A nanomechanical molecular “tape reader” is assembled and tested by threading a β-cyclodextrin ring onto a DNA oligomer and pulling it along with an AFM tip. The formation and mechanical operation of the system is confirmed by measuring the forces required to unfold secondary structures in the form of hairpins. Unfolding induced by this 0.7 nm aperture requires 40 times more force than that reported for pulling on the ends of the DNA. A kinetic analysis shows that much less strain is required to destabilize the double helix in this geometry. Consequently, much more force is required to provide the free energy needed for opening. DNA secondary structure may prove to be a significant obstacle both for enzymes that process DNA though an orifice, and for the passage through nanopores proposed for some novel sequencing schemes.
Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe here the application of the cat-SAR (categorical-SAR) program to two learning sets of rat mammary carcinogens. One set of developed models was based on a comparison of rat mammary carcinogens to rat noncarcinogens (MC-NC), and the second set compared rat mammary carcinogens to rat nonmammary carcinogens (MC-NMC). On the basis of a leave-one-out validation, the best rat MC-NC model achieved a concordance between experimental and predicted values of 84%, a sensitivity of 79%, and a specificity of 89%. Likewise, the best rat MC-MNC model achieved a concordance of 78%, a sensitivity of 82%, and a specificity of 74%. The MC-NMC model was based on a learning set that contained carcinogens in both the active (i.e., mammary carcinogens) and the inactive (i.e., carcinogens to sites other than the mammary gland) categories and was able to distinguish between these different types of carcinogens (i.e., tissue specific), not simply between carcinogens and noncarcinogens. On the basis of a structural comparison between this model and one for Salmonella mutagens, there was, as expected, a significant relationship between the two phenomena since a high proportion of breast carcinogens are Salmonella mutagens. However, when analyzing the specific structural features derived from the MC-NC learning set, a dichotomy was observed between fragments associated with mammary carcinogenesis and mutagenicity and others that were associated with estrogenic activity. Overall, these findings suggest that the MC-NC and MC-NMC models are able to identify structural attributes that may in part address the question of "why do some carcinogens cause breast cancer", which is a different question than "why do some chemicals cause cancer".
R. Bension has proposed that single molecules of DNA could be sequenced rapidly, in long sequential reads, by reading off the force required to pull a tightly fitting molecular ring over each base in turn using an atomic force microscope (AFM). We present molecular dynamics simulations that indicate that pulling DNA very rapidly (m/s) could generate large force peaks as each base is passed ( approximately 1 nN) with significant differences ( approximately 0.5 nN) between purine and pyrimidine. These speeds are six orders of magnitude faster than could be read out by a conventional AFM, and extending the calculations to accessible speeds using Kramers' theory shows that thermal fluctuations dominate the process with the result that purine and pyrimidine cannot be distinguished with the pulling speeds attained by current AFM technology.
SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen – Non-Carcinogen (TSC-NC) and Target Site Carcinogen – Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a specific target site and inactive ones that were whole animal non-carcinogens. On the other hand, the TSC-NTSC models used an inactive category also composed of carcinogens but to any/all other sites but the target site. Leave one out validations produced an overall average concordance value for all 12 models of 0.77 for the TSC-NC models and 0.73 for the TSC-NTSC models. Overall, these findings suggest that while the TSC-NC models are able to distinguish between carcinogens and non-carcinogens, the TSC-NTSC models are identifying structural attributes that associate carcinogens to specific tumour sites. Since the TSC-NTSC models are composed of active and inactive compounds that are genotoxic and non-genotoxic carcinogens, the TSC-NTSC models may be capable of deciphering non-genotoxic mechanisms of carcinogenesis. Together, models of this type may also prove useful in anticancer drug development since they essentially contain chemicals moieties that target specific tumour site.
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