Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.

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Designing antimicrobial peptides: form follows function

Ant colony optimization for continuous domains

Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

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A Comprehensive Survey of Clustering Algorithms

Publisher Summary This chapter presents a new population-based search algorithm called the Bees Algorithm (BA). This algorithm mimics the food foraging behavior of swarms of honeybees. In its basic version, the algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization. This chapter focuses on the latter. Following a description of the algorithm, the chapter presents the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm. The results show that the algorithm can reliably handle complex multi-model optimization problems without being trapped at local solutions. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials.

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The bees algorithm, a novel tool for complex optimisation problems

Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm. Clustering analysis, used in many disciplines and applications, is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this work, ABC is used for data clustering on benchmark problems and the performance of ABC algorithm is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering.

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A novel clustering approach: Artificial Bee Colony (ABC) algorithm

An ant colony approach for clustering

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and -specificity relationships in AMPs. CAMP is freely available at http://www.bicnirrh.res.in/antimicrobial.

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CAMP: a useful resource for research on antimicrobial peptides

An ant colony optimization framework has been compared and shown to be a viable alternative approach to other stochastic search algorithms. The algorithm has been tested for variety of different benchmark test functions involving constrained and unconstrained NLP, MILP, and MINLP optimization problems. This novel algorithm handles different types of continuous functions very well and can be successfully used for large-scale process optimization.

V. K. Jayaraman

Papers

An ant colony approach for clustering

CAMP: a useful resource for research on antimicrobial peptides

Ant Colony Approach to Continuous Function Optimization

Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process

Ant colony framework for optimal design and scheduling of batch plants