Tuning dynamic DNA- and peptide-driven self-assembly in DNA-peptide conjugates

DNA-peptide conjugates offer an opportunity to marry the benefits of both biomolecules, such as the high level of control and programmability found with DNA and the chemical diversity and biological stability of peptides. These hybrid systems offer great potential in fields such as therapeutics, nanotechnology, and robotics to name a few. Using the first DNA-β-turn peptide conjugate, we present three studies designed to investigate the self-assembly of DNA-peptide conjugates over a period of 28 days. Time-course studies, such as these have not been previously conducted for DNA-peptide conjugates, although they are common in pure peptide assembly, for example in amyloid research. By using aging studies to assess the structures produced, we gain insights into the dynamic nature of these systems. The first study explores the influence varying amounts of DNA-peptide conjugates have on the self-assembly of our parent peptide. Study 2 explores how DNA and peptide can work together to change the structures observed during aging. Study 3 investigates the presence of orthogonality within our system by switching the DNA and peptide control on and off independently. These results show that two orthogonal self-assemblies can be combined and operated either independently or in tandem within a single macromolecule, with both spatial and temporal effects upon the resultant nanostructures.

Tuning dynamic DNA- and peptide-driven self-assembly in DNA-peptide conjugates

DNA-peptide conjugates offer an opportunity to marry the benefits of both biomolecules, such as the high level of control and programmability found with DNA and the chemical diversity and biological stability of peptides. These hybrid systems offer great potential in fields such as therapeutics, nanotechnology, and robotics to name a few. Using the first DNA-β-turn peptide conjugate, we present three studies designed to investigate the self-assembly of DNA-peptide conjugates over a period of 28 days. Time-course studies, such as these have not been previously conducted for DNA-peptide conjugates, although they are common in pure peptide assembly, for example in amyloid research. By using aging studies to assess the structures produced, we gain insights into the dynamic nature of these systems. The first study explores the influence varying amounts of DNA-peptide conjugates have on the self-assembly of our parent peptide. Study 2 explores how DNA and peptide can work together to change the structures observed during aging. Study 3 investigates the presence of orthogonality within our system by switching the DNA and peptide control on and off independently. These results show that two orthogonal self-assemblies can be combined and operated either independently or in tandem within a single macromolecule, with both spatial and temporal effects upon the resultant nanostructures.

Integrated de novo Gene Prediction and Peptide Assembly of Metagenomic Sequencing Data

ABSTRACTMetagenomics is the study of all genomic content presented in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling, and peptide assembly. Unfortunately, their informational connection and dependency have been overlooked, and each has been formulated as an independent problem. In this work, we develop a sophisticated workflow called integrated Metagenomic Protein Predictor (iMPP), which leverages the informational dependencies for better de novo functional analysis. iMPP contains three novel modules: a hybrid assembly graph generation module, a graph-based gene calling module, and a peptide assembly-based refinement module. iMPP significantly improved the existing gene calling sensitivity on unassembled fragmented reads, achieving a 92% - 97% recall rate at a high precision level (>90%). iMPP further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins and delivering more hypothetical protein sequences. The high performance of iMPP can provide a more comprehensive and unbiased view of the microbial communities under investigation. iMPP is freely available from https://github.com/Sirisha-t/iMPP.