Timothy Nugent

Bio: Timothy Nugent is an academic researcher from Thomson Reuters. The author has contributed to research in topics: Protein structure prediction & Transmembrane protein. The author has an hindex of 21, co-authored 36 publications receiving 3123 citations. Previous affiliations of Timothy Nugent include University College London & Queen Mary University of London.

Scalable web services for the PSIPRED Protein Analysis Workbench

Abstract: Here, we present the new UCL Bioinformatics Group’s PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.

Transmembrane protein topology prediction using support vector machines.

Abstract: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/ . The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

Protein annotation and modelling servers at University College London

Abstract: The UCL Bioinformatics Group web portal offers several high quality protein structure prediction and function annotation algorithms including PSIPRED, pGenTHREADER, pDomTHREADER, MEMSAT, MetSite, DISOPRED2, DomPred and FFPred for the prediction of secondary structure, protein fold, protein structural domain, transmembrane helix topology, metal binding sites, regions of protein disorder, protein domain boundaries and protein function, respectively. We also now offer a fully automated 3D modelling pipeline: BioSerf, which performed well in CASP8 and uses a fragment-assembly approach which placed it in the top five servers in the de novo modelling category. The servers are available via the group web site at http://bioinf.cs.ucl.ac.uk/.

Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis

Abstract: A new de novo protein structure prediction method for transmembrane proteins (FILM3) is described that is able to accurately predict the structures of large membrane proteins domains using an ensemble of two secondary structure prediction methods to guide fragment selection in combination with a scoring function based solely on correlated mutations detected in multiple sequence alignments. This approach has been validated by generating models for 28 membrane proteins with a diverse range of complex topologies and an average length of over 300 residues with results showing that TM-scores > 0.5 can be achieved in almost every case following refinement using MODELLER. In one of the most impressive results, a model of mitochondrial cytochrome c oxidase polypeptide I was obtained with a TM-score > 0.75 and an rmsd of only 5.7 Å over all 514 residues. These results suggest that FILM3 could be applicable to a wide range of transmembrane proteins of as-yet-unknown 3D structure given sufficient homologous sequences.

Improving data transparency in clinical trials using blockchain smart contracts.

Abstract: The scientific credibility of findings from clinical trials can be undermined by a range of problems including missing data, endpoint switching, data dredging, and selective publication. Together, these issues have contributed to systematically distorted perceptions regarding the benefits and risks of treatments. While these issues have been well documented and widely discussed within the profession, legislative intervention has seen limited success. Recently, a method was described for using a blockchain to prove the existence of documents describing pre-specified endpoints in clinical trials. Here, we extend the idea by using smart contracts - code, and data, that resides at a specific address in a blockchain, and whose execution is cryptographically validated by the network - to demonstrate how trust in clinical trials can be enforced and data manipulation eliminated. We show that blockchain smart contracts provide a novel technological solution to the data manipulation problem, by acting as trusted administrators and providing an immutable record of trial history.

Tissue-based map of the human proteome

Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

SignalP 4.0: discriminating signal peptides from transmembrane regions

Abstract: We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions

Classification of intrinsically disordered regions and proteins.

Abstract: 1.1. Uncharacterized Protein Segments Are a Source of Functional Novelty Over the past decade, we have observed a massive increase in the amount of information describing protein sequences from a variety of organisms.1,2 While this may reflect the diversity in sequence space, and possibly also in function space,3 a large proportion of the sequences lacks any useful function annotation.4,5 Often these sequences are annotated as putative or hypothetical proteins, and for the majority their functions still remain unknown.6,7 Suggestions about potential protein function, primarily molecular function, often come from computational analysis of their sequences. For instance, homology detection allows for the transfer of information from well-characterized protein segments to those with similar sequences that lack annotation of molecular function.8−10 Other aspects of function, such as the biological processes proteins participate in, may come from genetic- and disease-association studies, expression and interaction network data, and comparative genomics approaches that investigate genomic context.11−17 Characterization of unannotated and uncharacterized protein segments is expected to lead to the discovery of novel functions as well as provide important insights into existing biological processes. In addition, it is likely to shed new light on molecular mechanisms of diseases that are not yet fully understood. Thus, uncharacterized protein segments are likely to be a large source of functional novelty relevant for discovering new biology.

Template-based protein structure modeling using the RaptorX web server

Abstract: A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX ( http://raptorx.uchicago.edu/ ) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ∼35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ∼6,000 sequences submitted by ∼1,600 users from around the world.

Coronavirus envelope protein: current knowledge

Abstract: Coronaviruses (CoVs) primarily cause enzootic infections in birds and mammals but, in the last few decades, have shown to be capable of infecting humans as well. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and, more recently, Middle-East respiratory syndrome (MERS) has demonstrated the lethality of CoVs when they cross the species barrier and infect humans. A renewed interest in coronaviral research has led to the discovery of several novel human CoVs and since then much progress has been made in understanding the CoV life cycle. The CoV envelope (E) protein is a small, integral membrane protein involved in several aspects of the virus’ life cycle, such as assembly, budding, envelope formation, and pathogenesis. Recent studies have expanded on its structural motifs and topology, its functions as an ion-channelling viroporin, and its interactions with both other CoV proteins and host cell proteins. This review aims to establish the current knowledge on CoV E by highlighting the recent progress that has been made and comparing it to previous knowledge. It also compares E to other viral proteins of a similar nature to speculate the relevance of these new findings. Good progress has been made but much still remains unknown and this review has identified some gaps in the current knowledge and made suggestions for consideration in future research. The most progress has been made on SARS-CoV E, highlighting specific structural requirements for its functions in the CoV life cycle as well as mechanisms behind its pathogenesis. Data shows that E is involved in critical aspects of the viral life cycle and that CoVs lacking E make promising vaccine candidates. The high mortality rate of certain CoVs, along with their ease of transmission, underpins the need for more research into CoV molecular biology which can aid in the production of effective anti-coronaviral agents for both human CoVs and enzootic CoVs.