Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints.

Coiled-coil proteins provide an excellent scaffold for multistate protein design due to their established sequence-to-structure relationships and ability to switch conformations in response to external stimuli, such as changes in pH or temperature. However, the computational design of multistate coiled-coil protein assemblies is challenging, as it requires accurate estimates of the free energy differences between multiple alternative coiled-coil conformations. Here, we demonstrate how this challenge can be tackled using metadynamics simulations with orientational, positional and conformational restraints.

drMD: Molecular Dynamics for Experimentalists.

Molecular dynamics (MD) simulations can be used by protein scientists to investigate a wide array of biologically relevant properties such as the effects of mutations on a protein's structure and activity, or probing intermolecular interactions with small molecule substrates or other macromolecules. Within the world of computational structural biology, several programs have become popular for running these simulations, but each of these programs requires a significant time investment from the researcher to run even simple simulations. Even after learning how to run and analyse simulations, many elements remain a "black box.

Installation of an organocatalyst into a protein scaffold creates an artificial Stetterase.

Using a protein scaffold covalently functionalised with a thiamine-inspired N-heterocyclic carbene (NHC), we created an artificial Stetterase (ArtiSt) which catalyses a stereoselective, intramolecular Stetter reaction. We demonstrate that ArtiSt functions under ambient conditions with low catalyst loading. Furthermore, activity can be increased >20 fold by altering the protein scaffold.

Applications of cell free protein synthesis in protein design.

In protein design, the ultimate test of success is that the designs function as desired. Here, we discuss the utility of cell free protein synthesis (CFPS) as a rapid, convenient and versatile method to screen for activity. We champion the use of CFPS in screening potential designs.

Computational design of Periplasmic binding protein biosensors guided by molecular dynamics.

Periplasmic binding proteins (PBPs) are bacterial proteins commonly used as scaffolds for substrate-detecting biosensors. In these biosensors, effector proteins (for example fluorescent proteins) are inserted into a PBP such that the effector protein's output changes upon PBP-substate binding. The insertion site is often determined by comparison of PBP apo/holo crystal structures, but random insertion libraries have shown that this can miss the best sites.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost.

Molecular insights into LINC complex architecture through the crystal structure of a luminal trimeric coiled-coil domain of SUN1.

The LINC complex, consisting of interacting SUN and KASH proteins, mechanically couples nuclear contents to the cytoskeleton. In meiosis, the LINC complex transmits microtubule-generated forces to chromosome ends, driving the rapid chromosome movements that are necessary for synapsis and crossing over. In somatic cells, it defines nuclear shape and positioning, and has a number of specialised roles, including hearing.

Differential sensing with arrays of de novo designed peptide assemblies.

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing.

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.

Generation of Photocaged Nanobodies for Intracellular Applications in an Animal Using Genetic Code Expansion and Computationally Guided Protein Engineering.

Nanobodies are becoming increasingly popular as tools for manipulating and visualising proteins in vivo. The ability to control nanobody/antigen interactions using light could provide precise spatiotemporal control over protein function. We develop a general approach to engineer photo-activatable nanobodies using photocaged amino acids that are introduced into the target binding interface by genetic code expansion.

DE-STRESS: a user-friendly web application for the evaluation of protein designs.

De novo protein design is a rapidly growing field, and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly.

De Novo Designed Peptide and Protein Hairpins Self-Assemble into Sheets and Nanoparticles.

The design and assembly of peptide-based materials has advanced considerably, leading to a variety of fibrous, sheet, and nanoparticle structures. A remaining challenge is to account for and control different possible supramolecular outcomes accessible to the same or similar peptide building blocks. Here a de novo peptide system is presented that forms nanoparticles or sheets depending on the strategic placement of a "disulfide pin" between two elements of secondary structure that drive self-assembly.

BAlaS: fast, interactive and accessible computational alanine-scanning using BudeAlaScan.

Motivation: In experimental protein engineering, alanine-scanning mutagenesis involves the replacement of selected residues with alanine to determine the energetic contribution of each side chain to forming an interaction. For example, it is often used to study protein-protein interactions. However, such experiments can be time-consuming and costly, which has led to the development of programmes for performing computational alanine-scanning mutagenesis (CASM) to guide experiments.

Peptide Assembly Directed and Quantified Using Megadalton DNA Nanostructures.

In nature, co-assembly of polypeptides, nucleic acids, and polysaccharides is used to create functional supramolecular structures. Here, we show that DNA nanostructures can be used to template interactions between peptides and to enable the quantification of multivalent interactions that would otherwise not be observable. Our functional building blocks are peptide-oligonucleotide conjugates comprising designed dimeric coiled-coil peptides covalently linked to oligonucleotide tags.

Navigating the Structural Landscape of De Novo α-Helical Bundles.

The association of amphipathic α helices in water leads to α-helical-bundle protein structures. However, the driving force for this-the hydrophobic effect-is not specific and does not define the number or the orientation of helices in the associated state. Rather, this is achieved through deeper sequence-to-structure relationships, which are increasingly being discerned.

Maintaining and breaking symmetry in homomeric coiled-coil assemblies.

In coiled-coil (CC) protein structures α-helices wrap around one another to form rope-like assemblies. Most natural and designed CCs have two-four helices and cyclic (C) or dihedral (D) symmetry. Increasingly, CCs with five or more helices are being reported.

Applying graph theory to protein structures: an Atlas of coiled coils.

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.

CCBuilder 2.0: Powerful and accessible coiled-coil modeling.

Unlabelled: The increased availability of user-friendly and accessible computational tools for biomolecular modeling would expand the reach and application of biomolecular engineering and design. For protein modeling, one key challenge is to reduce the complexities of 3D protein folds to sets of parametric equations that nonetheless capture the salient features of these structures accurately. At present, this is possible for a subset of proteins, namely, repeat proteins.

ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design.

Motivation: The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users.

Results: Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules.

Modular Design of Self-Assembling Peptide-Based Nanotubes.

An ability to design peptide-based nanotubes (PNTs) rationally with defined and mutable internal channels would advance understanding of peptide self-assembly, and present new biomaterials for nanotechnology and medicine. PNTs have been made from Fmoc dipeptides, cyclic peptides, and lock-washer helical bundles. Here we show that blunt-ended α-helical barrels, that is, preassembled bundles of α-helices with central channels, can be used as building blocks for PNTs.

De novo protein design: how do we expand into the universe of possible protein structures?

Protein scientists are paving the way to a new phase in protein design and engineering. Approaches and methods are being developed that could allow the design of proteins beyond the confines of natural protein structures. This possibility of designing entirely new proteins opens new questions: What do we build? How do we build into protein-structure space where there are few, if any, natural structures to guide us? To what uses can the resulting proteins be put? And, what, if anything, does this pursuit tell us about how natural proteins fold, function and evolve? We describe the origins of this emerging area of fully de novo protein design, how it could be developed, where it might lead, and what challenges lie ahead.

Computational design of water-soluble α-helical barrels.

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.

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.