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Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers

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Research in Computational Molecular Biology (RECOMB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10812))

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Abstract

Peptide arrays measure the binding intensity of a specific protein to thousands of amino acid peptides. By using peptides that cover all k-mers, a comprehensive picture of the binding spectrum is obtained. Researchers would like to measure binding to the longest k-mer possible, but are constrained by the number of peptides that can fit into a single microarray. A key challenge is designing a minimum number of peptides that cover all k-mers. Here, we suggest a novel idea to reduce the length of the sequence covering all k-mers by utilizing a unique property of the peptide synthesis process. Since the synthesis can start from both ends of the peptide template, it is enough to cover each k-mer or its reverse, and use the same template twice: in forward and reverse. Then, the computational problem is to generate a minimum length sequence that for each k-mer either contains it or its reverse. We developed an algorithm ReverseCAKE to generate such a sequence. ReverseCAKE runs in time linear in the output size and is guaranteed to produce a sequence that is longer by at most \(\varTheta (\sqrt{n}\log {n})\) characters compared to the optimum n. The obtained saving factor by ReverseCAKE approaches the theoretical lower bound as k increases. In addition, we formulated the problem as an integer linear program and empirically observed that the solutions obtained by ReverseCAKE are near-optimal. Through this work we enable more effective design of peptide microarrays.

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Correspondence to Yaron Orenstein .

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Appendix

Appendix

Fig. 4.
figure 4

Results and performance of ReverseCAKE and the ILP solver. (A) Results of sequence lengths are portrayed as ratios to an original de Bruijn sequence (\(|\varSigma |^k\)). The lower bound is from Proposition 1. The dashed red line is at half. (B,C) Runtimes and maximum memory usage of the algorithms, respectively. Y-axis is on a log-scale. (Color figure online)

Table 1. Lengths of reverse de Bruijn sequences produced by ReverseCAKE and an ILP solver. The columns are organized as follows: (i) the alphabet, where aa stands for amino acid; (ii) the length of a regular de Bruijn sequence that does not exploit reverse peptide synthesis; (iii) the lower bound on RdB sequence length (Proposition 1); (iv–v) the lengths of the sequence computed by ReverseCAKE (Sect. 3.3) and an ILP solver that reached an optimal solution (Sect. 3.4); (vi) the saving factor is the ratio between an optimal RdB sequence and a de Bruijn sequence.
Table 2. Performance evaluation of ReverseCAKE and an ILP solver. The runtime and maximum memory usage are reported in seconds (sec) and kilobytes (KB).

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Orenstein, Y. (2018). Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers. In: Raphael, B. (eds) Research in Computational Molecular Biology. RECOMB 2018. Lecture Notes in Computer Science(), vol 10812. Springer, Cham. https://doi.org/10.1007/978-3-319-89929-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-89929-9_10

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