E Santo Anderson A, Reis Aline, Pinheiro Anderson A, da Costa Paulo I, Feliciano Gustavo T
Institute of Chemistry, São Paulo State University, Araraquara, SP 14800-900, Brazil.
School of Pharmaceutical Sciences, São Paulo State University, Araraquara, SP 14801-360, Brazil.
Biochemistry. 2025 Apr 1;64(7):1541-1549. doi: 10.1021/acs.biochem.4c00671. Epub 2025 Mar 17.
In the context of fast and significant technological transformations, it is natural for innovative artificial intelligence (AI) methods to emerge for the design of bioactive molecules. In this study, we demonstrated that the design of mimetic antibodies (MA) can be achieved using a combination of software and algorithms traditionally employed in molecular simulation. This combination, organized as a genetic algorithm (GA), has the potential to address one of the main challenges in the design of bioactive molecules: GA convergence occurs rapidly due to the careful selection of initial populations based on intermolecular interactions at antigenic surfaces. Experimental immunoenzymatic tests prove that the GA successfully optimized the molecular recognition capacity of one of the MA. One of the significant results of this study is the discovery of new structural motifs, which can be designed in an original and innovative way based on the MA structure itself, eliminating the need for preexisting databases. Through the GA developed in this study, we demonstrated the application of a new protocol capable of guiding experimental methods in the development of new bioactive molecules.
在快速且重大的技术变革背景下,出现用于生物活性分子设计的创新型人工智能(AI)方法是很自然的。在本研究中,我们证明了可以使用传统上用于分子模拟的软件和算法组合来实现模拟抗体(MA)的设计。这种组合被组织成遗传算法(GA),有潜力解决生物活性分子设计中的一个主要挑战:由于基于抗原表面的分子间相互作用仔细选择初始种群,GA收敛迅速。实验性免疫酶测试证明GA成功优化了其中一种MA的分子识别能力。本研究的一个重要成果是发现了新的结构基序,其可以基于MA结构本身以新颖和创新的方式进行设计,无需预先存在的数据库。通过本研究开发的GA,我们展示了一种能够在新生物活性分子开发中指导实验方法的新方案的应用。