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PubMed1995

The role of three-state docking of myosin S1 with actin in force generation.

It has been shown that in solution myosin subfragment 1 binds to actin in three principal steps: [formula: see text] The nucleotide bound to myosin has a major influence on the equilibrium constant of the third of these steps but little effect on the other two. The third step is thought to be coupled to the force-generating event. Three-step binding and structure: The formation of the collision complex is strongly ionic strength dependent but independent of temperature. The isomerization to the A state is not strongly dependent on ionic strength but is affected by organic solvent and temperature. In contrast the isomerization to the R state-is affected by both ionic strength and organic solvent but little affected by temperature. The recent docking of the three-dimensional structures of actin and S1 suggest possible structural correlates of these events. These studies lead to predictions for the docking process, which may be tested using site-directed mutagenesis or peptide inhibitors. Three-step binding and head-head interactions: Studies of HMM binding to actin compared with S1 binding show that binding of two heads in the A state are unlikely presumably because of strain effects. However, binding of two heads as one A and one R state shows little evidence of strain while the isomerization of the second head to give two R states is fivefold weaker than for an isolated S1 head. These results suggest that in a rapidly shortening muscle only one head is likely to be attached at a time. Under isometric conditions, although it is possible for both heads to bind to adjacent actins, it is unlikely that both will be in the force holding R state simultaneously. Three-step binding and regulation by tropomyosin-troponin:Our recent solution studies have established that the thin filament can exist in three calcium-dependent states which we termed blocked, closed and open. A blocked state cannot form the A state with S1 and a closed state cannot form the force holding R state nor accelerate product release from S1. Thus control operates at two distinct points in the docking process. The docking process itself is coupled to hydrolysis of ATP (the A-to-R isomerization is inhibited by the presence of the gamma Pi on ATP), and therefore all of these events are interrelated.The coming together of these different strands provides a biochemical framework that should allow the dynamic properties of the crossbridge in muscle to be understood.

Actins↗

PubMed1997

ESCHER: a new docking procedure applied to the reconstruction of protein tertiary structure.

Evaluation of Surface Complementarity, Hydrogen bonding, and Electrostatic interaction in molecular Recognition (ESCHER) is a new docking procedure consisting of three modules that work in series. The first module evaluates the geometric complementarity and produces a set of rough solutions for the docking problem. The second module identifies molecular collisions within those solutions, and the third evaluates their electrostatic complementarity. We describe the algorithm and its application to the docking of cocrystallized protein domains and unbound components of protein-protein complexes. Furthermore, ESCHER has been applied to the reassociation of secondary and supersecondary structure elements. The possibility of applying a docking method to the problem of protein structure prediction is discussed.

Protein Structure, Secondary↗

PubMed2025

Integrated network pharmacology, molecular docking and experimental validation to investigate the mechanism of tannic acid in nasopharyngeal cancer.

Tannic acid (TA) is the primary bioactive component in the gallnut (Galla chinensis) and has exhibited the anticancer effects. However, the mechanism of its anti-cancer activity in nasopharyngeal carcinoma (NPC) remains unclear. This research aims to explore the underlying mechanism of TA in the treatment of nasopharyngeal cancer using network pharmacology, molecular docking and experimental validation. Firstly, the targets of TA and NPC were predicted and collected through databases, and the intersection targets were identified. Subsequently, protein-protein interaction (PPI) network analysis, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment analysis, molecular docking and molecular dynamics (MD) simulation were conducted to uncover the potential mechanisms of TA in treatment of NPC. Finally, in vitro experiments were utilized to verify the mechanism of TA with anticancer activity in NPC. The results of network pharmacology revealed 42 intersection targets between NPC-related targets and TA-related targets. The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling was identified as the main target pathway of TA against NPC. Additionally, molecular docking and MD simulation confirmed the closely binding affinities of TA with AKT1. Furthermore, the results of in vitro experiments demonstrated that TA exerts anticancer activity against NPC by targeting the PI3K/AKT signaling pathway, leading to the suppression of cell proliferation. TA is a promising therapeutic candidate for NPC through PI3K/AKT signaling pathway. These results provide insights into the clinical application of TA, particularly when considered in combination with other therapeutic modalities.

Molecular Docking Simulation↗

PubMed2025

Network pharmacology and molecular docking to explore the active compounds and mechanisms of Jerusalem artichoke for treating diabetes.

The effective components and mechanism of Jerusalem artichokes (JAs) in lowering blood glucose were studied through network pharmacology and molecular docking. The active compounds of Jerusalem artichoke were obtained by referring to the literature, and the active compounds were screened. The targets were predicted by the SwissTargetPrediction database, and the disease targets were screened using GeneCard, Disgenet, and OMIM databases. The protein-protein interaction (PPI) network diagram was constructed using the STRING database, and the intersection target was analyzed by gene ontology (GO) biological function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the David database. Finally, molecular docking was verified using AutoDockTools1.5.7 software. After screening, 412 gene targets, 476 disease targets, and 64 intersection targets were identified. The results of GO biological function analysis and KEGG pathway analysis showed that the technology was involved in multiple biological processes and regulatory pathways for hypoglycemia, such as the HIF-1, PI3K-Akt, and AMPK signaling pathways. Molecular docking results showed that Jasmonate, Liquiritigenin and Inulin of JAs had strong binding effects with PPARG and STAT3. JAs exert hypoglycemic effects through multi-component, multi-target and multi-pathway. In summary, this study investigated the hypoglycemic mechanism of JAs using network pharmacology and molecular interconnection technology, and concluded that JAs exert hypoglycemic effects through multiple components, targets, and pathways, which provides a theoretical basis for the study of JAs.

Molecular Docking Simulation↗

PubMed2025

Molecular mechanism of HaiZao-YuHu decoction in breast cancer treatment via network pharmacology and molecular docking: Computational pharmacology.

BACKGROUND: The molecular biological mechanisms of HaiZao-YuHu decoction were investigated using network pharmacology and molecular docking. METHODS: TCMSP database was used to collect the active ingredients and action targets of HaiZao-YuHu decoction, through the OMIM, PharmGkb, GeneCards, TDD, and DurgBank database query targets for breast cancer. Then, using the intersecting targets, the protein-protein interaction network of HaiZao-YuHu decoction was constructed using the STRING website. Network topology analysis was performed using Cytoscape 3.9.0 to identify the core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed with the R package. The Autodock software was used for molecular docking. RESULTS: Thirty-four active ingredients, 219 intersection targets and 4 key targets were obtained. gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis get 2152 biological processes and 186 pathways. Molecular docking showed that the 4 core targets could combine well with the 5 main active components. CONCLUSION: HaiZao-YuHu decoction can play a role in the treatment of breast cancer through multi-targets, multi-components, and multi-pathways.

Molecular Docking Simulation↗

PubMed2026

Elucidating the mechanism of Buyang Huanwu Decoction in the treatment of ischemic stroke: A network pharmacology and molecular docking study.

A large number of functional disorders and uncomfortable symptoms often remain following ischemic stroke (IS). Existing drug therapy is not ideal for the direct improvement of symptoms, which often leads to poor patient compliance with physical rehabilitation therapy. Buyang Huanwu Decoction (BYHWD) is a famous prescription that is effective in treating IS, especially during the sequela stage of IS. We analyzed the therapeutic mechanism of BYHWD through network pharmacology. This study aims to investigate the potential active ingredients, targets, and signaling pathways of BYHWD for the treatment of IS, utilizing network pharmacology and molecular docking technology. The active ingredients of 7 Chinese herbs in BYHWD were obtained from the Traditional Chinese Medicine Systems Pharmacology and HERB databases, and IS-related disease targets were searched in the DisGeNET, GeneCards, and OMIM databases. The protein-protein interaction network was constructed using the STRING database and analyzed by Cytoscape 3.10.2 software. Additionally, the target genes were uploaded to the Database for Annotation, Visualization, and Integrated Discovery website for Gene Ontology alongside Kyoto Encyclopedia of Genes and Genomes analyses. With the assistance of AutoDockTools and PyMOL software (Schrödinger, Inc.), a validation of molecular docking results and a visualization of the results were performed. The results showed that there were 190 intersection targets between the active drug components and IS, corresponding to 61 active components, among which the top 5 target genes were tumor suppressor protein 53, Jun proto-oncogene, AKT serine/threonine kinase 1, mitogen-activated protein kinase 1, and estrogen receptor alpha. The PI3K-Akt signaling pathway is one of the top 10 pathways. The molecular docking results indicated that most of the top 5 targets had good affinities for the 8 core compounds. This computational analysis suggests that BYHWD may treat IS through multiple targets and pathways. It may play a neuroprotective role by regulating the inflammatory response, oxidative stress, apoptosis, autophagy, and vascular endothelial homeostasis. The identification of core effective components provides a theoretical foundation and candidate compounds for further investigation into new drugs for the treatment of sequelae after IS.

Drugs, Chinese Herbal↗

PubMed2026

Exploring the treatment of liver cancer with Gehua Hugan Gao based on bioinformatics, network pharmacology, and molecular docking.

Gehua Hugan Gao (GHHGG) is a traditional Chinese medicine paste that is chiefly used to treat liver cancer. However, the potential impact of GHHGG on liver cancer remains unclear. We explored how GHHGG treats liver cancer using bioinformatics, network pharmacology, and molecular docking. Network pharmacology included GHHGG active ingredients, predicted targets, predicted targets for liver cancer, and differential gene collection. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and crucial targets were ranked according to their degree values. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of liver cancer targets were followed by survival, differential analysis, and molecular docking. Venn diagrams show 123 predicted GHHGG targets for the treatment of hepatocellular carcinoma (HCC). Enrichment analysis showed that GHHGG treats HCC through multiple targets and pathways. We also found that estrogen receptor 1, cytochrome P450 3A4, cyclin-dependent kinase 4, type IIA topoisomerase, aurora kinase A, and cyclin E1 targets were closely associated with HCC development through survival and differential analyses. Molecular docking confirmed GHHGG's strong affinity for liver cancer targets. This study helps us understand GHHGG ingredients and targets for liver cancer treatment. To a certain extent, the molecular mechanism of GHHGG in the treatment of liver cancer has been elucidated, thus providing a theoretical basis.

Molecular Docking Simulation↗

PubMed2026

Validating the potential mechanism and therapeutic effect of Qinlian Jiangxia decoction in the treatment of type 2 diabetes mellitus complicated with hyperlipidemia through network pharmacology, molecular docking, molecular dynamics simulation, andexperiments.

OBJECTIVE: To investigate the mechanism of action of Qinlian Jiangxia decoction (, QLJXD) in the treatment of type 2 diabetes mellitus (T2DM) complicated by hyperlipidemia using network pharmacology, molecular docking, molecular dynamics simulation and in vivo experiments. METHODS: Drug components, targets and disease targets were identified using databases such as TCM systems pharmacology database and analysis platform and GeneCards. The intersecting targets were subjected to protein-protein interaction analysis using the search tool for the retrieval of interacting genes/proteins database. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the intersecting targets were conducted using the Metascape platform to identify core components and targets. The results were validated using molecular docking, molecular dynamics simulations and in vivo experiments. RESULTS: QLJXD contains 76 active ingredients and 136 disease targets. The core ingredients are quercetin, β-sitosterol, wogonin and baicalein, while the core targets are fatty acid binding protein 4 (FABP4) and peroxisome proliferative activated receptor gamma (PPARG). Molecular docking and molecular dynamics simulations revealed that the core ingredients bound well to the core targets. Animal experiments demonstrated that QLJXD effectively inhibited the expression of FABP4 and increased the expression of PPARG, thereby enhancing disorders of glycolipid metabolism. CONCLUSION: The putative therapeutic efficacy of QLJXD in the management of T2DM complicated with hyperlipidemia may be ascribed to the synergistic actions of multiple components, such as quercetin, β-sitosterol, wogonin, and baicalein, which collectively modulate FABP4 and PPARG molecular targets.

Molecular Docking Simulation↗

PubMed2026

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

PubMed2026

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, this study aimed to clarify their association, identify the toxic targets of DEHP, and elucidate the underlying molecular mechanisms. METHODS: Multiple integrated approaches were employed, including Gene Expression Omnibus (GEO) data analysis, network toxicology, molecular docking, and machine learning. STRING and Cytoscape tools were utilized to identify key targets, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functional enrichment of intersecting targets. Machine learning and SHAP analysis were applied to screen core targets in GC. Molecular docking was performed to evaluate the binding affinity of DEHP toward core targets, and 200 ns molecular dynamics simulations were further conducted for representative complexes to validate their dynamic stability. RESULTS: A total of 18 key targets were identified using STRING and Cytoscape. GO and KEGG enrichment analyses demonstrated that these intersecting targets were primarily enriched in the extracellular region, as well as the Calcium signaling pathway and cAMP signaling pathway. Through machine learning analyses, 7 key genes (ADRB2, ESRRG, GRIA4, IL13RA2, NR3C2, PLA2G1B, and SULT2A1) were identified as core targets in GC through machine learning analyses. Molecular docking simulations revealed strong binding specificity between DEHP and the target proteins. Among them, NR3C2 and ADRB2 exhibited relatively high predictive importance in the machine learning models. DEHP showed favorable binding affinity toward these core targets, and molecular dynamics simulations further confirmed that ADRB2-DEHP and NR3C2-DEHP complexes maintained stable conformations throughout the simulation. CONCLUSIONS: Our findings identified GC associated genes that were computationally predicted as potential targets of DEHP. These results indicated structural compatibility between DEHP and its target proteins but did not prove that DEHP exposure accounts for the gene expression changes in GC.

Molecular Docking Simulation↗

PubMed2026

Exploring the mechanism of the Lianshi Jianpi formula in treating impaired glucose tolerance: a network pharmacology, molecular docking, and experimental validation study.

OBJECTIVE: To explore the bioactive constituents, key targets, signalling pathways, and molecular mechanisms of Lianshi Jianpi formula (, LSJPF) in the treatment of impaired glucose tolerance (IGT) through network pharmacology, molecular docking, and in vivo experiments. METHODS: The active ingredients and targets of LSJPF were identified using the Traditional Chinese Medicine Systems Pharmacology and HERB databases, whereas the IGT-related targets were sourced from GeneCards, DisGeNET, and PubMed. The overlap analysis identified potential targets of LSJPF. Protein-protein interaction networks and core targets were evaluated using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape, and molecular docking confirmed the binding affinities. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using Metascape. The therapeutic mechanisms were validated in an animal IGT model. RESULTS: LSJPF contained 229 compounds, with 15 active compounds and 77 potential target proteins. The phosphatidylinositol-3-kinase (PI3K)-protein kinase B (AKT) signalling pathway emerged as a key IGT pathway. The KEGG enrichment analysis revealed the pivotal genes RAC-alpha serine/threonine-protein kinase (AKT1), heat shock protein 90 kDa alpha B1, and B-cell lymphoma 2 family protein, which predominantly interact with beta-sitosterol and beta-carotene, the major constituents of Semen Euryales, Semen lablab Album, Semen sojae Atricolor in LSJPF. Molecular docking revealed strong binding affinities between LSJPF and IGT-related targets. In an animal IGT model, LSJPF treatment prevented weight loss; reduced food and water intake; decreased blood glucose levels; improved insulin resistance; decreased serum triglyceride, cholesterol, and low-density lipoprotein cholesterol levels; alleviated liver pathology; and significantly increased the levels of phosphorylated adenosine 5'-monophosphate-activated protein kinase (AMPK), PI3K, and AKT, suggesting its potential role in regulating glucose and lipid metabolism. CONCLUSIONS: These findings reveal the potential of LSJPF as an IGT intervention that targets the AMPK/PI3K/AKT cascade, validating network pharmacology predictions and highlighting the role of multipathway mechanisms in metabolic diseases.

Molecular Docking Simulation↗

PubMed1996

Exploring the energy landscapes of molecular recognition by a genetic algorithm: analysis of the requirements for robust docking of HIV-1 protease and FKBP-12 complexes.

Energy landscapes of molecular recognition are explored by performing "semi-rigid" docking of FK-506 and rapamycin with the Fukisawa binding protein (FKBP-12), and flexible docking simulations of the Ro-31-8959 and AG-1284 inhibitors with HIV-1 protease by a genetic algorithm. The requirements of a molecular recognition model to meet thermodynamic and kinetic criteria of ligand-protein docking simultaneously are investigated using a family of simple molecular recognition energy functions. The critical factor that determines the success rate in predicting the structure of ligand-protein complexes is found to be the roughness of the binding energy landscape, in accordance with a minimal frustration principle. The results suggest that further progress in structure prediction of ligand-protein complexes can be achieved by designing molecular recognition energy functions that generate binding landscapes with reduced frustration.

Algorithms↗

PubMed1997

Evaluation of the CASP2 docking section.

The docking section of CASP2 is reviewed. Seven small molecule ligand-protein targets and one protein-protein target were available for predictions. Many of the small molecule ligand complexes involved serine proteases. Overall results for the small molecule targets were good, with at least one prediction for each target being within 3 A root-mean-square deviation (RMSD) for nearly all targets and within 2 A RMSD for over half the targets. However, no single docking method seemed to consistently perform best. In addition, the predictions closest to the experimental results were not always those ranked the highest, pointing out that the evaluation (scoring) of potential solutions is still an area that needs improvement. The protein-protein target proved more difficult. None of the predictions did well in reproducing the geometry of the complex, although in many cases the interacting surfaces of the two proteins were predicted with reasonable accuracy. This target consisted of two large proteins and, therefore was a demanding target for docking methods.

Evaluation Studies as Topic↗

PubMed1999

New general approach for determining the solution structure of a ligand bound weakly to a receptor: structure of a fibrinogen Aalpha-like peptide bound to thrombin (S195A) obtained using NOE distance constraints and an ECEPP/3 flexible docking program.

A new approach incorporating flexible docking simulations and NMR data is presented for calculating the bound conformation of a ligand that interacts weakly with an enzyme. This approach consists of sampling directly the conformation of a flexible ligand inside a receptor active site containing surrounding flexible loops. To make this sampling efficient, a ligand-growing procedure has been adopted. Optimization of the ECEPP/3-plus-NOE constraint function is carried out by using a collective variable Monte Carlo minimization technique. Numerous energy minimizations are made possible for such a large system by using a Bezier splines energy grid technique. This new flexible docking approach was applied to determine the structure of a fibrinogen Aalpha-like peptide (7DFLAEGGGVRGPRV20) bound to an active site mutant of thrombin [thrombin(S195A)]. Structure calculations of the bound ligand, using 2D-transferred NOESY distance constraints in the DIANA program, showed that the N-terminal portion of the peptide (D7-R16) involves a chain reversal, whereas the C-terminal portion (G17-V20) adopts a fold that exists in several different orientations. In addition, the ECEPP/3 flexible docking package was used to assess the conformational variability of the ligand and surrounding 60D-insertion loop of thrombin. Amino acid residues (17-20) of the peptide interact with a region of the enzyme that exhibits broad specificity, with a preferred direction between the 60D-insertion loop and Pro37 of thrombin.

Animals↗

PubMed1999

Modelling of factor Xa-inhibitor complexes: a computational flexible docking approach.

In order to understand the structural basis of Factor Xa (FXa) specificity, structural complexes of FXa with its synthetic inhibitors are determined using a computational docking approach. The AutoDock suite of programs is used to determine the binding modes of the synthetic inhibitors such as 3- and 4-amidinobenzylphenyl ether (ABP), amidinophenyl pyruvic acid (APPA), diamidinobenzofuranyl ethene (DABE), and DX-9065a 2-(5'-amidino-2'-benzofuranyl)-3-(7'amidino-2'-napthyl)-propionic acid (ABAP) to FXa. The synthetic inhibitors docked in the present study are different in size, nature of linkage, and properties. Two sets of simulations were carried out for synthetic inhibitors docking to FXa. In the first set of simulations, no explicit water molecules were included. In the second set of simulations two explicit solvent molecules were considered. In all the computationally predicted synthetic inhibitor complexes of FXa, the specificity pocket residue Asp-189 is involved in hydrogen bonding with the bound inhibitor. The active site water molecule WAT522 is involved in hydrogen bonding with all the bound inhibitors. The computed energies clearly discriminate the high affinity from low affinity binders.

Amidines↗

PubMed1997

Centrosomal deployment of gamma-tubulin and pericentrin: evidence for a microtubule-nucleating domain and a minus-end docking domain in certain mouse epithelial cells.

This report provides evidence for two functionally and spatially distinct centrosomal domains in certain mouse cochlear epithelial cells. The vast majority of microtubules elongate from sites associated with the apical cell surface in these cells rather than from pericentriolar material surrounding the immediate environs of their apically situate centrioles. The distribution of gamma-tubulin and pericentrin at cell apices has been examined while microtubule nucleation is progressing because these centrosomal proteins are believed to be essential for microtubule nucleation. Antibodies to both proteins bind to pericentriolar regions but no binding has been detected at the apical cell surface-associated sites where the ends of thousands of recently nucleated microtubules are concentrated. Sparse transient microtubule populations can be detected between pericentriolar regions and surface sites while microtubule assembly advances. A procedure apparently operates in which the pericentriolar region functions as a microtubule-nucleating domain and the cell surface-associated sites operate as docking domains which capture the minus ends of microtubules that migrate to them shortly after nucleation. Docking domains may include some components of the pericentriolar material that have been relocated at the cell apex. A docking element hypothesis for centrosomal control of minus end positioning and dynamics in animal cells generally is proposed. This investigation has also shown that the concentration of gamma-tubulin and pericentrin around centrioles differs spatially and quantitatively in ways that are characteristic for the four cell types studied. Some of these characteristics can be related to differences in control of microtubule number and positioning.

Animals↗

PubMed1996

Low-resolution docking: prediction of complexes for underdetermined structures.

One of the most fundamental questions concerning ligand-receptor interaction is whether such a process of intermolecular association is generally determined by local structural elements of the participating molecules, or whether there are also large-scale motifs in molecule structures that facilitate complex formation. From the point of view of practical docking computations, the elaborate character of local structural details in ligand-receptor interaction creates a large number of false-positive matches, which interfere with determination of the best fit. Another significant obstacle in protein docking is the problem of structural data inaccuracy (poor structure resolution, conformational changes upon complex formation, etc.). Our study [Vakser (1995) Protein Eng., 8, 371-377], based on ultralow (approximately 7 A resolution) representation of molecular structures, allowes to average all high-resolution structural details, and still predict most of the structural features of the ligand-receptor complex. The approach dramatically improves the signal-to-noise ratio in determination of the best fit, and moves the structure inaccuracy tolerance to the range of the macrostructure. In the present paper, we describe a further validation of the main principles of this approach and a detailed analysis of the low-resolution docking results. This includes clustering of ligand positions around the receptor molecule and cross-validation of ligands and receptors from different complexes. We also discuss the important implications of the approach to the multiple-minima problem and a possible role of different structural elements in the recognition mechanism.

Animals↗

PubMed2001

Efficient electrostatic solvation model for protein-fragment docking.

A method is presented for the fast evaluation of the binding energy of a protein-small molecule complex with electrostatic solvation. It makes use of a fast preprocessing step based on the assumption that the main contribution to electrostatic desolvation upon ligand binding originates from the displacement of the first shell of water molecules. For a rigid protein, the precomputation of the energy contributions on a set of grids allows the estimation of the energy in solution of about 300 protein-fragment binding modes per second on a personal computer. The docking procedure is applied to five rigid binding sites whose size ranges from 17 residues to a whole protein of 107 amino acids. Using a library of 70 mainly rigid molecules, known micromolar inhibitors or close analogs are docked and prioritized correctly. The docking based rank-ordering of the library requires about 5 h and is proposed as a complementary approach to structure-activity relationships by nuclear magnetic resonance. Proteins 2001;42:256-268.

Binding Sites↗