Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cutoff distance d c value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter d c on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to K initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter d c. Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect. INDEX TERMS Clustering, density peak, particle swarm optimization, fitness function.
Dengue virus (DENV) has four serotypes that complicate vaccine development. Envelope protein domain III (EDIII) of DENV is a promising target for therapeutic antibody development. One EDIII-specific antibody, dubbed 1A1D-2, cross-reacts with DENV 1, 2, and 3 but not 4. To improve the affinity of 1A1D-2, in this study, we analyzed the previously solved structure of 1A1D-2-DENV2 EDIII complex. Mutations were designed, including A54E and Y105R in the heavy chain, with charges complementary to the epitope. Molecular dynamics simulation was then used to validate the formation of predicted salt bridges. Interestingly, a surface plasmon resonance experiment showed that both mutations increased affinities of 1A1D-2 toward EDIII of DENV1, 2, and 3 regardless of their sequence variation. Results also revealed that A54E improved affinities through both a faster association and slower dissociation, whereas Y105R improved affinities through a slower dissociation. Further simulation suggested that the same mutants interacted with different residues in different serotypes. Remarkably, combination of the two mutations additively improved 1A1D-2 affinity by 8, 36, and 13-fold toward DENV1, 2, and 3, respectively. In summary, this study demonstrated the utility of tweaking antibody-antigen charge complementarity for affinity maturation and emphasized the complexity of improving antibody affinity toward multiple antigens.
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