Design of structurally distinct proteins using strategies inspired by evolution

By Tim M. Jacobs, Benfeard Williams, T. Williams, X. Xu, A. Eletsky, J. Federizon, Thomas Szyperski, Brian Kuhlman. 2016

134 citations recorded by the source corpus.

Building new proteins from the old Proteins are the workhorses of biology. Designing new, stable proteins with functions desirable in biotechnology or biomedicine remains challenging. Jacobs et al. developed a computational method called SEWING that designs proteins using pieces of existing structures (see the Perspective by Netzer and Fleishman). The new proteins can contain structural features such as pockets or grooves that are required for function. The solved structures of two designed proteins agreed well with the design models. The method allows rapid design of a diverse set of structures that will facilitate functional design. Science, this issue p. 687; see also p. 657 A design strategy builds new protein structures by recombining pieces of existing protein domains. Natural recombination combines pieces of preexisting proteins to create new tertiary structures and functions. We describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C. High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models. This method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Principles for computational design of binding antibodies (2017, background): “…a protein fold and its binding activity, the last step optimizes both antibody stability and binding energy jointly (15); previous computational-design algorithms, by contrast, concentrated on only one feature, either stability or binding, depending on the application (5, 6, 8, 9, 16, 17).”
Sampling of structure and sequence space of small protein folds (2021, methodology): “Design trajectories of most Ros-setta de novo backbone design protocols have used prede ﬁ ned ABEGO sequences (as a “ blueprint ” ) with the exception of the SEWING method, which stitches together alpha-helices 12 .”
Protein sequence design with a learned potential (2020, background): “Computational protein design has emerged as powerful tool for studying the conformational space of proteins, and has allowed us to expand the space of known protein folds [1, 2, 3, 4, 5, 6], create variations on existing topologies [7, 8, 9, 10, 11], and access the expansive space of sequences yet t”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “The structure extension with native-substructure graphs (SEWING) method (36) combines continuous or discontinuous helical building blocks from existing proteins (Fig.”
Essentials of de novo protein design: Methods and applications (2018, background): “Jacobs et al. developed the structure extension with native-substructure graphs (SEWING) method that allows to recombine pieces of secondary structures from natural proteins, and therefore allows the design of proteins containing irregularities, such as kinked helices and small buried cavities 36 (s”
Modular repeat protein sculpting using rigid helical junctions (2020, unknown): “Since the helix-fusion approach is similar to SEWING, which has been previously experimentally validated (2, 3), we focused our experimental characterization on designs made using the Rosetta fragment assembly approach.”
Perturbing the energy landscape for improved packing during computational protein design (2020, unknown): “For one of the eight pools of designs (the four-helix bundles, denoted HHHH in Figure 5), we performed step (a) by stitching together fragments of helices and loops using a method that is similar to the SEWING protocol.(13) As input for this approach, we extracted libraries of helical fragments and ”
Fully differentiable full-atom protein backbone generation (2019, background): “Current state-of-the-art methods for backbone generation rely on either sampling or assembling native backbone fragments (15; 16).”