Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Selenocysteine (Sec) is the 21st non-standard proteinogenic amino acid. Due to the particularity of the codon encoding Sec, the selenoprotein synthesis needs to be completed by unique mechanisms in specific biological systems. In this paper, the underlying mechanisms for the biosynthesis and incorporation of Sec into selenoprotein were comprehensively reviewed on five aspects: (i) the specific biosynthesis mechanism of Sec and the role of its internal influencing factors (SelA, SelB, SelC, SelD, SPS2 and PSTK); (ii) the elements (SECIS, PSL, SPUR and RF) on mRNA and their functional mechanisms; (iii) the specificity (either translation termination or translation into Sec) of UGA; (iv) the structure–activity relationship and action mechanism of SelA, SelB, SelC and SelD; and (v) the operating mechanism of two key enzyme systems for inorganic selenium source flow before Sec synthesis. Lastly, the size of the translation initiation interval, other action modes of SECIS and effects of REPS (Repetitive Extragenic Palindromic Sequences) that affect the incorporation efficiency of Sec was also discussed to provide scientific basis for the large-scale industrial fermentation for the production of selenoprotein.

It has tremendous values for both drug discovery and basic research to develop a solid bioinformatical tool for guiding peptide reagent design. Based on the physical and chemical properties of amino acids, a new strategy for peptide reagent design, the so‐called AABPD (amino acid based‐peptide design), is proposed. The peptide samples in a training dataset are described by a series of HMLP (heuristic molecular lipophilicity potential) parameters and other physicochemical properties of amino acid residues that form a three‐dimensional data matrix where each component is defined by three indexes: the first index refers to the peptide samples, the second to the amino acid positions, and the third to the amino acid parameters. The binding free energy between a peptide ligand and its protein receptor is calculated by a linear free energy equation through the physicochemical parameters, resulting in a set of simultaneous linear equations between the bioactivity of the peptides and the physicochemical properties of amino acids. An iterative double least square technique is developed for the solution of the three‐dimensional simultaneous linear equation set to determine the amino acid position coefficients of peptide sequence and the physicochemical parameter coefficients of amino acid residues alternately. The two sets of coefficients thus obtained are used for predicting the bioactivity of other query peptide reagents. Two calculation examples, the peptide substrate specificity of the SARS coronavirus 3C‐like proteinase and the affinity prediction for epitope‐peptides with Class I MHC molecules are studied by using the peptide reagent design strategy. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007