In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives.

在本文中，我们讨论了从头开始蛋白质结构预测的两种替代方案的优点和问题。一方面，讨论了基于深度学习的最新方法，这些方法显着改善了多种蛋白质的预测结果。另一方面，分析了基于蛋白质构象能量最小化和不同搜索策略的方法。在后一种情况下，我们的方法基于差异进化和片段替换技术之间的模因组合，还结合了进化搜索中利基市场的可能性。不同的蛋白质被用来分析这两种方法的优缺点，提出了两种替代方案整合的可能性。