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In protein design, the ultimate test of success is that the designs function as desired. Here, we discuss the utility of cell free protein synthesis (CFPS) as a rapid, convenient and versatile method to screen for activity. We champion the use of CFPS in screening potential designs. Compared to in vivo protein screening, a wider range of different activities can be evaluated using CFPS, and the scale on which it can easily be used—screening tens to hundreds of designed proteins—is ideally suited to current needs. Protein design using physics‐based strategies tended to have a relatively low success rate, compared with current machine‐learning based methods. Screening steps (such as yeast display) were often used to identify proteins that displayed the desired activity from many designs that were highly ranked computationally. We also describe how CFPS is well‐suited to identify the reasons designs fail, which may include problems with transcription, translation, and solubility, in addition to not achieving the desired structure and function. [ABSTRACT FROM AUTHOR]

Periplasmic binding proteins (PBPs) are bacterial proteins commonly used as scaffolds for substrate-detecting biosensors. In these biosensors, effector proteins (for example fluorescent proteins) are inserted into a PBP such that the effector protein's output changes upon PBP-substate binding. The insertion site is often determined by comparison of PBP apo/holo crystal structures, but random insertion libraries have shown that this can miss the best sites. Here, we present a PBP biosensor design method based on residue contact analysis from molecular dynamics. This computational method identifies the best previously known insertion sites in the maltose binding PBP, and suggests further previously unknown sites. We experimentally characterise fluorescent protein insertions at these new sites, finding they too give functional biosensors. Furthermore, our method is sufficiently flexible to both suggest insertion sites compatible with a variety of effector proteins, and be applied to binding proteins beyond PBPs. Author summary: "Biosensors" are microscopic tools that can detect specific molecules of interest and are made of biological building blocks, such as proteins. Upon coming into contact with their target molecule, such as a marker of a specific disease, biosensors change shape and their properties are altered. For example, upon contact with a disease marker, a biosensor could glow brightly to work as a diagnostic, or perhaps it could produce a chemical signal that can be detected by the immune system. Understanding how biosensors change shape in response to their target is key to developing more biosensors with complex responses. In this paper, we use computer simulations of a biosensor component to understand what parameters are important for biosensor design. We use these parameters to generate new biosensors that respond to their target molecule by producing light, but put forward a case that the lesson learned are generalisable enough to inform more complex sensor outputs as well. [ABSTRACT FROM AUTHOR]

The increased availability of user-friendly and accessible computational tools for biomolecular modeling would expand the reach and application of biomolecular engineering and design. For protein modeling, one key challenge is to reduce the complexities of 3D protein folds to sets of parametric equations that nonetheless capture the salient features of these structures accurately. At present, this is possible for a subset of proteins, namely, repeat proteins. The α-helical coiled coil provides one such example, which represents ≈ 3-5% of all known protein-encoding regions of DNA. Coiled coils are bundles of α helices that can be described by a small set of structural parameters. Here we describe how this parametric description can be implemented in an easy-to-use web application, called CCBuilder 2.0, for modeling and optimizing both α-helical coiled coils and polyproline-based collagen triple helices. This has many applications from providing models to aid molecular replacement for X-ray crystallography, in silico model building and engineering of natural and designed protein assemblies, and through to the creation of completely de novo 'dark matter' protein structures. CCBuilder 2.0 is available as a web-based application, the code for which is open-source and can be downloaded freely. . Lay Summary We have created CCBuilder 2.0, an easy to use web-based application that can model structures for a whole class of proteins, the α-helical coiled coil, which is estimated to account for 3-5% of all proteins in nature. CCBuilder 2.0 will be of use to a large number of protein scientists engaged in fundamental studies, such as protein structure determination, through to more-applied research including designing and engineering novel proteins that have potential applications in biotechnology. [ABSTRACT FROM AUTHOR]

The article presents a case study of hemophagocytic lymphohistiocytosis (HLH) in a premature infant. Prenatal and birth history of the baby was discussed. The baby was given a single-volume exchange transfusion when she was admitted to correct her anemia and required multiple platelet and immunoglobulin transfusions which were given on the first 5 days of life due to her persistent thrombocytopenia. The study noted the importance of immediate treatment when there is a strong clinical suspicion for HLH.

In protein design, the ultimate test of success is that the designs function as desired. Here, we discuss the utility of cell free protein synthesis (CFPS) as a rapid, convenient and versatile method to screen for activity. We champion the use of CFPS in screening potential designs. Compared to in vivo protein screening, a wider range of different activities can be evaluated using CFPS, and the scale on which it can easily be used-screening tens to hundreds of designed proteins-is ideally suited to current needs. Protein design using physics-based strategies tended to have a relatively low success rate, compared with current machine-learning based methods. Screening steps (such as yeast display) were often used to identify proteins that displayed the desired activity from many designs that were highly ranked computationally. We also describe how CFPS is well-suited to identify the reasons designs fail, which may include problems with transcription, translation, and solubility, in addition to not achieving the desired structure and function., (© 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)