Biomedical subjects

Gopal Pandurangan

Publications and source records attributed to Gopal Pandurangan.

3 recordsLinked to original sources

PubMed2005

An efficient randomized algorithm for contact-based NMR backbone resonance assignment.

MOTIVATION: Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance (NMR) spectroscopy. A minimalist approach to assignment, which we call 'contact-based', seeks to dramatically reduce experimental time and expense by replacing the standard suite of through-bond experiments with the through-space (nuclear Overhauser enhancement spectroscopy, NOESY) experiment. In the contact-based approach, spectral data are represented in a graph with vertices for putative residues (of unknown relation to the primary sequence) and edges for hypothesized NOESY interactions, such that observed spectral peaks could be explained if the residues were 'close enough'. Due to experimental ambiguity, several incorrect edges can be hypothesized for each spectral peak. An assignment is derived by identifying consistent patterns of edges (e.g. for alpha-helices and beta-sheets) within a graph and by mapping the vertices to the primary sequence. The key algorithmic challenge is to be able to uncover these patterns even when they are obscured by significant noise. RESULTS: This paper develops, analyzes and applies a novel algorithm for the identification of polytopes representing consistent patterns of edges in a corrupted NOESY graph. Our randomized algorithm aggregates simplices into polytopes and fixes inconsistencies with simple local modifications, called rotations, that maintain most of the structure already uncovered. In characterizing the effects of experimental noise, we employ an NMR-specific random graph model in proving that our algorithm gives optimal performance in expected polynomial time, even when the input graph is significantly corrupted. We confirm this analysis in simulation studies with graphs corrupted by up to 500% noise. Finally, we demonstrate the practical application of the algorithm on several experimental beta-sheet datasets. Our approach is able to eliminate a large majority of noise edges and to uncover large consistent sets of interactions. AVAILABILITY: Our algorithm has been implemented in the platform-independent Python code. The software can be freely obtained for academic use by request from the authors.

Algorithms↗

PubMed2003

Incidence of side-effects associated with high-dose ferric gluconate in patients with severe chronic renal failure.

Ferric gluconate complex in sucrose (Ferrlecit) has been associated with less side-effects than iron dextran; however, the recommended dose of 62.5-125 mg per treatment is only suitable for haemodialysis (HD) patients. We retrospectively analysed the incidence of the side-effects associated with a high dose of Ferrlecit infusion (20 treatments in 13 patients; 10 treatments of 250 mg/3-4 h, and 10 treatments of 500 mg/5 h infusion). The patients were in the age range of 32-75 years old, seven with chronic renal failure (CRF), and six on dialysis treatment. One (10%) of the 10 treatments using a 250 mg dose was complicated with severe nausea/vomiting, diarrhoea and a burning sensation in the feet. Three (30%) of the 10 treatments using a 500 mg dose were complicated with: chills, severe nausea/vomiting, hypotension and syncope in one; severe nausea/vomiting, diarrhoea and hypotension in one; and an episode of vomiting in one patient. A single treatment with a 250 mg dose resulted in no significant change in haematological parameters. A single treatment with a 500 mg dose resulted in a significant increase in haemoglobin (Hgb) and haematocrit (Hct), but only a rising trend in serum iron,% transferrin saturation and ferritin pre versus 1-2 months postinfusion. In conclusion, Ferrlecit doses of 250 or 500 mg are complicated with significant untoward reactions in 10-30% of patients, in a dose-dependent fashion.

Adult↗

PubMedDate not supplied

A random graph approach to NMR sequential assignment.

Nuclear magnetic resonance (NMR) spectroscopy allows scientists to study protein structure, dynamics and interactions in solution. A necessary first step for such applications is determining the resonance assignment, mapping spectral data to atoms and residues in the primary sequence. Automated resonance assignment algorithms rely on information regarding connectivity (e.g., through-bond atomic interactions) and amino acid type, typically using the former to determine strings of connected residues and the latter to map those strings to positions in the primary sequence. Significant ambiguity exists in both connectivity and amino acid type information. This paper focuses on the information content available in connectivity alone and develops a novel random-graph theoretic framework and algorithm for connectivity-driven NMR sequential assignment. Our random graph model captures the structure of chemical shift degeneracy, a key source of connectivity ambiguity. We then give a simple and natural randomized algorithm for finding optimal assignments as sets of connected fragments in NMR graphs. The algorithm naturally and efficiently reuses substrings while exploring connectivity choices; it overcomes local ambiguity by enforcing global consistency of all choices. By analyzing our algorithm under our random graph model, we show that it can provably tolerate relatively large ambiguity while still giving expected optimal performance in polynomial time. We present results from practical applications of the algorithm to experimental datasets from a variety of proteins and experimental set-ups. We demonstrate that our approach is able to overcome significant noise and local ambiguity in identifying significant fragments of sequential assignments.

Algorithms↗