The Protein Structure Prediction (PSP) problem is one of the most significant open problems in bioinformatics. In the AB off-lattice model, the protein sequence is labeled as ‘A’ or ‘B’ according to the amino acid classification of being hydrophobic or hydrophilic. It has been widely explored in the literature because polarity is one of the main driving forces behind protein structure definition. This work provides a high-performance hybrid algorithm to approach the 3D-AB off-lattice model through Graphics Processing Units (GPUs). The proposed hybrid algorithm, named cuHjDE–3D, is a self-adaptive Differential Evolution (DE) that uses the jDE mechanism to self-adapt the DE parameters and employs the Hooke-Jeeves Direct Search (HJDS) as the exploitation routine. The experiments were conducted on real protein sequences from the Protein Data Bank (PDB) and compared against state-of-the-art algorithms from the related literature concerning the 3D-AB off-lattice model. Moreover, we provide a methodology to compare a 3D-AB predicted conformation with its native conformation from the PDB repository using the RMSD metric. The obtained results highlight the optimization potential of the proposed method. Also, the GPU running time analysis reports the positive impact of using a massively parallel architecture, with speedups up to 277× , promoting the necessary scalability to handle the 3D-AB model.

蛋白质结构预测（PSP）问题是生物信息学中最重要的开放问题之一。在AB非格模型中，根据氨基酸分类为疏水性或亲水性，蛋白质序列被标记为“ A”或“ B”。由于极性是蛋白质结构定义背后的主要驱动力之一，因此已在文献中进行了广泛的研究。这项工作提供了一种高性能的混合算法，可以通过图形处理单元（GPU）来处理3D-AB格外模型。提出的名为cuHjDE-3D的混合算法是一种自适应差分进化（DE），它使用jDE机制来自适应DE参数，并采用Hooke-Jeeves直接搜索（HJDS）作为开发例程。实验是对来自蛋白质数据库（PDB）的真实蛋白质序列进行的，并与相关文献中有关3D-AB非晶格模型的最新算法进行了比较。此外，我们提供了一种方法，可以使用RMSD指标将3D-AB预测构象与其在PDB存储库中的原生构象进行比较。获得的结果突出了该方法的优化潜力。此外，GPU运行时间分析报告了使用大规模并行架构的积极影响，其加速高达277倍，从而提升了处理3D-AB模型所需的可伸缩性。我们提供了一种方法，可以使用RMSD指标将3D-AB预测构象与其在PDB存储库中的原生构象进行比较。获得的结果突出了该方法的优化潜力。此外，GPU运行时间分析报告了使用大规模并行架构的积极影响，其加速高达277倍，从而提升了处理3D-AB模型所需的可伸缩性。我们提供了一种方法，可以使用RMSD指标将3D-AB预测构象与其在PDB存储库中的原生构象进行比较。获得的结果突出了所提出方法的优化潜力。此外，GPU运行时间分析报告了使用大规模并行架构的积极影响，其加速高达277倍，从而提升了处理3D-AB模型所需的可伸缩性。