A Sequence-segment Neighbor Encoding Schema for Protein Hotspot Residue Prediction

Background: Hotspots are those residues that contribute major free energy of binding in protein-protein interactions. Protein functions are frequently dependent on hotspot residues. At present, hotspot residues are always identified by Alanine scanning mutagenesis technology, which is costly, time-consuming and laborious. Objective: Therefore, more accurate and efficient methods have to be developed to identify protein hotspot residues. Methods: This paper proposed a novel encoding schema of sequence-segment neighbors and constructed a random forest-based model to identify hotspots in protein interaction interfaces. Firstly, 10 amino acid physicochemical properties, 16 features related to the PI and DI, and 25 features related to ASA were extracted. Different from the previous residue encoding schemas, such as auto correlation descriptor or triplet combination information, this paper employed the influence of amino acids neighbors to hotspot residues and amino acids with a certain distance in sequence to the hotspot. Results: Moreover, the proposed model was compared with other hotspot prediction methods, including APIS, Robetta, FOLDEF, KFC, MINERVA models, etc. Conclusion: The experimental results showed that the proposed model can improve the prediction ability of protein hotspot residues on the same test set.

A Time-efficient Multi-Protocol Probe Scheme for Fine-grain IoT Device Identification

Internet of Things (IoT) devices connected to the Internet are exploding, which poses a significant threat for their management and security protection. IoT device identification is a prerequisite for discovering, monitoring, and protecting these devices. Although we can identify the device type easily through grabbing protocol banner information, both brand and model of different types of device are various and diverse. We should therefore utilize multi-protocol probes to improve the fineness of device identification and obtain the corresponding brand and model. However, it is still a challenge to balance between the multi-protocol probe overhead and the identification fineness. To solve this problem, we proposed a time-efficient multi-protocol probe scheme for fine-grain devices identification. We first adopted the concept of reinforcement learning to model the banner-based device identification process into a Markov decision process (MDP). Through the value iteration algorithm, an optimal multi-protocol probe sequence is generated for a type-known IoT device, and then the optimal multi-protocol probes sequence segment is extracted based on the gain threshold of identification accuracy. We took 132,835 webcams as the sample data to experiment. The experimental results showed that our optimal multi-protocol probes sequence segment could reduce the identification time of webcams’ brand and model by 50.76% and achieve the identification accuracy of 90.5% and 92.3% respectively. In addition, we demonstrated that our time-efficient optimal multi-protocol probe scheme could also significantly improve the identification efficiency of other IoT devices, such as routers and printers.

PCR amplification and DNA sequence of mcyA gene: The distribution profile of a toxigenic Microcystis aeruginosa in the Hartbeespoort Dam, South Africa

Using new polymerase chain reaction (PCR) primers, a once known to be under-transcribed microcystin synthetase A (mcyA) gene from the only known toxigenic cyanobacterium Microcystis aeruginosa dominating the Hartbeespoort Dam was consistently amplified from genomic DNA extracted from a set of algal and cell free water samples collected across this dam. In addition to this, five more mcy genes (mcyBCDEG) were also amplified during this study. The resultant mcyA PCR products (518 bp) were purified and sequenced and gave nucleotide sequence segments of 408 bp sizes. The obtained sequence was aligned to the published mcyA gene sequence available online on the NCBI database and resulted in 100% similarity to a 408 bp mcyA gene sequence segment of M. aeruginosa UWOCC RID-1. Furthermore, it was found that the above sequence segment (408 bp) spans from a common base in M. aeruginosa PCC 7806 and M. aeruginosa PCC 7820 from 141 to 548 bp in the N-methyl transferase (NMT) region signifying their closer relatedness to M. aeruginosa UWOCC strains. This study has for the first time amplified mcyA gene consistently from both intracellular and extracellular DNA extracts obtained from algal and cell free water samples, respectively. Sequence data and the amplified mcy genes showed that M. aeruginosa is widely distributed and dominant in this dam.