Structure Collisions between Interacting Proteins

Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions

The LabelHash algorithm for substructure matching

Background: There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Results: We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95 % sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs a

Characteristics of Australian cohort study participants who do and do not take up an additional invitation to join a long-term biobank: The 45 and Up Study

BACKGROUND: Large-scale population biobanks are critical for future research integrating epidemiology, genetic, biomarker and other factors. Little is known about the factors influencing participation in biobanks. This study compares the characteristics of biobank participants with those of non-participants, among members of an existing cohort study. METHODS: Individuals aged 45 and over participating in The 45 and Up Study and living ≤20km from central Wagga Wagga, New South Wales (NSW), Australia (rural/regional area) or ≤10km from central Parramatta, NSW (urban area) (n=2340) were invited to join a biobank, giving a blood sample and having additional measures taken, including height, weight, waist circumference, heart rate and blood pressure. RESULTS: The overall uptake of the invitation to participate was 33% (762/2340). The response rate was 41% (410/1002) among participants resident in the regional area, and 26% (352/1338) among those resident in the urban area. Characteristics associated with significantly decreased participation were being aged 80 and over versus being aged 45–64 (participation rate ratio: RR = 0.45, 95%CI 0.34-0.60), not being born in Australia versus being born in Australia (0.69, 0.59-0.81), having versus not having a major disability (0.54, 0.38-0.76), having full-time caregiving responsibilities versus not being a full-time carer (0.62, 0.42-0.93) and being a current smoker versus never having smoked (0.66, 0.50-0.89). Factors associated with increased participation were being in part-time work versus not being in paid work (1.24, 1.07-1.44) and having an annual household income of ≥$50,000 versus <$20,000 (1.50, 1.26-1.80). CONCLUSIONS: A range of socio-economic, health and lifestyle factors are associated with biobank participation among members of an existing cohort study, with factors relating to health-seeking behaviours and access difficulties or time limitations being particularly important. If more widespread participation in biobanking is desired, particularly to ensure sufficient numbers among those most affected by these issues, specific efforts may be required to increase participation in certain groups such as migrants, the elderly, and those in poor health. Whilst caution should be exercised when generalising estimates of absolute prevalence from biobanks, estimates for many internal comparisons are likely to remain valid