Data mining has been recognized by many researchers as a hot topic in different areas. In the post-genomic era, the growing number of sequences deposited in databases has been the reason why these databases have become a resource for novel biological information. In recent years, the identification of antimicrobial peptides (AMPs) in databases has gained attention. The identification of unannotated AMPs has shed some light on the distribution and evolution of AMPs and, in some cases, indicated suitable candidates for developing novel antimicrobial agents. The data mining process has been performed mainly by local alignments and/or regular expressions. Nevertheless, for the identification of distant homologous sequences, other techniques such as antimicrobial activity prediction and molecular modelling are required. In this context, this review addresses the tools and techniques, and also their limitations, for mining AMPs from databases. These methods could be helpful not only for the development of novel AMPs, but also for other kinds of proteins, at a higher level of structural genomics. Moreover, solving the problem of unannotated proteins could bring immeasurable benefits to society, especially in the case of AMPs, which could be helpful for developing novel antimicrobial agents and combating resistant bacteria.

数据挖掘已被许多研究人员认为是不同领域的热门话题。在后基因组时代，存储在数据库中的序列数量不断增长，这就是为什么这些数据库已成为获取新型生物学信息的资源的原因。近年来，数据库中抗菌肽（AMPs）的识别已引起关注。未注释的AMPs的鉴定为AMPs的分布和进化提供了一些启示，并且在某些情况下为开发新型抗微生物剂提供了合适的候选对象。数据挖掘过程主要通过局部对齐和/或正则表达式执行。然而，为了鉴定远距离的同源序列，还需要其他技术，例如抗微生物活性预测和分子建模。在这种情况下，本文将探讨从数据库中挖掘AMP的工具和技术及其局限性。这些方法不仅有助于新型AMP的开发，而且还有助于更高水平的结构基因组学研究的其他种类的蛋白质。此外，解决未注释蛋白的问题可能给社会带来不可估量的收益，特别是在AMP的情况下，这可能有助于开发新型抗菌剂和对抗耐药细菌。