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Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.)

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO's core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results. [ABSTRACT FROM AUTHOR]