软件、计算机辅助合成路线选择软件、计算机辅助材料选择的专家系统以及建设高分子材料数据库等。此外，正在推进分子和原子一级水平设计和合成高分子材料的研究。&
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nginx/1.4.1From Wikipedia, the free encyclopedia
Docking glossary
or host or lock
The "receiving" , most commonly a
or other .
or guest or key
The complementary partner molecule which
to the receptor. Ligands are most often
but could also be another biopolymer.
Computational simulation of a candidate ligand binding to a receptor.
Binding mode
The orientation of the ligand relative to the receptor as well as the
of the ligand and receptor when bound to each other.
A candidate binding mode.
The process of evaluating a particular pose by counting the number of favorable
The process of classifying which ligands are most likely to interact favorably to a particular receptor based on the predicted
of binding.
Schematic diagram illustrating the docking of a
(brown) to a
(green) to produce a complex.
docked to a .
In the field of , docking is a method which predicts the preferred orientation of one molecule to a second when
to each other to form a stable . Knowledge of the preferred orientation in turn may be used to predict the strength of association or
between two molecules using, for example, .
The associations between biologically relevant molecules such as , , , and
play a central role in . Furthermore, the relative orientation of the two interacting partners may affect the type of signal produced (e.g.,
vs ). Therefore docking is useful for predicting both the strength and type of signal produced.
Docking is frequently used to predict the binding orientation of
candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the . Given the biological and
significance of molecular docking, considerable efforts have been directed towards improving the methods used to predict docking.
One can think of molecular docking as a problem of “lock-and-key”, in which one wants to find the correct relative orientation of the “key” which will open up the “lock” (where on the surface of the lock is the key hole, which direction to turn the key after it is inserted, etc.). Here, the protein can be thought of as the “lock” and the ligand can be thought of as a “key”. Molecular docking may be defined as an optimization problem, which would describe the “best-fit” orientation of a ligand that binds to a particular protein of interest. However, since both the ligand and the protein are flexible, a “hand-in-glove” analogy is more appropriate than “lock-and-key”. During the course of the docking process, the ligand and the protein adjust their conformation to achieve an overall "best-fit" and this kind of conformational adjustment resulting in the overall binding is referred to as "induced-fit".
Molecular docking research focusses on computationally simulating the
process. It aims to achieve an optimized conformation for both the protein and ligand and relative orientation between protein and ligand such that the
of the overall system is minimized..
Two approaches are particularly popular within the molecular docking community. One approach uses a matching technique that describes the protein and the ligand as complementary surfaces. The second approach simulates the actual docking process in which the ligand-protein pairwise interaction energies are calculated. Both approaches have significant advantages as well as some limitations. These are outlined below.
Geometric matching/ shape complementarity methods describe the protein and ligand as a set of features that make them dockable. These features may include
descriptors. In this case, the receptor’s molecular surface is described in terms of its
and the ligand’s molecular surface is described in terms of its matching surface description. The complementarity between the two surfaces amounts to the shape matching description that may help finding the complementary pose of docking the target and the ligand molecules. Another approach is to describe the hydrophobic features of the protein using turns in the main-chain atoms. Yet another approach is to use a Fourier shape descriptor technique. Whereas the shape complementarity based approaches are typically fast and robust, they cannot usually model the movements or dynamic changes in the ligand/ protein conformations accurately, although recent developments allow these methods to investigate ligand flexibility. Shape complementarity methods can quickly scan through several thousand ligands in a matter of seconds and actually figure out whether they can bind at the protein’s active site, and are usually scalable to even protein-protein interactions. They are also much more amenable to pharmacophore based approaches, since they use geometric descriptions of the ligands to find optimal binding.
Simulating the docking process as such is much more complicated. In this approach, the protein and the ligand are separated by some physical distance, and the ligand finds its position into the protein’s active site after a certain number of “moves” in its conformational space. The moves incorporate rigid body transformations such as translations and rotations, as well as internal changes to the ligand’s structure including torsion angle rotations. Each of these moves in the conformation space of the ligand induces a total energetic cost of the system. Hence, the system's total energy is calculated after every move.
The obvious advantage of docking simulation is that ligand flexibility is easily incorporated, whereas shape complementarity techniques must use ingenious methods to incorporate flexibility in ligands. Also, it more accurately models reality, whereas shape complimentary techniques are more of an abstraction.
Clearly, simulation is computationally expensive, having to explore a large energy landscape. Grid-based techniques, optimization methods, and increased computer speed have made docking simulation more realistic.
To perform a docking screen, the first requirement is a structure of the protein of interest. Usually the structure has been determined using a biophysical technique such as , or . This protein structure and a database of potential ligands serve as inputs to a docking program. The success of a docking program depends on two components: the
Main article:
in theory consists of all possible orientations and
of the protein paired with the ligand. However in practice with current computational resources, it is impossible to exhaustively explore the search space—this would involve enumerating all possible distortions of each molecule (molecules are dynamic and exist in an ensemble of conformational states) and all possible
and translational orientations of the ligand relative to the protein at a given level of . Most docking programs in use account for a flexible ligand, and several attempt to model a flexible protein receptor. Each "snapshot" of the pair is referred to as a pose.
A variety of conformational search strategies have been applied to the ligand and to the receptor. These include:
systematic or
searches about rotatable bonds
simulations
to "evolve" new low energy conformations
Conformations of the ligand may be generated in the absence of the receptor and subsequently docked or conformations may be generated on-the-fly in the presence of the receptor binding cavity, or with full rotational flexibility of every dihedral angle using fragment based docking.
energy evaluation are most often used to select energetically reasonable conformations, but knowledge-based methods have also been used.
Computational capacity has increased dramatically over the last decade making possible the use of more sophisticated and computationally intensive methods in computer-assisted drug design. However, dealing with receptor flexibility in docking methodologies is still a thorny issue. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. Neglecting it, however, leads to poor docking results in terms of binding pose prediction.
Multiple static structures experimentally determined for the same protein in different conformations are often used to emulate receptor flexibility. Alternatively
of amino acid side chains that surround the binding cavity may be searched to generate alternate but energetically reasonable protein conformations.
Main article:
The scoring function takes a pose as input and returns a number indicating the likelihood that the pose represents a favorable binding interaction.
Most scoring functions are physics-based
that estimate th a low (negative) energy indicates a stable system and thus a likely binding interaction. An alternative approach is to derive a statistical potential for interactions from a large database of protein-ligand complexes, such as the , and evaluate the fit of the pose according to this inferred potential.
There are a large number of structures from
for complexes between proteins and high affinity ligands, but comparatively fewer for low affinity ligands as the later complexes tend to be less stable and therefore more difficult to crystallize. Scoring functions trained with this data can dock high affinity ligands correctly, but they will also give plausible docked conformations for ligands that do not bind. This gives a large number of
hits, i.e., ligands predicted to bind to the protein that actually don't when placed together in a test tube.
One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using (potentially) more accurate but computationally more intensive techniques such as
A binding interaction between a
ligand and an
protein may result in activation or
of the enzyme. If the protein is a receptor, ligand binding may result in
or . Docking is most commonly used in the field of
— most drugs are small
molecules, and docking may be applied to:
hit identification – docking combined with a
can be used to quickly screen large databases of potential drugs
to identify molecules that are likely to bind to protein target of interest (see ).
lead optimization – docking can be used to predict in where and in which relative orientation a ligand binds to a protein (also referred to as the binding mode or pose). This information may in turn be used to design more potent and selective analogs.
– Protein ligand docking can also be used to predict pollutants that can be degraded by enzymes.
The number of docking programs currently available is high and has been steadily increasing over the last decades. The following list presents an overview of the most common protein-ligand docking programs, listed alphabetically, with indication of the corresponding year of publication and country of origin. This list is comprehensive but not complete.
Country of Origin
Year Published
AutoDock Vina
South Korea
DockVision
Switzerland
Hammerhead
Lead finder
LigDockCSA
South Korea
PSO@AUTODOCK
South Korea
SOFTDocking
Lengauer T, Rarey M (1996). "Computational methods for biomolecular docking". Curr. Opin. Struct. Biol. 6 (3): 402–6. :.  .
Kitchen DB, Decornez H, Furr JR, Bajorath J (2004). "Docking and scoring in virtual screening for drug discovery: methods and applications". Nature reviews. Drug discovery 3 (11): 935–49. :.  .
Jorgensen WL (1991). "Rusting of the lock and key model for protein-ligand binding". Science 254 (5034): 954–5. :.  .
Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004). "Testing a flexible-receptor docking algorithm in a model binding site". J. Mol. Biol. 337 (5): 1161–82. :.  .
Goldman BB, Wipke WT (2000). "QSD quadratic shape descriptors. 2. Molecular docking using quadratic shape descriptors (QSDock)". Proteins 38 (1): 79–94. :.  .
Meng EC, Shoichet BK, Kuntz ID (2004). "Automated docking with grid-based energy evaluation". Journal of Computational Chemistry 13 (4): 505–524. :.
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998). "Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function". Journal of Computational Chemistry 19 (14): . :.
Feig M, Onufriev A, Lee MS, Im W, Case DA, Brooks CL (2004). "Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures". Journal of Computational Chemistry 25 (2): 265–84. :.  .
Shoichet BK, Kuntz ID, Bodian DL (2004). "Molecular docking using shape descriptors". Journal of Computational Chemistry 13 (3): 380–397. :.
Cai W, Shao X, Maigret B (January 2002). "Protein-ligand recognition using spherical harmonic molecular surfaces: towards a fast and efficient filter for large virtual throughput screening". J. Mol. Graph. Model. 20 (4): 313–28. :.  .
Morris RJ, Najmanovich RJ, Kahraman A, Thornton JM (May 2005). "Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons". Bioinformatics 21 (10): 2347–55. :.  .
Kahraman A, Morris RJ, Laskowski RA, Thornton JM (April 2007). "Shape variation in protein binding pockets and their ligands". J. Mol. Biol. 368 (1): 283–301. :.  .
Kearsley SK, Underwood DJ, Sheridan RP, Miller MD (October 1994). "Flexibases: a way to enhance the use of molecular docking methods". J. Comput. Aided Mol. Des. 8 (5): 565–82. :.  .
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (March 2004). "Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy". J. Med. Chem. 47 (7): 1739–49. :.  .
Zsoldos Z, Reid D, Simon A, Sadjad SB, Johnson AP (July 2007). "eHiTS: A new fast, exhaustive flexible ligand docking system". Journal of Molecular Graphics and Modelling 26 (1): 198–212. :.  .
Wang Q, Pang YP (2007). Romesberg, Floyd, ed. . PLoS ONE 2 (9): e820. :.  .  .
Klebe G, Mietzner T (October 1994). "A fast and efficient method to generate biologically relevant conformations". J. Comput. Aided Mol. Des. 8 (5): 583–606. :.  .
Cerqueira NM, Bras NF, Fernandes PA, Ramos MJ (January 2009). "MADAMM: a multistaged docking with an automated molecular modeling protocol". Proteins 74 (1): 192–206. :.  .
Totrov M, Abagyan R (April 2008). . Curr. Opin. Struct. Biol. 18 (2): 178–84. :.  .  .
Hartmann C, Antes I, Lengauer T (February 2009). "Docking and scoring with alternative side-chain conformations". Proteins 74 (3): 712–26. :.  .
Taylor RD, Jewsbury PJ, Essex JW (October 2003). "FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function". J Comput Chem 24 (13): 1637–56. :.  .
Suresh PS, Kumar A, Kumar R, Singh VP (January 2008). "An in silico [correction of insilico] approach to bioremediation: laccase as a case study". J. Mol. Graph. Model. 26 (5): 845–9. :.  .
Sousa SF, Ribeiro AJ, Coimbra JTS, Neves RPP, Martins SA, Moorthy HNS, Fernandes PA, Ramos MJ (January 2013). "Protein-ligand docking in the new millennium - A Retrospective of 10 years in the field". Curr. Med. Chem. 20 (5): . :.  .
- Simplest online docking interface
Bikadi Z, Kovacs S, Demko L, Hazai E. . Virtua Drug Ltd. Internet service that calculates the site, geometry and energy of small molecules interacting with proteins
Malinauskas T. .
Malinauskas T. .
for Debian
Project of Conformational Sampling and Docking on Grids : one aim is to deploy some intrinsic distributed docking algorithms on computational Grids, download
- Directory of computational drug design tools.
- a complete solution for ligand-receptor docking
- prediction of ligand induced conformational changes in receptor active sites.}