Camelidae (Camelus ferus, Camelus bactrianus, Camelus dromedarius, Vicugna vicugna, Vicugna pacos, Lama guanicoe, Lama glama) and some cartilaginous fish (nurse shark, wobbegong and dogfish sharks) were discovered to produce functional antibodies which naturally lacked light chains [1, 2]. The single domain antigen-binding fragments of camelid heavy chain antibodies are referred to VHHs or nanobodies. Nanobody entity contains four framework regions (FR1–4), forming the core structure of the domain, which are alternated with three complementary determining regions (CDR1–3). Compared with traditional antibodies, nanobodies show obvious advantages: higher affinity, higher solubility, higher domain stability, smaller size (~15 kda) and recognition of hidden antigenic sites, reduced aggregation tendencies, more easily expressed in microorganisms [3–8].

In the latest years, there has been an enormous growth of interest in studying nanobody for its unique characteristic structure and being the ideal candidate for the development of sophisticated nanobiotechnologies in diverse fields. In basic research, nanobodies are developed into various research tools such as affinity purification [9], gene activation or inactivation [10], immunoprecipitation [11]. Due to the nanoscopic size and high affinity against intracellular signaling molecules, nanobodies and their derivative formats are used as nanotracer in intracellular bioimaging [12]. In addition, nanobodies are applied in disease diagnosis, for example, molecular diagnosis for breast cancer [13]. The application of nanobodies in targeting therapeutics is in an early stage but shows good prospects in the therapy of acute thrombotic thrombocytopenic purpura [14], infectious disease [15], rheumatoid arthritis [16], central nervous system disease [17], breast and ovarian cancers [18] and so on. Besides, nanobodies are used to be enzyme inhibitors to increase starch content [19] and also to do food testing in agriculture.

Currently, the application research of nanobody is still in exploration. Although thousands of nanobodies have been published, there exists no database of nanobodies integrating nanobody resources. iCAN is built as the first comprehensive nanobody database, which is presented for the aim that providing searchable, unified annotation and integrative analysis tools for both academic and industrial researchers to expand and accelerate the nanobody research. In order to accomplish this, the annotation data was collected and integrated from public sources. The first version of iCAN has 2391 entries, comprising 74 nanobodies from RCSB PDB, 156 nanobodies from EMBL and 2131 nanobodies from patents. Information related to nanobody sequence, structure, target antigens, function, taxonomy of the source organism is shown. In addition, iCAN also provides online bioinformatic tools which are designed for sequence analysis such as Blast, Clustal Omega, Motif extraction and CDR prediction tool. These tools can be utilized to fast discover characteristic feature of nanobodies against one special antigen in sequence. Altogether, iCAN will aid the scientific/industrial community in analyzing the nanobody features and exploring the applications of nanobodies.

Obtaining detailed information and sequence feature for nanobody of interest is necessary for nanobody research and application. iCAN provides users with such information and convenience. Here we present an example that one can get the sequence feature of nanobodies for a given antigen (Human Epidermal Growth Factor Receptor, EGFR) by using iCAN data and analytic tools. To achieve the above goal, one can follow the below pipeline: obtaining nanobody sequence by searching antigen name or by blasting a given similar nanobody sequence, followed by downloading the data, multiple sequence alignment and motif extraction.

Obtaining nanobody sequence by BLAST

BLAST is one of the most important tools for searching nanobodies with sequence as the keyword. It can be used to search nanobody from all nanobodies or, specially, from the patented nanobodies recorded in iCAN database. In the page of Blast search result, the top 10 significant alignments are shown (Fig. 3a).

Besides our current work, there are still many aspects of the database that can be further improved in the future. First, a large fraction of nanobody sequences are likely not to be published for some reasons such as commercial interest, which are not included in this database. It is very necessary to expand the number of nanobodies of iCAN in the future with the help of all researchers. Second, structure of nanobody is key feature for specially recognizing and binding with antigen, thus exploration of nanobody structure is important for designing novel functional nanobody for target antigen. In the current version of the database, the structure data of many nanobodies is vacant. More structure data are required. As fast increased nanobody structure data will be uploaded to PDB database, more nanobody 3D structure data will be collected and help us get an idea about the relationship between nanobody sequence and its 3D structure.

Additionally, as nanobody has family-specific sequence composition, with fast increasing sequence data of nanobodies, there is a possibility that one can mine the sequence to discover nanobody sequence feature and try to de novo design novel nanobody sequence. We will further build-up and embed protein homology modeling tools in our database to describe nanobody structure feature. The above work will be completed with more accumulation of nanobody sequence and novel tools involvement. At that time, iCAN will provide more powerful functions for users.

In this study, we present the first database of nanobody, iCAN. That is a comprehensive and free database of nanobody which not only collects the nanobody information, but also provides tools for sequence alignment, pattern feature creation and keywords-based search function. The information and tools in iCAN will enable researchers to work on nanobody more efficiently.

The datasets generated and/or analyzed during the current study are available in the iCAN repository, http://ican.ils.seu.edu.cn

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Datasets in iCAN are freely available for academic users. For all other uses, please contact with us by email: ican@seu.edu.cn