MOTIVATION: Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. We propose a sparse and interpretable representation for proteins, based on evolutionarily conserved fragments. Specifically, we use a curated set of 40 functional and evolutionarily conserved fragments as an alphabet to build Fragment Graphs and Fragment Sets. These fragment-based representations are both lightweight and functionally informative, capturing up to 55% more variance using fewer than 13 of the dimensions required by traditional methods. RESULTS: On a dataset of 215 functionally diverse proteins, our approach creates more coherent functional clusters than traditional sequence- and structure-based methods, even among proteins with ≤30% sequence identity. Fragment-based searches of protein databases achieve accuracies comparable to traditional methods, while using 90% fewer tokens per protein. These searches execute ∼68.7× faster than RMSD-based structural methods and ∼1.64× faster than sequence-based methods, even including fragment pre-processing overhead. Additionally, we show that our representation effectively guides RFDiffusion for protein backbone generation with functional recovery rates higher than 40%. In summary, our fragment-based representation offers a scalable and interpretable alternative for the next generation of protein design tools for backbone design, sequence design, and functional similarity searches within protein structure databases. AVAILABILITY: https://github.com/wells-wood-research/tessera.

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. Here, we present a PBP biosensor design method based on residue contact analysis from molecular dynamics. This computational method identifies the best previously known insertion sites in the maltose binding PBP, and suggests further previously unknown sites. We experimentally characterise fluorescent protein insertions at these new sites, finding they too give functional biosensors. Furthermore, our method is sufficiently flexible to both suggest insertion sites compatible with a variety of effector proteins, and be applied to binding proteins beyond PBPs.

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.

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. While programmes are available for this, there is a need for a real-time web application that is accessible to non-expert users. RESULTS: Here, we present BAlaS, an interactive web application for performing CASM via BudeAlaScan and visualizing its results. BAlaS is interactive and intuitive to use. Results are displayed directly in the browser for the structure being interrogated enabling their rapid inspection. BAlaS has broad applications in areas, such as drug discovery and protein-interface design. AVAILABILITY AND IMPLEMENTATION: BAlaS works on all modern browsers and is available through the following website: https://balas.app. The project is open source, distributed using an MIT license and is available on GitHub (https://github.com/wells-wood-research/balas).

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.

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. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the 'dark matter of protein-fold space'. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology. AVAILABILITY AND IMPLEMENTATION: A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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/.