Combining Coarse-Grained Protein Models with Replica-Exchange All-Atom Molecular Dynamics

We describe a combination of all-atom simulations with CABS, a well-established coarse-grained protein modeling tool, into a single multiscale protocol. The simulation method has been tested on the C-terminal beta hairpin of protein G, a model system of protein folding. After reconstructing atomistic details, conformations derived from the CABS simulation were subjected to replica-exchange molecular dynamics simulations with OPLS-AA and AMBER99sb force fields in explicit solvent. Such a combination accelerates system convergence several times in comparison with all-atom simulations starting from the extended chain conformation, demonstrated by the analysis of melting curves, the number of native-like conformations as a function of time and secondary structure propagation. The results strongly suggest that the proposed multiscale method could be an efficient and accurate tool for high-resolution studies of protein folding dynamics in larger systems.Comment: 12 pages, 4 figure

InSiDDe: A server for designing artificial disordered proteins

InSiDDe (In Silico Disorder Design) is a program for the in silico design of intrinsically disordered proteins of desired length and disorder probability. The latter is assessed using IUPred and spans values ranging from 0.55 to 0.95 with 0.05 increments. One to ten artificial sequences per query, each made of 50 to 200 residues, can be generated by InSiDDe. We describe the rationale used to set up InSiDDe and show that an artificial sequence of 100 residues with an IUPred score of 0.6 designed by InSiDDe could be recombinantly expressed in E. coli at high levels without degradation when fused to a natural molecular recognition element (MoRE). In addition, the artificial fusion protein exhibited the expected behavior in terms of binding modulation of the specific partner recognized by the MoRE. To the best of our knowledge, InSiDDe is the first publicly available software for the design of intrinsically disordered protein (IDP) sequences. InSiDDE is publicly available online

New encouraging developments in contact prediction: Assessment of the CASP11 results

This article provides a report on the state-of-the-art in the prediction of intra-molecular residue-residue contacts in proteins based on the assessment of the predictions submitted to the CASP11 experiment. The assessment emphasis is placed on the accuracy in predicting long-range contacts. Twenty-nine groups participated in contact prediction in CASP11. At least eight of them used the recently developed evolutionary coupling techniques, with the top group (CONSIP2) reaching precision of 27% on target proteins that could not be modeled by homology. This result indicates a breakthrough in the development of methods based on the correlated mutation approach. Successful prediction of contacts was shown to be practically helpful in modeling three-dimensional structures; in particular target T0806 was modeled exceedingly well with accuracy not yet seen for ab initio targets of this size (>250 residues

PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins

This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin

Prediction of protein-protein interaction types using association rule based classification

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Park et alBackground: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Results: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. Conclusion: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/SHP was supported by the Korea Research Foundation Grant funded by the Korean Government(KRF-2005-214-E00050). JAR has been supported by the Programme Alβan, the European Union Programme of High level Scholarships for Latin America, scholarship E04D034854CL. SK was supported by Soongsil University Research Fund

Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks

Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS) systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known) interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15~25% of the TCS proteins could participate in out-of-operon "crosstalks". Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.Comment: Supplementary information available on http://www.plosone.org/article/info:doi/10.1371/journal.pone.001972