Bioinformatics Institute BII

Singapore

Bioinformatics Institute BII

Singapore

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Eisenhaber B.,Bioinformatics Institute BII | Eisenhaber S.,University of Vienna | Kwang T.Y.,Bioinformatics Institute BII | Gruber G.,Bioinformatics Institute BII | And 4 more authors.
Cell Cycle | Year: 2014

The transamidase subunit GAA1/GPAA1 is predicted to be the enzyme that catalyzes the attachment of the glycosylphosphatidyl (GPI) lipid anchor to the carbonyl intermediate of the substrate protein at the ω-site. Its ∼300-amino acid residue lumenal domain is a M28 family metallo-peptide- synthetase with an α/β hydrolase fold, including a central 8-strand β-sheet and a single metal (most likely zinc) ion coordinated by 3 conserved polar residues. Phosphoethanolamine is used as an adaptor to make the non-peptide GPI lipid anchor look chemically similar to the N terminus of a peptide. © 2014 Landes Bioscience.


De J.,Bioinformatics Institute BII | De J.,Nanyang Technological University | Ma T.,University of Tokyo | Li H.,Beijing Institute of Technology | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

As an early indication of diseases including diabetes, hypertension, and retinopathy of prematurity, structural study of retinal vessels becomes increasingly important. These studies have driven the need toward accurate and consistent tracing of retinal blood vessel tree structures from fundus images in an automated manner. In this paper we propose a two-step pipeline: First, the retinal vessels are segmented with the preference of preserving the skeleton network, i.e., retinal segmentation with a high recall. Second, a novel tracing algorithm is developed where the tracing problem is uniquely mapped to an inference problem in probabilistic graphical models. This enables the exploitation of well-developed inference toolkit in graphical models. The competitive performance of our method is verified on publicly available datasets comparing to the state-of-the-arts. © 2013 Springer-Verlag.


Jiang D.,National University of Singapore | Wong W.-C.,Bioinformatics Institute BII | Schwarz H.,National University of Singapore | Lim K.-H.,Singapore General Hospital | And 2 more authors.
European Journal of Immunology | Year: 2012

High macrophage infiltration into tumours often correlates with poor prognoses; in colorectal, stomach and skin cancers, however, the opposite is observed but the mechanisms behind this phenomenon remain unclear. Here, we sought to understand how tumour-associated macrophages (TAMs) in colorectal cancer execute tumour-suppressive roles. We found that TAMs in a colorectal cancer model were pro-inflammatory and inhibited the proliferation of tumour cells. TAMs also produced chemokines that attract T cells, stimulated proliferation of allogeneic T cells and activated type-1 T cells associated with anti-tumour immune responses. Using colorectal tumour tissues, we verified that TAMs in vivo were indeed pro-inflammatory. Furthermore, the number of tumour-infiltrating T cells correlated with the number of TAMs, suggesting that TAMs could attract T cells; and indeed, type-1 T cells were present in the tumour tissues. Patient clinical data suggested that TAMs exerted tumour-suppressive effects with the help of T cells. Hence, the tumour-suppressive mechanisms of TAMs in colorectal cancer involve the inhibition of tumour cell proliferation alongside the production of pro-inflammatory cytokines, chemokines and promoting type-1 T-cell responses. These new findings would contribute to the development of future cancer immunotherapies based on enhancing the tumour-suppressive properties of TAMs to boost anti-tumour immune responses. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Maurer-Stroh S.,Bioinformatics Institute BII | Lee R.T.C.,Bioinformatics Institute BII | Eisenhaber F.,Bioinformatics Institute BII | Cui L.,National Public Health Laboratory | Phuah S.P.,National Public Health Laboratory
PLoS Currents | Year: 2010

As the 2009 (H1N1) influenza A virus continues evolving, most mutations appear geographically and temporally confined. However, the latest surveillance data suggests emergence of a new prominent mutation, E391K, in the hemagglutinin (HA) that is globally on the rise. Interestingly, when modelled in the context of the available HA crystal structure, this mutation could alter salt bridge patterns and stability in a region of the HA oligomerization interface that is important for membrane fusion and also a known antigenic site. We discuss occurrence of HA-E391K in global surveillance data and associated clinical phenotypes from Singapore ranging from mostly mild to few severe symptoms, including sporadic vaccine failure. More clinical and experimental data are needed to determine if this mutation could alter the biology and fitness of the virus or if its increased occurrence is due to founder effects.


Namboodiri S.,Kerala University | Verma C.,Bioinformatics Institute BII | Dhar P.K.,Kerala University | Giuliani A.,Instituto Superiore Of Sanita | Nair A.S.,Kerala University
Communications in Computer and Information Science | Year: 2011

Recurrence Quantitative Analysis is a relatively new pattern recognition tool well suited for short, non-linear and non stationary systems. It is designed to detect recurrence patterns that are expressed as a set of Recurrence Quantification variables. In our work we made use of this tool on allosteric protein system to identify residues involved in the transmission of the structural rearrangements as an upshot of allostery. Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Here, we scrutinized the sequence landscape of 'ras' protein by partitioning its residues into windows of equal size. An 11 element characteristic vector, comprising of 10 features extracted from the Recurrence Quantification Analysis along with a feature relating to allosteric involvement, was defined for each windowed sequence set. By applying multivariate statistical analysis tools including Principal Component Analysis and Multiple Regression Analysis upon the characteristic feature vectors extracted from all the windowed sequence set, we could develop a significant linear model to identify the residues that are critical to allostery of 'ras' protein. © 2011 Springer-Verlag.


Namboodiri S.,Kerala University | Verma C.,Bioinformatics Institute BII | Dhar P.K.,Kerala University | Giuliani A.,Instituto Superiore Of Sanita | Nair A.S.,Kerala University
Systems and Synthetic Biology | Year: 2010

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins. © 2011 Springer Science+Business Media B.V.

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