Biomedical subjects

Jan Prins

Publications and source records attributed to Jan Prins.

3 recordsLinked to original sources

PubMed2006

Structure-based function inference using protein family-specific fingerprints.

We describe a method to assign a protein structure to a functional family using family-specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active-site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family-specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.

Bacterial Proteins↗

PubMed2003

Therapeutic drug monitoring of nelfinavir and indinavir in treatment-naive HIV-1-infected individuals.

BACKGROUND: Both virological failure and the toxicity of HIV protease inhibitors have been related to interindividual variability of plasma drug concentrations. Therapeutic drug monitoring (TDM) offers the possibility to detect patients with drug concentrations outside therapeutic ranges, who can subsequently benefit from dose modifications. METHODS: ATHENA was a randomized controlled clinical trial. Subjects were randomly assigned to either a TDM group, in which the results of drug concentration measurements plus advice were reported to their treating physician, or to a control group for whom TDM results were not reported. This analysis refers to treatment-naive patients who started a regimen containing indinavir or nelfinavir before November 1999. FINDINGS: A total of 147 patients were randomly assigned: 92 to nelfinavir, 55 to indinavir. After one year of follow-up significantly fewer patients in the TDM group had discontinued nelfinavir or indinavir than in the control group: 17.4 versus 39.7%. This was mainly driven by a significantly lower rate of discontinuation because of virological failure in nelfinavir patients: 2.4% in the TDM group versus 17.6% in the control group, and by a non-significant difference in the rate of discontinuation because of toxicity in indinavir patients: 14.3% in the TDM group versus 29.6% in the control group. In a non-completer equals failure analysis of all randomized patients, the TDM group showed a significantly higher proportion of patients with a viral load below 500 copies after 12 months of treatment (78.2 versus 55.1%). INTERPRETATION: TDM of nelfinavir and indinavir in treatment-naive patients improves treatment response.

Adult↗

PubMedDate not supplied

Comparing graph representations of protein structure for mining family-specific residue-based packing motifs.

We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identifies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduce the number of edges in the graph representation and hence present computational advantage, yet the patterns extracted from such graphs have a biological interpretation approximately equivalent to that of those extracted from distance based graphs.

Algorithms↗