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In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.

Electronic mail is one of the oldest and widely used services in the Internet. In this paper, an empirical study of the security properties of email server communication within the German IP address space range is presented. Instead of investigating end-user security or end-to-end encryption, we focus on the connections between SMTP servers relying on transport layer security. We analyze the involved ciphers suites, the certificates used and certificate authorities, and the behavior of email providers when communicating with improperly secured email servers. Conclusions drawn from this analysis lead to several recommendations to mitigate the security issues currently present in the email system as it is deployed in the Internet.

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