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Tsuchihashi, R.

Publications and source records attributed to Tsuchihashi, R..

2 recordsLinked to original sources

De novo design of a peptide ligand for specific affinity purification of human complement C1q

Affinity purification is a essential technique for isolating highly purified proteins; however, generating affinity ligands require significant time and financial investment. To address these limitations, this study proposes a novel affinity chromatography method utilizing in silico-designed cyclic peptides as ligands. Targeting Complement C1q (C1q), a plasma protein that plays crucial roles in classical complement pathway, we employed the biomolecular structure prediction model, AlphaFold2, to design specific binding cyclic peptides. Based on these designs, we synthesized lariat-type cyclic peptides characterized by disulfide cyclization and biotinylation, which were subsequently immobilized on streptavidin carriers. Performance tests confirmed that the resulting column specifically captured C1q, allowing for elution via a standard NaCl concentration gradient. Notably, high selectivity was preserved even in the presence of plasma, underscoring the ligands practical robustness. By overcoming traditional constraints through (1) rapid and simple design, (2) high specificity, and (3) universal versatility without genetic modification, this de novo design strategy represents a potential breakthrough in protein purification technologies. HighlightsO_LIAI-driven de novo design generated a specific cyclic peptide ligand for Complement C1q C_LIO_LIThe synthetic ligand enabled one-step purification of Complement C1q directly from human plasma C_LIO_LIMild elution conditions preserved the targets oligomeric structure and native interactome C_LIO_LIThis label-free strategy offers a rapid, low-cost alternative to antibody-based chromatography C_LI

bioinformatics↗

Cyclic peptides space: The methodology of sequence selection to cover the comprehensive physical properties

Cyclic peptides have emerged as a pivotal modality for next-generation therapeutics, due to their superior biocompatibility, high selectivity, and structural stability. While AI-driven peptide design has advanced rapidly, conventional optimization algorithms are often constrained by initialization biases, which impede the efficient exploration of the vast chemical space. Here, we propose a novel methodology that integrates the protein language model ESM-2 with cyclic permutation averaging of embeddings to resolve this bottleneck. This approach establishes a comprehensive "peptide space", a high-dimensional vector representation that encapsulates the physicochemical and structural attributes of cyclic peptides. Our analysis reveals that random sequence selection results in a heterogeneous distribution within this space, potentially underrepresenting specific functional regions. Conversely, navigating this defined peptide space enables the selection of libraries that uniformly span diverse molecular properties. In a proof-of-concept study designing binders for {beta}2-microglobulin ({beta}2m), we demonstrate that initial sequences uniformly sampled from our peptide space yield superior candidates more efficiently than those derived from random selection. Furthermore, this framework facilitates the quantitative assessment of mutational perturbations on global peptide properties, supporting rational decision-making for both broad exploration and local optimization. This "peptide space" concept provides a foundational framework for defining appropriate search boundaries and enhancing computational efficiency in AI-mediated drug discovery. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/710724v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1dd903eorg.highwire.dtl.DTLVardef@128f941org.highwire.dtl.DTLVardef@1041e13org.highwire.dtl.DTLVardef@1527b25_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗