David S Palmer | PubMed Health

PubMed2003

Sequence-dependent DNA structure: a database of octamer structural parameters.

We have constructed the potential energy surfaces for all unique tetramers, hexamers and octamers in double helical DNA, as a function of the two principal degrees of freedom, slide and shift at the central step. From these potential energy maps, we have calculated a database of structural and flexibility properties for each of these sequences. These properties include: the values of each of the six step parameters (twist roll, tilt, rise, slide and shift), for each step of the sequence; flexibility measures for both decrease and increase in each property value from the minimum energy conformation for the central step; and the deviation from the path of a hypothetical straight octamer. In an analysis of structural change as a function of sequence length, we observe that almost all DNA tends to B-DNA and becomes less flexible. A more detailed analysis of octamer properties has allowed us to determine the structural preferences of particular sequence elements. GGC and GCC sequences tend to confer bistability, low stability and a predisposition to A-form DNA, whereas AA steps strongly prefer B-DNA and inhibit A-structures. There is no correlation between flexibility and intrinsic curvature, but bent DNA is less stable than straight. The most difficult deformation is undertwisting. The TA step stands out as the most flexible sequence element with respect to decreasing twist and increasing roll. However, as with the structural properties, this behavior is highly context-dependent and some TA steps are very straight.

DNA↗

PubMedDate not supplied

Chemoinformatics-based classification of prohibited substances employed for doping in sport.

Representative molecules from 10 classes of prohibited substances were taken from the World Anti-Doping Agency (WADA) list, augmented by molecules from corresponding activity classes found in the MDDR database. Together with some explicitly allowed compounds, these formed a set of 5245 molecules. Five types of fingerprints were calculated for these substances. The random forest classification method was used to predict membership of each prohibited class on the basis of each type of fingerprint, using 5-fold cross-validation. We also used a k-nearest neighbors (kNN) approach, which worked well for the smallest values of k. The most successful classifiers are based on Unity 2D fingerprints and give very similar Matthews correlation coefficients of 0.836 (kNN) and 0.829 (random forest). The kNN classifiers tend to give a higher recall of positives at the expense of lower precision. A naïve Bayesian classifier, however, lies much further toward the extreme of high recall and low precision. Our results suggest that it will be possible to produce a reliable and quantitative assignment of membership or otherwise of each class of prohibited substances. This should aid the fight against the use of bioactive novel compounds as doping agents, while also protecting athletes against unjust disqualification.

Algorithms↗