Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “docking”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18Linked to original sources
PubMed1999

Identification of the docked granule pool responsible for the first phase of glucose-stimulated insulin secretion.

The mechanisms underlying the first phase of glucose-stimulated insulin release, the deterioration of which marks the early stages of both type 1 and type 2 diabetes, are essentially unknown. Among many hypotheses, one holds that the first phase is due to a readily releasable pool of insulin-containing granules. We used current knowledge of the mechanisms of exocytosis and the proteins involved in docking granules at the plasma membrane to test this hypothesis. A docked pool of readily releasable granules was identified by immunoprecipitation of the plasma membrane protein syntaxin with a specific antibody and by co-immunoprecipitation of soluble N-ethylmaleimide-sensitive factor attachment protein-25 (SNAP-25) and the granule proteins synaptobrevin and synaptotagmin. The four SNARE proteins co-immunoprecipitated each other, thus identifying the core complex associated with docked granules. Using co-immunoprecipitation as a marker for docked granules, we found that the docked pool was rapidly discharged during the first phase of glucose-stimulated insulin release and refilled during the second phase. Other secretagogues also released the pool, whereas the physiological inhibitor norepinephrine blocked its release. Further studies on the nature of this pool of granules should shed light on the causes of its deterioration in the early stages of diabetes and the reasons for deficient insulin release.

Animals↗
PubMed2005

Effects of castration, tooth resection, or tail docking on plasma metabolites and stress hormones in young pigs.

Changes in the activity of the sympathetic nervous system or in the hypothalamo-pituitary-adrenal axis have been extensively used to evaluate pain induced by castration or tail docking in numerous species. Such data are missing in pigs. Therefore, three experiments were conducted to determine the effects of castration, tail docking, or tooth resection on stress hormones. Glucose and lactate also were measured because catecholamines stimulate mobilization of glycogen, which results in glucose and lactate release. In Exp.1, 18 male pigs from seven litters (two or three pigs per litter) were catheterized surgically into one jugular vein, under general anesthesia, at 5 or 6 d of age. Two days later, they were submitted either to bilateral castration, control handling, or no handling (n = 6 per group). Blood samples were collected before (- 15 and -2 min) and after (5, 15, 30, 60, 90, 180 min) the experimental treatment. In Exp. 2, 27 female pigs from 12 litters (one to four pigs per litter) were submitted either to tooth clipping with pliers, tooth resection with a grinder apparatus, control handling, or no handling (n = 6 or 7 per group) at 1 d of age. In Exp. 3, 17 female pigs from nine litters (one to three pigs per litter) were submitted to one of the following treatments: 1) tail docking with an electric-heated scissor docking iron, 2) control handling, and 3) no handling (n = 5 or 6 per group) at 1 d of age. Castration induced significant (P < 0.05) increases in adrenocorticotropin hormone (ACTH; from 5 to 60 min), cortisol (from 15 to 90 min), and lactate (from 5 to 30 min). These variations are indicative of stress and tissue damage following castration. In contrast, neither tail docking nor tooth resection had marked effects on plasma cortisol, ACTH, glucose, and lactate. Measurements of plasma cortisol, lactate, and ACTH could be useful for validating treatments designed to relieve the distress reaction induced by castration in pigs.

Adrenocorticotropic Hormone↗
PubMed2000

BiGGER: a new (soft) docking algorithm for predicting protein interactions.

A new computationally efficient and automated "soft docking" algorithm is described to assist the prediction of the mode of binding between two proteins, using the three-dimensional structures of the unbound molecules. The method is implemented in a software package called BiGGER (Bimolecular Complex Generation with Global Evaluation and Ranking) and works in two sequential steps: first, the complete 6-dimensional binding spaces of both molecules is systematically searched. A population of candidate protein-protein docked geometries is thus generated and selected on the basis of the geometric complementarity and amino acid pairwise affinities between the two molecular surfaces. Most of the conformational changes observed during protein association are treated in an implicit way and test results are equally satisfactory, regardless of starting from the bound or the unbound forms of known structures of the interacting proteins. In contrast to other methods, the entire molecular surfaces are searched during the simulation, using absolutely no additional information regarding the binding sites. In a second step, an interaction scoring function is used to rank the putative docked structures. The function incorporates interaction terms that are thought to be relevant to the stabilization of protein complexes. These include: geometric complementarity of the surfaces, explicit electrostatic interactions, desolvation energy, and pairwise propensities of the amino acid side chains to contact across the molecular interface. The relative functional contribution of each of these interaction terms to the global scoring function has been empirically adjusted through a neural network optimizer using a learning set of 25 protein-protein complexes of known crystallographic structures. In 22 out of 25 protein-protein complexes tested, near-native docked geometries were found with C(alpha) RMS deviations < or =4.0 A from the experimental structures, of which 14 were found within the 20 top ranking solutions. The program works on widely available personal computers and takes 2 to 8 hours of CPU time to run any of the docking tests herein presented. Finally, the value and limitations of the method for the study of macromolecular interactions, not yet revealed by experimental techniques, are discussed.

Algorithms↗
PubMed2025

Investigating the molecular mechanism of Yangxin decoction in treating major depressive disorder using network pharmacology and molecular docking technology approaches.

Yangxin decoction has been used to treat major depressive disorder (MDD). This study aims to identify the active components and potential mechanisms of Yangxin decoction in treating MDD using network pharmacology and molecular docking technology. The active components and targets of Yangxin decoction were screened, and MDD-related targets were predicted. Networks of "herbal medicine-active components-potential targets" and protein-protein interaction were constructed. Core components and core targets were identified through network topology analysis. Gene ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on candidate genes. Molecular docking was conducted using AutoDock software (Olson Laboratory of the Scripps Research Institute, San Diego) to explore the interactions between core targets and active components, and the results were visualized using PyMOL (DeLano Scientific LLC, South San Francisco). A total of 433 active components and 392 targets of Yangxin decoction were identified, along with 11,796 MDD-related targets. There were 680 overlapping targets between Yangxin decoction and MDD, associated with 104 active components. Core targets identified through network topology analysis and molecular docking included serine/threonine kinase 1 (AKT1), tumor necrosis factor, interleukin-6, tumor protein P53, and proto-oncogene tyrosine-protein kinase Src. Gene ontology enrichment analysis revealed 1606 biological processes, 191 cellular components, and 373 molecular functions. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified 212 signaling pathways, with significant enrichment in caffeine metabolism, bladder cancer, advanced glycation end products-receptor for advanced glycation end products signaling pathway in diabetic complications, and vascular endothelial growth factor signaling pathway. Molecular docking results showed strong binding energy between core active components and core targets. Yangxin decoction exhibits multi-component, multi-pathway, and multi-target therapeutic characteristics. It primarily regulates targets such as AKT1, tumor necrosis factor, interleukin-6, tumor protein P53, and proto-oncogene tyrosine-protein kinase Src through advanced glycation end products-receptor for advanced glycation end products, vascular endothelial growth factor, and ErbB signaling pathways, exerting anti-inflammatory, immune-regulating, and oxidative stress-inhibiting effects to alleviate MDD.

Molecular Docking Simulation↗
PubMed2026

Analysis of the molecular mechanism underlying di(2-ethylhexyl) phthalate-induced bladder carcinogenesis via network toxicology and molecular docking approaches: An observational study.

This study aims to investigate the toxicity of di(2-ethylhexyl) phthalate (DEHP) and the potential molecular mechanisms of DEHP-induced bladder cancer (BLCA) using network toxicology and molecular docking strategies. The toxicity of DEHP was assessed using Prox-II software, and potential targets for DEHP-induced BLCA were identified by integrating data from ChEMBL database, Search Tool for Interactions of Chemicals, SwissTargetPrediction, GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, and The Cancer Genome Atlas. STRING database and Cytoscape were employed to construct target networks and determine core targets. The expression levels of core targets were analyzed using R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on potential and core targets. Molecular docking was carried out using CB-Dock 2 to verify the interactions between DEHP and core targets. A total of 105 potential targets related to DEHP-induced BLCA were identified, from which 7 core targets were selected: cyclin-dependent kinase 1, interleukin 6, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, cyclin B2, and B-cell lymphoma 2. IL-6 and B-cell lymphoma 2 showed downregulated expression in tumor tissues, while cyclin-dependent kinase 1, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, and cyclin B2 were upregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these targets were enriched in cell signaling and cancer-related pathways. Molecular docking confirmed that DEHP interacts with these core targets. DEHP may promote the development of BLCA by interacting with key proteins and signaling pathways. This study provides a theoretical basis for understanding the molecular mechanisms of DEHP-induced BLCA and offers references for future prevention and treatment strategies.

Diethylhexyl Phthalate↗
PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗
PubMed2026

A Network Pharmacology and Molecular Docking Study of TongBi Formula for Osteoarthritis.

This study applied network pharmacology combined with molecular docking to predict the potential therapeutic targets and molecular mechanisms of TongBi Formula (TBF) in osteoarthritis (OA). Active components and corresponding targets of TBF were retrieved from the traditional Chinese medicine Systems Pharmacology Database and Analysis Platform, while OA-related targets were collected from Online Mendelian Inheritance in Man, GeneCards, DrugBank, and Therapeutic Target Database. A network visualization and analysis software was used to construct compound-target and protein-protein interaction (PPI) networks. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery platform. Molecular docking analysis was conducted using a molecular docking software to evaluate the predicted binding affinity between key active compounds and core target proteins. A total of 47 overlapping targets between TBF and OA were identified. PPI network analysis highlighted JUN, RELA, IL6, MAPK1, and IL10 as potential hub targets. Enrichment analysis suggested that TBF may regulate inflammation, lipid metabolism, and multiple intracellular signaling pathways associated with OA progression. Molecular docking results demonstrated favorable predicted binding affinities between core active compounds and key OA-related protein targets. These findings provide a computational framework for understanding the potential mechanisms of TBF against OA and support further experimental validation.

Molecular Docking Simulation↗
PubMed2026

Integrated Network Pharmacology and Molecular Docking Analysis of Sishen Decoction Identifies Potential Targets and Pathways in Gout.

Gout is a disease characterized by hyperuricemia and the deposition of urate crystals in joints and soft tissues, leading to recurrent acute arthritis. Its increasing prevalence imposes substantial clinical and socioeconomic burdens. Sishen Decoction (SSD) has been used in the treatment of gout, but its potential molecular mechanisms remain unclear. This study applied an integrated network pharmacology and molecular docking approach to identify potential targets and signaling pathways associated with SSD in gout. Active compounds and corresponding targets of SSD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), while gout-related targets were collected from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Overlapping targets were identified and used to construct a drug-component-target-disease network. A protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed, followed by molecular docking using the docking server analysis module. A total of 37 bioactive compounds were associated with 116 overlapping gout-related targets. The top hub targets included TP53, IL6, IL1B, TNF, AKT1, EGFR, CASP3, JUN, BCL2, and MMP9. GO analysis suggested that these targets are involved in gene expression regulation and signal transduction. KEGG enrichment analysis indicated significant associations with the mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), interleukin-17 (IL-17), and tumor necrosis factor (TNF) signaling pathways. Molecular docking predicted favorable interactions between key compounds and hub targets, with all binding energies of &#x2264;-5 kcal/mol. These computational findings provide potential mechanistic hypotheses for the action of SSD in gout and may support future experimental validation.

Molecular Docking Simulation↗
PubMed1997

Automated docking of monosaccharide substrates and analogues and methyl alpha-acarviosinide in the glucoamylase active site.

Glucoamylase is an important industrial glucohydrolase with a large specificity range. To investigate its interaction with the monosaccharides D-glucose, D-mannose, and D-galactose and with the substrate analogues 1-deoxynojirimycin, D-glucono-1,5-lactone, and methyl alpha-acarviosinide, MM3(92)-optimized structures were docked into its active site using AutoDock 2.1. The results were compared to structures of glucoamylase complexes obtained by protein crystallography. Charged forms of some substrate analogues were also docked to assess the degree of protonation possessed by glucoamylase inhibitors. Many forms of methyl alpha-acarviosinide were conformationally mapped by using MM3(92), characterizing the conformational pH dependence found for the acarbose family of glucosidase inhibitors. Their significant conformers, representing the most common states of the inhibitor, were used as initial structures for docking. This constitutes a new approach for the exploration of binding modes of carbohydrate chains. Docking results differ slightly from x-ray crystallographic data, the difference being of the order of the crystallographic error. The estimated energetic interactions, even though agreeing in some cases with experimental binding kinetics, are only qualitative due to the large approximations made by AutoDock force field.

1-Deoxynojirimycin↗
PubMedDate not supplied

Molecular docking of superantigens with class II major histocompatibility complex proteins.

The molecular recognition of two superantigens with class II major histocompatibility complex molecules was simulated by using protein-protein docking. Superantigens studied were staphylococcal enterotoxin B (SEB) and toxic shock syndrome toxin-1 (TSST-1) in their crystallographic assemblies with HLA-DR1. Rigid-body docking was performed sampling configurational space of the interfacial surfaces by employing a strategy of partitioning the contact regions on HLA-DR1 into separate molecular recognition units. Scoring of docked conformations was based on an electrostatic continuum model evaluated with the finite-difference Poisson-Boltzmann method. Estimates of nonpolar contributions were derived from the buried molecular surface areas. We found for both superantigens that docking the HLA-DR1 surface complementary with the SEB and TSST-1 contact regions containing a homologous hydrophobic surface loop provided sufficient recognition for the reconstitution of native-like conformers exhibiting the highest-scoring free energies. For the SEB complex, the calculations were successful in reproducing the total association free energy. A comparison of the free-energy determinants of the conserved hydrophobic contact residue indicates functional similarity between the two proteins for this interface. Though both superantigens share a common global association mode, differences in binding topology distinguish the conformational specificities underlying recognition.

Algorithms↗
PubMed2001

Design, docking, and evaluation of multiple libraries against multiple targets.

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

Amino Acids↗
PubMed2001

High throughput docking for library design and library prioritization.

The prioritization of the screening of combinatorial libraries is an extremely important task for the rapid identification of tight binding ligands and ultimately pharmaceutical compounds. When structural information for the target is available, molecular docking is an approach that can be used for prioritization. Here, we present the initial validation of a new rapid approach to molecular docking developed for prioritizing combinatorial libraries. The algorithm is tested on 103 individual cases from the protein data bank and in nearly 90% of these cases docks the ligand to within 2.0 A of the observed binding mode. Because the mean CPU time is <5 s/mol, this approach can process hundreds of thousands of compounds per week. Furthermore, if a somewhat less thorough search is performed, the search time drops to 1 s/mol, thus allowing millions of compounds to be docked per week and tested for potential activity. Proteins 2001;43:113-124.

Binding Sites↗
PubMed2004

A hybrid method of molecular dynamics and harmonic dynamics for docking of flexible ligand to flexible receptor.

We have developed a new docking method to consider receptor flexibility, a hybrid method of molecular dynamics and harmonic dynamics. The global motions of the whole receptor were approximately introduced into those of the receptor in the docking simulation as harmonic dynamics. On the other hand, the local flexibility of the side chains was also considered by conventional molecular dynamics. We confirmed that this new method can reproduce the fluctuations of the whole receptor by making a comparison of the directions and amplitudes of the global fluctuations. Then this method was applied to the docking of HIV-1 protease and its ligand. As a result, we observed a docking process where the ligand enters into the binding pocket well, which implies that this method is effective enough to reproduce a molecular complex formation.

Algorithms↗
PubMed2005

A scoring function for docking ligands to low-resolution protein structures.

We present a docking method that uses a scoring function for protein-ligand docking that is designed to maximize the docking success rate for low-resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high- or low-resolution protein structures. We show that this docking method can be successfully applied to predict the ligand-binding site of low-resolution structures. For a set of 25 protein-ligand complexes, in 76% of the cases, more than 50% of ligand-contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 A RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand-contacting residue. Furthermore, using an 81-protein-ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand-contacting residues when native protein structures are used. Using 3 A RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand-binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein-ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater.

Algorithms↗
PubMed1997

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.

Algorithms↗
PubMed1996

Docking enzyme-inhibitor complexes using a preference-based free-energy surface.

We present a docking scheme that utilizes both a surface complementarity screen as well as an energetic criterion based on surface area burial. Twenty rigid enzyme/inhibitor complexes with known coordinate sets are arbitrarily separated and reassembled to an average all-atom rms (root mean square) deviation of 1.0 A from the native complexes. Docking is accomplished by a hierarchical search of geometrically compatible triplets of surface normals on each molecule. A pruned tree of possible bound configurations is built up using successive consideration of larger and larger triplets. The best scoring configurations are then passed through a free-energy screen where the lowest energy member is selected as the predicted native state. The free energy approximation is derived from observations of surface burial by atom pairs across the interface of known enzyme/inhibitor complexes. The occurrence of specific atom-atom surface burial, for a set of complexes with well-defined secondary structure both in the bound and unbound states, is parameterized to mimic the free energy of binding. The docking procedure guides the inhibitor into its native state using orientation and distance-dependent functions that reproduce the ideal model of free energies with an average rms deviation of 0.9 kcal/mol. For all systems studied, this docking procedure identifies a single, unique minimum energy configuration that is highly compatible with the native state.

Enzyme Inhibitors↗
PubMed2002

Docking multiple conformations of a flexible ligand into a protein binding site using NMR restraints.

A method is described for docking a large, flexible ligand using intra-ligand conformational restraints from exchange-transferred NOE (etNOE) data. Numerous conformations of the ligand are generated in isolation, and a subset of representative conformations is selected. A crude model of the protein-ligand complex is used as a template for overlaying the selected ligand structures, and each complex is conformationally relaxed by molecular mechanics to optimize the interaction. Finally, the complexes were assessed for structural quality. Alternative approaches are described for the three steps of the method: generation of the initial docking template; selection of a subset of ligand conformations; and conformational sampling of the complex. The template is generated either by manual docking using interactive graphics or by a computational grid-based search of the binding site. A subset of conformations from the total number of peptides calculated in isolation is selected based on either low energy and satisfaction of the etNOE restraints, or a cluster analysis of the full set. To optimize the interactions in the complex, either a restrained Monte Carlo-energy minimization (MCM) protocol or a restrained simulated annealing (SA) protocol were used. This work produced 53 initial complexes of which 8 were assessed in detail. With the etNOE conformational restraints, all of the approaches provide reasonable models. The grid-based approach to generate an initial docking template allows a large volume to be sampled, and as a result, two distinct binding modes were identified for a fifteen-residue peptide binding to an enzyme active site.

Anion Exchange Protein 1, Erythrocyte↗
PubMed2002

Protein--protein docking of electron transfer complexes: cytochrome c oxidase and cytochrome c.

Electron transferring protein complexes form only transiently and the crystal structures of electron transfer protein--protein complexes involving cytochrome c could so far be determined only for the pairs of yeast cytochrome c peroxidase (CcP) with iso-1-cytochrome c (iso-1-cyt c) and with horse heart cytochrome c (cyt c). This article presents models from computational docking for complexes of cytochrome c oxidase (COX) from Paracoccus denitrificans with horse heart cytochrome c, and with its physiological counterpart cytochrome c552 (c552). Initial docking is performed with the FTDOCK program, which permits an exhaustive search of translational and rotational space. A filtering procedure is then applied to reduce the number of complexes to a manageable number. In a final step of structural and energetic refinement, the complexes are optimized by rigid-body energy minimization with the molecular mechanics package CHARMM. This methodology was first tested on the CcP:iso-1-cyt c complex, in which the complex with the lowest CHARMM energy has an RMSD from the crystal structure of only 1.8 A (C(alpha) carbon atoms). Notably, the crystal conformation has an even lower energy. The same procedure was then applied to COX:cyt c and COX:c552. The lowest-energy COX:cyt c complex is very similar to a docking model previously described for the complex of bovine cytochrome c oxidase with horse heart cytochrome c. For the COX:c552 complex, cytochrome c552 is found in two different orientations, depending on whether it is docked against COX from a two-subunit or from a four-subunit crystal structure, respectively. Both conformations are discussed critically in the light of the available experimental data.

Algorithms↗