No abstract

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×104 known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.

BackgroundDrug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.ResultsIn order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.ConclusionThe approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.

BackgroundHIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens.ResultsA unified encoding of protein sequence and structure was used as the feature vector for predicting phenotypic resistance from genotype data. Two machine learning algorithms, Random Forest and K-nearest neighbor, were used. The prediction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets. A supervised machine learning approach for automatic prediction of drug resistance was developed to handle genotype-phenotype datasets of HIV protease (PR) and reverse transcriptase (RT). It predicts the drug resistance phenotype and its relative severity from a query sequence. The accuracy of the classification was higher than 0.973 for eight PR inhibitors and 0.986 for ten RT inhibitors, respectively. The overall cross-validated regression R2-values for the severity of drug resistance were 0.772–0.953 for 8 PR inhibitors and 0.773–0.995 for 10 RT inhibitors.ConclusionsMachine learning using a unified encoding of sequence and protein structure as a feature vector provides an accurate prediction of drug resistance from genotype data. A practical webserver for clinicians has been implemented.

We have systematically validated the activity and inhibition of a HIV-1 protease (PR) variant bearing 17 mutations (PRS17), selected to represent high resistance by machine learning on genotype-phenotype data. Three of five mutations in PRS17 correlating with major drug resistance, M46L, G48V and V82S, and five of eleven natural variations, differ from two clinically derived extreme mutants, PR20 and PR22 bearing 19 and 22 mutations, respectively. PRS17, which forms a stable dimer (<10 nM), is ~10- and 2-fold less efficient in processing the Gag polyprotein relative to the wild-type and PR20, respectively, but maintains the same cleavage order. Isolation of a model precursor of PRS17 flanked by the 56 amino acid transframe region (TFP-p6pol) at its N-terminus, which is impossible when expressing an analogous PR20 precursor, allowed systematic comparison of inhibition of TFP-p6pol-PRS17 and mature PRS17. Resistance of PRS17 to 8 protease inhibitors (PIs) relative to PR ranges from 1.5 to 5 orders of magnitude increase in Ki from 0.01 to 8.4 μM. Amprenavir, darunavir, atazanavir and lopinavir, the most effective of the 8 PIs, inhibit precursor autoprocessing at the p6pol/PR site with IC50 ranging from ~7.5 to 60 μM. Thus this process, crucial for stable dimer formation, shows ~200 to 800-fold weaker inhibition than the mature PRS17. TFP/p6pol cleavage, which occurs faster, is inhibited even more weakly by all PIs except darunavir (IC50 of 15 μM); amprenavir shows a 2-fold increase in IC50 (~15 μM), and atazanavir and lopinavir show increased IC50 of >42 μM and >70 μM, respectively.

GRL-02031 (1) is an HIV-1 protease (PR) inhibitor containing a novel P1′ (R)-aminomethyl-2-pyrrolidinone group. Crystal structures at resolutions of 1.25 to 1.55 Å were analyzed for complexes of 1 with the PR containing major drug resistant mutations, PRI47V, PRL76V, PRV82A and PRN88D. Mutations of I47V and V82A alter residues in the inhibitor-binding site, while L76V and N88D are distal mutations having no direct contact with the inhibitor. Substitution of a smaller amino acid in PRI47V and PRL76V, and the altered charge of PRN88D are associated with significant local structural changes compared to the wild-type PRWT, while substitution of alanine in PRV82A increases the size of the S1′ subsite. The P1′ pyrrolidinone group of 1 accommodates to these local changes by assuming two different conformations. Overall, the conformation and interactions of 1 with PR mutants resemble those of PRWT with similar inhibition constants in good agreement with the antiviral potency on multidrug resistant HIV-1.