Structure-based Prediction of Protein-protein Interaction Networks across Proteomes

Protein-protein interactions (PPIs) orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. Significant efforts have been devoted to expand the coverage of the proteome-wide interaction space at molecular level. A number of experimental techniques have been developed to discover PPIs, however these approaches have some limitations such as the high costs and long times of experiments, noisy data sets, and often high false positive rate and inter-study discrepancies. Given experimental limitations, computational methods are increasingly becoming important for detection and structural characterization of PPIs. In that regard, we have developed a novel pipeline for high-throughput PPI prediction based on all-to-all rigid body docking of protein structures. We focus on two questions, ‘how do proteins interact?’ and ‘which proteins interact?’. The method combines molecular modeling, structural bioinformatics, machine learning, and functional annotation data to answer these questions and it can be used for genome-wide molecular reconstruction of protein-protein interaction networks. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Further, we validated our method against a few human pathways. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques

Predicting protein interface residues using easily accessible on-line resources

© The Author 2015. Published by Oxford University Press. It has beenmore than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we re- view 10methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experi- mental structures and high-quality homology models, structure-basedmethods outperformthose using only protein se- quences, with global template-based approaches providing the best performance. Formoderate-qualitymodels, sequence- basedmethods often performbetter than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in severalmethods quantitatively improve the results only for experimental struc- tures, suggesting that these procedures should be tuned up for computer-generatedmodels. Finally, we anticipate that advancedmeta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improve- ments, easily accessible web servers already provide the scientific community with convenient resources for the identifica- tion of protein-protein interaction sites

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

© 2015 Maheshwari and Brylinski. Background: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. Results: To address this problem, we developed eRankPPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRankPPI employs multiple features including interface probability estimates calculated by eFindSitePPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRankPPI consistently outperforms state-of-the-art algorithms improving the success rate by ∼10 %. Conclusions: eRankPPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/eRankPPI

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

Abstract Background Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved. Results In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. Conclusions Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques

Additional file 1: of Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

A text file containing binary interactions predicted for E. coli proteins. (ZIP 1940 kb

Anaesthetic Management of Laparoscopic Adrenalectomy for Adrenocortical Tumour in a Paediatric Patient ? A Case Report

Adrenocortical tumors are rare in children. An 8 months, 8kg child with Cushingoid features with persistent severe hypertension was admitted in pediatric surgery ward.Blood pressure was controlled by ACE inhibitors & calcium channel blockers. Diagnosis of adrenal tumor was confirmed by abdominal CT Scan. We report the management of this patient posted for laparoscopy adrenalectom

Diagnostic utility of fine needle aspiration cytology in pediatric tumors

<b>Background and Aims:</b> Fine needle aspiration cytology (FNAC) is a relatively new technique for the diagnosis of pediatric tumors. Most of the studies conducted so far have dealt only with malignant neoplasms or neoplasms of a particular organ/organ system in the pediatric population. Our work included a comprehensive study of both benign and malignant tumors in children younger than 15 years of age to correlate their clinical, cytological, and histological findings and to evaluate the effectiveness of FNAC in their diagnosis. <b> Materials and Methods:</b> We studied 588 cases over a period of ten years. Data was collected retrospectively as well as prospectively, and included all patients younger than 15 years of age, who presented with tumors or associated symptoms. Clinical, cytological, and histopathological correlations were done. <b> Results:</b> Benign soft tissue tumors formed the largest group among all pediatric tumors (41.5&#x0025;). Lymphomas were the most common (25.1&#x0025;) of all malignant tumors, followed closely by small round cell tumors (SRCTs, 21.3&#x0025;). FNAC was performed in 342 (55.1&#x0025;) cases, cyto-histological correlation was possible in 226 (38.4&#x0025;) cases; and a concordant diagnosis was seen in 218 (37.1&#x0025;) cases, giving an overall diagnostic accuracy of 96.46&#x0025; with FNAC. Occasional rare cases like Dabska&#x2032;s tumor and intraabdominal desmoplastic small round cell tumor could also be diagnosed by FNAC. <b> Conclusions:</b> We conclude that FNAC is an effective method for the evaluation and screening of pediatric masses, as well as for follow-up of patients with a history of malignancy