Population-based heteropolymer design to mimic protein mixtures.

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2-4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

Efficient independent vector extraction of dominant source (L).

The complete decomposition performed by blind source separation is computationally demanding and superfluous when only the speech of one specific target speaker is desired. This letter proposes a computationally efficient blind source extraction method based on the fast fixed-point optimization algorithm under the mild assumption that the average power of the source of interest outweighs the interfering sources. Moreover, a one-unit scaling operation is designed to solve the scaling ambiguity for source extraction. Experiments validate the efficacy of the proposed method in extracting the dominant source.

Robust direction-of-arrival estimation for a target speaker based on multi-task U-net based direct-path dominance test.

A dedicated multi-task network for a direct-path dominance test has been proposed recently, based on which the robustness of the direction-of-arrival estimation for a target speaker tends to be significantly improved. In this Letter, the network is further refined to avoid the original two-stage processing. Moreover, the benefit and generalization of the multi-task network are confirmed by comparison with a single-task network on a significantly larger database. The efficacy of the proposed method in high noisy environments is also validated in real application environments.