Gail J. Bartlett

Computational design of water-soluble α-helical barrels

Building with alphahelical coiled coils Understanding how proteins fold into well-defined three-dimensional structures has been a longstanding challenge. Increased understanding has led to increased success at designing proteins that mimic existing protein folds. This raises the possibility of custom design of proteins with structures not seen in nature. Thomson et al. describe the design of channelcontaining α-helical barrels, and Huang et al. designed hyperstable helical bundles. Both groups used rational and computational design to make new protein structures based on α-helical coiled coils but took different routes to reach different target structures. Science, this issue p. 485, p. 481 Protein design expands the repertoire of coiled-coil structures to α-helical barrels and hyperstable helical bundles. The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function.

A de novo peptide hexamer with a mutable channel

The design of new proteins that expand the repertoire of natural protein structures represents a formidable challenge. Success in this area would increase understanding of protein structure, and present new scaffolds that could be exploited in biotechnology and synthetic biology. Here we describe the design, characterisation and X-ray crystal structure of a new coiled-coil protein. The de novo sequence forms a stand-alone, parallel, 6-helix bundle with a channel running through it. Although lined exclusively by hydrophobic leucine and isoleucine side chains, the 6 Å channel is permeable to water. One layer of leucine residues within the channel is mutable accepting polar aspartic acid (Asp) and histidine (His) side chains, and leading to subdivision and organization of solvent within the lumen. Moreover, these mutants can be combined to form a stable and unique (Asp-His)3 heterohexamer. These new structures provide a basis for engineering de novo proteins with new functions.

CCBuilder: an interactive web-based tool for building, designing and assessing coiled-coil protein assemblies

Motivation: The ability to accurately model protein structures at the atomistic level underpins efforts to understand protein folding, to engineer natural proteins predictably and to design proteins de novo. Homology-based methods are well established and produce impressive results. However, these are limited to structures presented by and resolved for natural proteins. Addressing this problem more widely and deriving truly ab initio models requires mathematical descriptions for protein folds; the means to decorate these with natural, engineered or de novo sequences; and methods to score the resulting models. Results: We present CCBuilder, a web-based application that tackles the problem for a defined but large class of protein structure, the α-helical coiled coils. CCBuilder generates coiled-coil backbones, builds side chains onto these frameworks and provides a range of metrics to measure the quality of the models. Its straightforward graphical user interface provides broad functionality that allows users to build and assess models, in which helix geometry, coiled-coil architecture and topology and protein sequence can be varied rapidly. We demonstrate the utility of CCBuilder by assembling models for 653 coiled-coil structures from the PDB, which cover >96% of the known coiled-coil types, and by generating models for rarer and de novo coiled-coil structures. Availability and implementation: CCBuilder is freely available, without registration, at http://coiledcoils.chm.bris.ac.uk/app/cc_builder/ Contact: D.N.Woolfson@bristol.ac.uk or Chris.Wood@bristol.ac.uk

Signatures of n→π* interactions in proteins

The folding of proteins is directed by a variety of interactions, including hydrogen bonding, electrostatics, van der Waals interactions, and the hydrophobic effect. We have argued previously that an n→π* interaction between carbonyl groups be added to this list. In an n→π* interaction, the lone pair (n) of one carbonyl oxygen overlaps with the π* antibonding orbital of another carbonyl group. The tendency of backbone carbonyl groups in proteins to engage in this interaction has consequences for the structures of folded proteins that we unveil herein. First, we employ density functional theory to demonstrate that the n→π* interaction causes the carbonyl carbon to deviate from planarity. Then, we detect this signature of the n→π* interaction in high‐resolution structures of proteins. Finally, we demonstrate through natural population analysis that the n→π* interaction causes polarization of the electron density in carbonyl groups and detect that polarization in the electron density map of cholesterol oxidase, further validating the existence of n→π* interactions. We conclude that the n→π* interaction is operative in folded proteins.

How do miniproteins fold

A high-throughput study yields libraries of miniproteins that help to explain how proteins are stabilized How does the amino acid sequence of a protein chain determine and maintain its three-dimensional folded state? Answering this question—a key aspect of the protein-folding problem (1)—would help to explain how multiple noncovalent interactions conspire to assemble and stabilize complicated biomolecular structures; to predict protein structure and function from sequence for proteins that cannot be characterized experimentally; and to design new protein structures that do not exist in nature (2). On page 168 of this issue, Rocklin et al. use parallel protein design on a massive scale to create thousands of miniprotein variants and to determine what sequences specify and stabilize these structures (3). The work opens up considerable possibilities for protein folding and design.

Applying graph theory to protein structures: an atlas of coiled coils

Abstract Motivation To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Here, we use tools from graph theory to define an Atlas classification scheme for automatically categorizing certain protein substructures. Results Focusing on the α-helical coiled coils, which are ubiquitous protein-structure and protein–protein interaction motifs, we present a suite of computational resources designed for analyzing these assemblies. iSOCKET enables interactive analysis of side-chain packing within proteins to identify coiled coils automatically and with considerable user control. Applying a graph theory-based Atlas classification scheme to structures identified by iSOCKET gives the Atlas of Coiled Coils, a fully automated, updated overview of extant coiled coils. The utility of this approach is illustrated with the first formal classification of an emerging subclass of coiled coils called α-helical barrels. Furthermore, in the Atlas, the known coiled-coil universe is presented alongside a partial enumeration of the ‘dark matter’ of coiled-coil structures; i.e. those coiled-coil architectures that are theoretically possible but have not been observed to date, and thus present defined targets for protein design. Availability and implementation iSOCKET is available as part of the open-source GitHub repository associated with this work (https://github.com/woolfson-group/isocket). This repository also contains all the data generated when classifying the protein graphs. The Atlas of Coiled Coils is available at: http://coiledcoils.chm.bris.ac.uk/atlas/app.