Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

Abstract

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.computational protein design | synthetic repeat proteins | de novo backbone design | coarse-grained model D uring the course of evolution, natural proteins may be recruited to new unrelated functions conferring a selective advantage to the organism (1, 2). This accretion of new features and functions is likely to have left behind complex interlocking amino acid dependencies that can make reengineering natural proteins difficult and unpredictable (3). For this reason, we and others hypothesize that it is more desirable to design de novo proteins because these proteins provide a biologically neutral platform onto which functional elements can be grafted (4). Artificial proteins have been designed by decoding simple residue patterning rules that govern the packing of secondary structural elements, and this technique has been particularly successful for α-helical bundle proteins (5-7). An alternative approach is to assemble de novo folds from backbone fragments of known structures or idealized secondary structural elements and use computational protein design methods to design the sequence (4,(8)(9)(10). Both the computational and simpler rules-based design approaches have concentrated on designing proteins consisting of canonical secondary structure linked with loops of minimal length.A class of proteins that has attracted considerable interest is artificial proteins based on repeating structural motifs due to their intrinsic modularity and designability (11). Repeat proteins have applications that include their use as novel nanomaterials (12-14) and as scaffolds for molecular recognition (15, 16). These proteins may be designed using sequence consensus-based rules (17) or computational prot...

Propagation of Fibrillar Structural Forms in Proteins Stopped by Naturally Occurring Short Polypeptide Chain Fragments

“…Nanotechnology provides examples of synthetic beta-solenoid proteins composed from repeated pentatpeptides [ 25 ]. Repetition of structural forms (usually short beta fragments) is intuitively expected given the underlying sequential repetition [ 34 ].…”

Computational protein design with backbone plasticity

“…We propose that the ability to sample directly using coarse-grained potential energy functions enables the efficient incorporation of functional geometric restraints and the use of more sophisticated sampling methods that are more difficult to achieve with fragment insertion methods. Recently, we have successfully applied this fragment-free method to the design of de novo backbone protein design [56].

Figure 2.Sampling backbone loop conformations using a coarse-grained model.Conformational space can be rapidly sampled using a reduced representation before being rebuilt into a full-atom model as part of a hierarchical design strategy.

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Geometrical frustration as a potential design principle for peptide-based assemblies

“…Moreover, the Protein Data Bank contains numerous structures that crystallize within p3-related space groups that can be described in terms of layered, open-framework packing arrangements. Recently, computational design has been employed to optimize protein -protein interfaces within these structures to afford stable, free-standing two-dimensional assemblies of singlemolecule thickness [60], or, alternatively, to create synthetic protein tectons that are capable of self-associating selectively in such packing arrangements [19,58,61]. Perhaps an even greater challenge might be the intentional design of a peptide system that self-assembled into a nanosheet based on a 'frustrated' trigonal unit cell, such as a heterotrimeric system in which the three peptides had distinct sequences and were chemically and physically distinguishable within the two-dimensional assembly.…”