1.
Reprogramming extracellular vesicles with engineered proteins.
Shi, X, Cheng, Q, Zhang, Y
Methods (San Diego, Calif.). 2020;:95-102
Abstract
Extracellular vesicles (EVs) have been emerging as a new class of cell-free therapy for the treatment of a variety of diseases, including cancer, tissue injuries, and inflammatory diseases. Reprograming native EVs by genetic engineering and other approaches offers an attractive prospect of extending therapeutic capabilities of EVs beyond their natural functions and properties. In this review article, we survey the state-of-the-art methods of EVs engineering and summarize major therapeutic applications of the reprogrammed EVs.
2.
An Evolution-Based Approach to De Novo Protein Design.
Brender, JR, Shultis, D, Khattak, NA, Zhang, Y
Methods in molecular biology (Clifton, N.J.). 2017;:243-264
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Abstract
EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs.
3.
An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis.
Mitra, P, Shultis, D, Brender, JR, Czajka, J, Marsh, D, Gray, F, Cierpicki, T, Zhang, Y
PLoS computational biology. 2013;(10):e1003298
Abstract
Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.