Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 6 Articles
Background: The development of accurate protein-protein docking programs is making this kind of simulations an\r\neffective tool to predict the 3D structure and the surface of interaction between the molecular partners in\r\nmacromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a\r\nconsequence, the accurate and tedious screening of many docking models is usually required in the analysis step.\r\nMethods: All the programs under CONS-COCOMAPS have been written in python, taking advantage of python\r\nlibraries such as SciPy and Matplotlib. CONS-COCOMAPS is freely available as a web tool at the URL:\r\nhttp://www.molnac.unisa.it/BioTools/conscocomaps/.\r\nResults: Here we presented CONS-COCOMAPS, a novel tool to easily measure and visualize the consensus in\r\nmultiple docking solutions. CONS-COCOMAPS uses the conservation of inter-residue contacts as an estimate of the\r\nsimilarity between different docking solutions. To visualize the conservation, CONS-COCOMAPS uses intermolecular\r\ncontact maps.\r\nConclusions: The application of CONS-COCOMAPS to test-cases taken from recent CAPRI rounds has shown that it\r\nis very efficient in highlighting even a very weak consensus that often is biologically meaningful....
Background: Many functionally important proteins in a cell form complexes with multiple chains. Therefore,\r\ncomputational prediction of multiple protein complexes is an important task in bioinformatics. In the development\r\nof multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a\r\nreasonable and practical fashion. However, since there are only few works done in developing methods for\r\nmultiple protein docking, there is no study that investigates how accurate structural models of multiple protein\r\ncomplexes should be to allow scientists to gain biological insights.\r\nMethods: We generated a series of predicted models (decoys) of various accuracies by our multiple protein\r\ndocking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in\r\nterms of the number of correctly predicted pair conformations in the decoys.\r\nResults and conclusion: We found that pairs of chains with the correct mutual orientation exist even in the\r\ndecoys with a large overall root mean square deviation (RMSD) to the native. Therefore, in addition to a global\r\nstructure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be\r\nbetter evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We\r\ntermed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Ã?â?¦) as fpair and propose to\r\nuse it for evaluation of the accuracy of multiple protein docking....
The present study reports the results of a combined computational and site mutagenesis study designed to provide new insights\r\ninto the orthosteric binding site of the human M3 muscarinic acetylcholine receptor. For this purpose a three-dimensional\r\nstructure of the receptor at atomic resolution was built by homology modeling, using the crystallographic structure of bovine\r\nrhodopsin as a template. Then, the antagonist N-methylscopolamine was docked in the model and subsequently embedded in a\r\nlipid bilayer for its refinement using molecular dynamics simulations. Two different lipid bilayer compositions were studied: one\r\ncomponent palmitoyl-oleyl phosphatidylcholine (POPC) and two-component palmitoyl-oleyl phosphatidylcholine/palmitoyloleyl\r\nphosphatidylserine (POPC-POPS). Analysis of the results suggested that residues F222 and T235 may contribute to the\r\nligand-receptor recognition. Accordingly, alanine mutants at positions 222 and 235 were constructed, expressed, and their binding\r\nproperties determined. The results confirmed the role of these residues in modulating the binding affinity of the ligand....
Background: Many important cellular processes are carried out by protein complexes. To provide physical pictures\r\nof interacting proteins, many computational protein-protein prediction methods have been developed in the past.\r\nHowever, it is still difficult to identify the correct docking complex structure within top ranks among alternative\r\nconformations.\r\nResults: We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface\r\nprediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on\r\ncases, the challenge is to develop a method which does not deteriorate but improves docking results by using a\r\nbinding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface\r\nwith Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction\r\nalgorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the\r\nprovided protein-protein binding interface prediction as constraints, which is followed by the second round of\r\ndocking with updated docking interface information to further improve docking conformation. Benchmark results\r\non bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as\r\ncompared with docking without using binding site prediction or using the binding site prediction as post-filtering.\r\nConclusion: We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein\r\nbinding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy\r\nover alternative methods in the series of benchmark experiments including docking using actual docking interface\r\nsite predictions as well as unbound docking cases....
Knowledge of protein targets of drugs is necessary in solving various problems in drug discovery process. A computer aided drug design to new drug development is striking alternative to the traditional paradigm of drug discovery through screening. The elements of this approach are reviewed, with emphasis on the use of homology-built model structures. QSAR is among the most comprehensively used in-silico method for analogue-based drug design. The use of various descriptor classes like, quantum chemical, conceptual density functional theory (DFT) molecular mechanics and docking-based descriptors in prediction of anti-cancer activity are well known. The QSAR has broadly been applied for the activity prediction of various series of biological and chemical compounds together with anticancer drugs. This article consists of review of QSAR studies which have been carried out with some newer anticancer agents which are potent and highly selective....
Background: Bed bugs (Cimex lectularius) are hematophagous nocturnal parasites of humans that have attained\r\nhigh impact status due to their worldwide resurgence. The sudden and rampant resurgence of C. lectularius has\r\nbeen attributed to numerous factors including frequent international travel, narrower pest management practices,\r\nand insecticide resistance.\r\nResults: We performed a next-generation RNA sequencing (RNA-Seq) experiment to find differentially expressed\r\ngenes between pesticide-resistant (PR) and pesticide-susceptible (PS) strains of C. lectularius. A reference\r\ntranscriptome database of 51,492 expressed sequence tags (ESTs) was created by combining the databases derived\r\nfrom de novo assembled mRNA-Seq tags (30,404 ESTs) and our previous 454 pyrosequenced database (21,088 ESTs).\r\nThe two-way GLMseq analysis revealed ~15,000 highly significant differentially expressed ESTs between the PR and\r\nPS strains. Among the top 5,000 differentially expressed ESTs, 109 putative defense genes (cuticular proteins,\r\ncytochrome P450s, antioxidant genes, ABC transporters, glutathione S-transferases, carboxylesterases and acetyl\r\ncholinesterase) involved in penetration resistance and metabolic resistance were identified. Tissue and\r\ndevelopment-specific expression of P450 CYP3 clan members showed high mRNA levels in the cuticle, Malpighian\r\ntubules, and midgut; and in early instar nymphs, respectively. Lastly, molecular modeling and docking of a\r\ncandidate cytochrome P450 (CYP397A1V2) revealed the flexibility of the deduced protein to metabolize a broad\r\nrange of insecticide substrates including DDT, deltamethrin, permethrin, and imidacloprid.\r\nConclusions: We developed significant molecular resources for C. lectularius putatively involved in metabolic\r\nresistance as well as those participating in other modes of insecticide resistance. RNA-Seq profiles of PR strains\r\ncombined with tissue-specific profiles and molecular docking revealed multi-level insecticide resistance in C.\r\nlectularius. Future research that is targeted towards RNA interference (RNAi) on the identified metabolic targets\r\nsuch as cytochrome P450s and cuticular proteins could lay the foundation for a better understanding of the\r\ngenetic basis of insecticide resistance in C. lectularius....
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