The number of sequences and structures of biological mol-ecules, such as DNA, RNA, and proteins, is rapidly increas-ing in databases. According to the statistics in the newest versionofGOLDdatabase(https://gold.jgi-psf.org/)[1], 6651 genomes have been completed and published and 51954 genomes are of permanent drafts or incomplete. The pro-tein data bank (PDB, http://www.rcsb.org/)[2]haspub-lished 107958 biological macromolecular structures, includ-ing 35542 protein sequences, 28142 structures of human sequences, and 7611 nucleic acid containing structures. To discover the underlying mechanism behind the information, it is necessary to develop effective feature parameters for rep-resenting the sequence and structure. Although many studies have been proposed for sequence and structure analysis, more and more studies indicated that the problem is far from solved [3, 4]. How to computationally analyze the big data of biologically molecular sequences and structures has been one of the major challenges in bioinformatics. Machine learning and optimization methods are important methods for the analyses. This special issue focuses on recent progress of the computational methods for biological sequences or structures studies. Correspondingly, after a rigorous peer review, eleven papers were selected.