A reduced representation in the format of a barcode has been developed to provide an overview of the topological nature of a given protein structure from 3D coordinate file. The molecular structure of a protein coordinate file from Protein Data Bank is first expressed in terms of an alpha-numero code and further converted to a barcode image. The barcode representation can be used to compare and contrast different proteins based on their structure. The utility of this method has been exemplified by comparing structural barcodes of proteins that belong to same fold family, and across different folds. In addition to this, we have attempted to provide an illustration to (i) the structural changes often seen in a given protein molecule upon interaction with ligands and (ii) Modifications in overall topology of a given protein during evolution. The program is fully downloadable from the website http://www.iitg.ac.in/probar/.
The strength of protein data bank (PDB) has been growing exponentially over last 3 decades. As structural genomics initiatives gain momentum, this trend is expected to continue in the following years as well, principally because of the rapid advancement in high throughput structure determination techniques.[2, 3] Total number of structures reported in PDB is inching closer to the milestone of 1 lakh structures. Total number of folds identified so far is 1392 and 1282 as per SCOP[4, 5] and CATH6 classification, respectively, and no additions to this number have been reported since 2009. Nevertheless proteins belong to the same fold family do exhibit variations at sequential, structural (to some extent) as well as functional levels.[7, 8] Numerous tools are available as open source programs for protein visualization and structure prediction.[10, 11] There have also been attempts to present reduced representations to three-dimensional protein structures in 2D and 1D. TOPS diagrams and contact maps show protein secondary structure and topology in two dimensions, while DSSP presents secondary structure information of a protein molecule sequentially from N terminus to C terminus as a 1D string. We present here a new representation of protein structure in the form of a “barcode.” The advantage of this type of representation is that, it can encode secondary structure as well as their relative orientation in space. We can align different “barcodes” to compare and contrast structural and topological information of a given structure. Inspiration to this type of a representation was drawn from the pioneering contribution in encoding information as “barcodes” by Bernard Silver and Norman Woodland in 1949. It took 3–4 decades to completely operationalize the technology using barcodes for cataloguing articles across a wide variety of applications. We present in this article, the design and utility of this computational tool in cataloguing proteins according to their structure. The program is fully downloadable from the website http://www.iitg.ac.in/probar/; we also provide a webserver that can display barcode images of close to about 70,000 protein molecules in PDB.
VALIDATION OF COMPUTATIONAL METHODS
Crystal structure of B1 immunoglobulin-binding domain of streptococcal protein G (1PGB.pdb) is used as a model structure to illustrate the design of protein barcode representation. The 56 residue protein molecule with one alpha helix and one beta sheet consisting of four beta strand has a well-defined hydrophobic core. Total number of secondary structure elements is five, with first and second strands forming an antiparallel beta sheet followed by a helix. Another antiparallel beta sheet follows the helix, coplanar with the first sheet with final beta strand being parallel to the first strand. As all four strands form one continuous sheet, all four strands are colored same (blue in this case). Secondary structure elements (SSEs) not part of the same sheet are colored differently as illustrated in Figures 3 and 4. All successive secondary structures in protein G are antiparallel in their relative orientation and hence having an identical space width of three units. Space width is customizable by appropriately modifying the code. Space width may change according to the relative topology of successive SSEs. Therefore, protein barcode provides information about SSEs and their relative topology with necessary clarity. Furthermore, it is possible to derive TOPS representation from barcode with reasonable accuracy and vice versa (Figs. 1 and 2).
Structure comparison using barcode identity index (BII): analyzing the spatial orientations of proteins is significant for their functional and evolutionary studies and such an objective may be achieved by comparison of barcodes. To indicate the utility of protein barcode, we further examined the barcode images generated from structure files of all PDB structures of DHFR (dihydrofolate reductase) across different species. Although the barcode images look more or less identical, subtle differences can be observed in structures adapted during evolution from left to right (Fig. 3). A barcode identity index (BII) has also been formulated to compare structures quantitatively (Fig. 4) and structural adaptations at specific loci can be identified by carefully comparing two barcode images. Barcode identity index (BII) is calculated from a metadata of barcode image, consisting of numbers that correspond to the “barcode” and aligning them. In a typical case, Helix is represented as 0, Strand as 1, and the orientation between secondary structures as 3, 4, 5, and 6 based on space width between 2 bars in the barcode representation. For example, 1A41.pdb may be represented as 03030413140304030303030. The number that represents a barcode (query) is aligned with another number (subject) using Needleman Wunsch algorithm. Further details may be found in Supporting Information and BII code may be downloaded from Barcode webpage.
Protein barcode is presented as a TIFF image. If this representation is widely accepted by the scientific community, then it will help in locating proteins in a “protein-barcode” database by making use of Content-based image retrieval (CBIR) tools.[18, 19] This method is basically meant for addressing the problem of searching digital images in large databases. It analyzes the content of the image rather than the meta-data or descriptions or tags associated with the image. Barcode representation foresees this opportunity in subsequent phases of its development, although it is beyond the scope of this manuscript. Furthermore, we tested barcode image comparison to study the possible structural alterations during ligand binding on the same DHFR structure. The number and type of ligands bound to DHFR receptor were given in Table S1 (Supporting Information). The disparities in structures are pictorially represented as barcodes and their relative similarities in overall topology may be quantified from calculating BII. For illustrative purpose, topologically similar structures are clubbed together and structurally dissimilar molecules are separated in a VIBGYOR color scheme.
Protein barcode is the representation of secondary structures, and their orientations as barcode images. The colored bars in the barcode image correspond to the SSEs and white spaces between the secondary structures represent the orientation between the two SSEs. Three-dimensional co-ordinate file from PDB is used to generate these barcodes. DSSP program is used to obtain secondary structure information. The information about strands and the sheet they belong to is also obtained from DSSP file. The orientation between secondary structures is the angle in radians calculated by atan2 method. The first step in generating a “barcode” is the generation of an alpha-numero code (ANCODE). ANCODE is a combination of alphabets, H (for helix), and S (for strand/sheet) followed by a four-digit number divided into two pairs. First pair represents overall SSE count and second pair represents the count of secondary structure each SSE belongs to. For example, S0401in Figure 1(D) signifies that the given strand is the fourth SSE in the overall structure, but belongs to the first sheet. Similarly, H0301 in Figure 1(D) signifies that Helix (H) is the third SSE but is first (01) helix in the overall structure.
The orientation of each SSEs with the previous and successive ones is assigned based on a tableaux representation [Figure 1(C)]. If both secondary structures are pointing within 90° against each other, they are considered parallel (P) and if they are between −135° and +135°, antiparallel. The relative orientations in between are designated as L and R in either directions as shown in Figure 1(C).
BARCODE is derived from ANCODE generated using pdb file. H is always colored black, S is colored based on the corresponding sheet id. Each sheet id is colored unique. For example, Figure 2(A) has seven strands with four strands forming one sheet (green) and the remaining three forms second sheet (blue). Orientations of successive SSEs are represented by the “width” of white space between the bars in barcode image. Orientation and pixel width is as follows, P = 6 units, A = 3 units, R = 4 units, and L = 5 units. Representations of successive SSEs are denoted in ANCODE in the sixth and seventh spaces after a colon. The first letter shows orientation between previous SSE and second letter shows the succeeding one. If the previous SSE and succeeding SSE is missing (as in the case of N terminus and C terminus) it is denoted as “O” [Fig. 1(C,D)]. Thus, secondary structures and topology are encoded in the ANCODE string and further translated to barcode image in TIFF format in MATLAB.
In this methodology article, we attempted to present a new reduced representation of protein structures so as to compare and contrast two structures based on their secondary structure and topology. Apart from the structural and topological information conveyed, we can also quantify the overall comparison by way of a barcode identity index (BII). The two experiments described above are indicative of the utility of the tool. Addressing a scientific problem and comparison with other tools are not within the scope of this article, yet the value of the method for qualitative and quantitative comparison of protein structures may not be discounted. The program is fully downloadable from the webpage http://www.iitg.ac.in/probar/.
Authors acknowledge the contributions of Prof. P. K. Bora of Electrical Engineering at IIT Guwahati for useful suggestions and Rakesh Kumar of Biotechnology, IIT Guwahati in the final formulation of this manuscript and creation of webpage.