Rationally seeded computational protein design of ɑ-helical barrels.

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

PDBench: evaluating computational methods for protein-sequence design.

SUMMARY: Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target. AVAILABILITY AND IMPLEMENTATION: https://github.com/wells-wood-research/PDBench. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CC+ : A searchable database of validated coiled coils in PDB structures and AlphaFold2 models.

α-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more α helices wrap around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+ , from the RCSB Protein Data Bank (PDB). Here, we report an update of CC+ following an update of SOCKET (to Socket2) and the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains ≈12,000 coiled-coil assemblies from experimentally determined structures, and ≈120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be updated automatically. We envisage that CC+ could be used to understand coiled-coil assemblies and their sequence-to-structure relationships, and to aid protein design and engineering.