Prolonged function and optimization of actomyosin motility for upscaled network-based biocomputation

Abstract

Abstract Significant advancements have been made towards exploitation of naturally available molecular motors and their associated cytoskeletal filaments in nanotechnological applications. For instance, myosin motors and actin filaments from muscle have been used with the aims to establish new approaches in biosensing and network-based biocomputation. The basis for these developments is a version of the in vitro motility assay (IVMA) where surface-adsorbed myosin motors propel the actin filaments along suitably derivatized nano-scale channels on nanostructured chips. These chips are generally assembled into custom-made microfluidic flow cells. For effective applications, particularly in biocomputation, it is important to appreciably prolong function of the biological system. Here, we systematically investigated potentially critical factors necessary to achieve this, such as biocompatibility of different components of the flow cell, the degree of air exposure, assay solution composition and nanofabrication methods. After optimizing these factors we prolonged the function of actin and myosin in nanodevices for biocomputation from <20 min to >60 min. In addition, we demonstrated that further optimizations could increase motility run times to >20 h. Of great importance for the latter development was a switch of glucose oxidase in the chemical oxygen scavenger system (glucose oxidase–glucose–catalase) to pyranose oxidase, combined with the use of blocking actin (non-fluorescent filaments that block dead motors). To allow effective testing of these approaches we adapted commercially available microfluidic channel slides, for the first time demonstrating their usefulness in the IVMA. As part of our study, we also demonstrate that myosin motor fragments can be stored at −80 °C for more than 10 years before use for nanotechnological purposes. This extended shelf-life is important for the sustainability of network-based biocomputation.