W552–W558 Nucleic Acids Research, 2009, Vol. 37, Web Server issue doi:10.1093/nar/gkp439 Published online 28 May 2009 seeMotif: exploring and visualizing sequence motifs in 3D structures Darby Tien-Hao Chang1, Ting-Ying Chien2 and Chien-Yu Chen3,* 1Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, 2Department of Computer Science and Information Engineering and 3Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, 10617, Taiwan, R.O.C. Received March 4, 2009; Revised April 23, 2009; Accepted May 11, 2009 ABSTRACT Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps. A good way to investigate novel motifs is to utilize the abun- dant 3D structures that have also been accumulated at an astounding rate in recent years. This article reports the development of the web service seeMotif, which provides users with an interactive interface for visualizing sequence motifs on protein structures from the Protein Data Bank (PDB). Researchers can quickly see the locations and con- formation of multiple motifs among a number of related structures simultaneously. Considering the fact that PDB sequences are usually shorter than those in sequence databases and/or may have missing residues, seeMotif has two complementary approaches for selecting structures and mapping motifs to protein chains in structures. As more and more structures belonging to previously uncharac- terized protein families become available, combin- ing sequence and structure information gives good opportunities to facilitate understanding of protein functions in large-scale genome projects. Available at: http://seemotif.csie.ntu.edu.tw, http://seemotif. ee.ncku.edu.tw or http://seemotif.csbb.ntu.edu.tw. INTRODUCTION In the past few years, an urgent demand for eﬃcient der- ivation of sequence motifs has arisen, due to the large number of sequencing projects for various species (1–4). Motifs are frequently observed in biological sequences, such as transcription factor binding sites in DNA sequences and catalytic or protein–protein interaction sites in protein sequences. These motifs have been shown critical for biomolecules to perform their functions. While developing algorithms to discover motifs attracts more and more attention today, eﬃcient determination of the biological roles of such motifs is considered as one of the next important steps (5). In proteins, sequence motifs are largely used in auto- mated function annotation and residue characterization. Examples of databases containing protein motifs include PROSITE (2), ELM (3), MnM (6), BLOCKS (7) and PRINTS (8). Protein sequence motifs can be roughly cate- gorized into three distinct groups. Motifs of the ﬁrst group are derived for the goal of function or domain classiﬁca- tion, where PROSITE patterns serve as a famous example. The second group focuses more on functional sites of biopolymers, such as catalytic sites and protein–DNA or protein–protein interaction sites. Such motifs often serve as sequence signatures for predicting interacting regions and can be used to predict functionally important residues (9,10). Many motifs fall in both the ﬁrst and the second groups, although they were ﬁrst derived only for one of the two goals. The third group comprises motifs that are considerably shorter than those in the ﬁrst two groups. They usually contain only 3–10 residues and are named short linear motifs (4,11). Many such particular sequence patterns are shown to provide pivotal functional roles, and some of them are found in intrinsically disor- dered regions. Both ELM and MnM contain hundreds of such motifs. In addition to known motifs, novel motifs are continuing to be discovered daily by computational tools. Several pattern mining algorithms, including PRATT (12,13), TEIRESIAS (14), MEME (15), QuasiMotiFinder (16) and MAGIIC-PRO (17), have demonstrated the potential to discover valuable signatures using sequences only. In addition, many recent studies focus on developing eﬀective methods for short linear motifs (18–22). Meanwhile, the task of detecting putative *To whom correspondence should be addressed. Tel: +886 2 3366 5334; Fax: +886 2 2362 7620; Email: cychen@mars.csie.ntu.edu.tw 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.