SwarmDock: a server for flexible protein–protein docking

Torchala, Mieczyslaw; Moal, Iain H.; Chaleil, Raphaël A. G.; Fernández‐Recio, Juan; Bates, Paul A.

doi:10.1093/bioinformatics/btt038

Cited by 270 publications

(203 citation statements)

References 7 publications

Supporting

Mentioning

194

Contrasting

Unclassified

Order By: Relevance

“…However, the great majority of biological functions are mediated not by isolated proteins but by their interactions. In addition to predicting the correct geometry of protein-protein complexes (in 3D) from their unbound components, for which a number of fully automated servers now exist, see for example the SwarmDock server [20] or the ClusPro server [21], of equal importance is to study the dynamics of binding, i.e. how the binding partners, upon complex formation, sample the binding funnel.…”

Section: Resultsmentioning

confidence: 99%

“…Automatic generation of protein-protein conformational space networks (in RaTrav formatted files), for any protein receptor/ligand pair, was recently incorporated into our docking tool, the SwarmDock Server [20]. For this study, we chose the vitamin D-binding protein/actin complex (Brookhaven protein database code, 1KXP) [22], which was previously studied by us in terms of conformational occupation probabilities and their usefulness to filter away non-funnel like protein-protein energy structures, thus improving the ranking of the correct docking poses [23].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

RaTrav: a tool for calculating mean first-passage times on biochemical networks

et al. 2013

Self Cite

View full text Add to dashboard Cite

BackgroundThe concept of mean first-passage times (MFPTs) occupies an important place in the theory of stochastic processes, with the methods of their calculation being equally important in theoretical physics, chemistry and biology. We present here a software tool designed to support computational biology studies where Markovian dynamics takes place and MFPTs between initial and single or multiple final states in network-like systems are used. Two methods are made available for which their efficiency is strongly dependent on the topology of the defined network: the combinatorial Hill technique and the Monte Carlo simulation method.ResultsAfter a brief introduction to RaTrav, we highlight the utility of MFPT calculations by providing two examples (accompanied by Additional file 1) where they are deemed to be of importance: analysis of a protein-protein docking funnel and interpretation of the free energy transduction between two coupled enzymatic reactions controlled by the dynamics of transition between enzyme conformational states.ConclusionsRaTrav is a versatile and easy to use software tool for calculating MFPTs across biochemical networks. The user simply prepares a text file with the structure of a given network, along with some additional basic parameters such as transition probabilities, waiting probabilities (if any) and local times (weights of edges), which define explicitly the stochastic dynamics on the network. The RaTrav tool can then be applied in order to compute desired MFPTs. For the provided examples, we were able to find the favourable binding path within a protein-protein docking funnel and to calculate the degree of coupling for two chemical reactions catalysed simultaneously by the same protein enzyme. However, the list of possible applications is much wider.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

RaTrav: a tool for calculating mean first-passage times on biochemical networks

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…PDB file, 1BOQ (Giblin et al, 1998) was retrieved to dock the MC1R protein (54-105 region) of P. reticulata. The SwarmDock server was used for docking (Torchala et al, 2013). The modeled 3D structure was submitted to the coach server (Yang et al, 2013) to predict probable ligands.…”

Section: Docking and Ligand Predictionmentioning

confidence: 99%

<i>In Silico</i> Sequence Analysis, Structure Prediction and Function Annotation of Melanocortin 1 Receptor Gene (MC1R) from the Guppy <i>Poecilia reticulata</i>

Mohideen

Sheriff

Altaff

2015

American Journal of Biochemistry and Biotechnology

View full text Add to dashboard Cite

Abstract:The common guppy, Poecilia reticulata is one of the most popular aquarium fish used as a model organism in toxicology, embryology and genetical studies. Vibrant colors in males are a secondary sexual character which attracts the females for mating. Pigmentation in guppy is found to be linked with melanin synthesis. Melanocortin genes are involved in the synthesis of melanin pigments. Alteration in Melanocortin 1 Receptor (MC1R) genes results in polymorphism. Analysis of the MC1R gene of Poecilia reticulata revealed a 966 bp open reading frame encoding 322 amino acids. The deduced MC1R protein sequence was predicted to possess three domains: 7TM GPCR (7 transmembrane G-protein-coupled receptor) chemoreceptor Srsx at the N-terminal, 7 TM receptor and the G-proteincoupled chemokine receptor like protein domain at the C-terminal. Predicted mutations in the MC1R protein sequence shows Indel (Insertion and deletion) and block mutations. Phylogenetic analysis revealed that MC1R of Poecilia reticulata and other fish species were separated from other vertebrates. The 3D structure of the 54-105 region of MC1R protein predicted for the first time comprised of two helices namely α1 and α2 from amino acid residues 59-69 and 86-97 respectively. The extracellular region is composed of two loops from amino acid residues 54-58 at the N-terminal and 88-105 at C-terminal. The intracellular region of the receptor contains a beta turn from the amino acid residues 71-73 which connects the α1 and α2 helices by means of a loop. Docking of α-MSH, ligand (Ca 2+ ions and Cholesterol) interactions and the presence of CRAC domain (Cholesterol Recognition Amino acid Consensus sequence) within the TM region of receptor further reiterates the predicted structure has a definite functional role in pigmentation.

show abstract

“…One can consider unit A as the ClusPro [3] PyDock [15] GRAMMX [39] RosettaDock [25] ZDock [34] SymmDock [5] SwarmDock [38] HopDock [12] idDock [13] Figure 1: Several top methods, available as web servers, in pairwise docking (including our recent HopdDock [12] and idDock [13] methods) have been applied to obtain best models for a D2R-D2R assembly indicated to form in the living cell by wet-lab studies [7,10]. The transmembrane regions (TMs) are color coded in each of the two chains (TM1 red, TM2 dark grey, TM3 orange, TM4 yellow, TM5 tan, TM6 light grey, TM7 green.)…”

Section: Sampling-based Rigid-body Dockingmentioning

confidence: 99%

Knowledge-based search and multi-objective filters

Hashmi

Veltri

Kabbani

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

View full text Add to dashboard Cite

Many experimental studies point to the ubiquitous role of protein complexation in the cell while lamenting the lack of structural models to permit structure-function studies. This scarcity is due to persisting challenges in protein-protein docking. Methods based on energetic optimization have to handle vast and high-dimensional configuration spaces and inaccurate energy functions only to arrive at the wrong interface. Methods that employ learned models to replace or precede energetic evaluations are limited by the generality of these models. Computational approaches designed to be general often fail to provide realistic models on protein classes of interest in the wet laboratory. One such class are G protein-coupled receptors, which wet-lab studies suggest undergo complexation, possibly affecting drug efficacy. In this paper, we propose a computational protocol to address the unique challenges posed by these receptors. To deal with challenges, such as receptor size and inaccuracy of energy functions, the protocol takes a geometry-driven approach and integrates in the search geometric constraints posed by the environment where the receptors operate. Various filters are designed to handle the computational cost of energetic evaluation, and analysis techniques based on new scoring strategies, including multi-objective analysis, are employed to reduce the sampled ensemble to a few credible structural models. We demonstrate that dimeric models of the Dopamine D2 receptor targeted to treat psychotic disorders reproduce macroscopic knowledge extracted in the * Corresponding Author

show abstract

SwarmDock: a server for flexible protein–protein docking

Cited by 270 publications

References 7 publications

RaTrav: a tool for calculating mean first-passage times on biochemical networks

RaTrav: a tool for calculating mean first-passage times on biochemical networks

<i>In Silico</i> Sequence Analysis, Structure Prediction and Function Annotation of Melanocortin 1 Receptor Gene (MC1R) from the Guppy <i>Poecilia reticulata</i>

Knowledge-based search and multi-objective filters

Contact Info

Product

Resources

About