The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

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Advances in protein structure prediction and design

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. We develop relational language models for protein sequences that directly condition on a graph specification of the target structure. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

/pdf/generative-models-for-graph-based-protein-design-4m3tey8cl6.pdf

Generative models for graph-based protein design

Proteins are designed to bind to specific surfaces while also presenting a programmed surface superstructure. There is a general need for the engineering of protein-like molecules that organize into geometrically specific superstructures on molecular surfaces, directing further functionalization to create richly textured, multilayered assemblies. Here we describe a computational approach whereby the surface properties and symmetry of a targeted surface define the sequence and superstructure of surface-organizing peptides. Computational design proceeds in a series of steps that encode both surface recognition and favorable intersubunit packing interactions. This procedure is exemplified in the design of peptides that assemble into a tubular structure surrounding single-walled carbon nanotubes (SWNTs). The geometrically defined, virus-like coating created by these peptides converts the smooth surfaces of SWNTs into highly textured assemblies with long-scale order, capable of directing the assembly of gold nanoparticles into helical arrays along the SWNT axis.

Computational Design of Virus-Like Protein Assemblies on Carbon

We developed a de novo protein design strategy to swiftly engineer decoys for neutralizing pathogens that exploit extracellular host proteins to infect the cell. Our pipeline allowed the design, validation, and optimization of de novo hACE2 decoys to neutralize SARS-CoV-2. The best decoy, CTC-445.2, binds with low nanomolar affinity and high specificity to the RBD of the spike protein. Cryo-EM shows that the design is accurate and can simultaneously bind to all three RBDs of a single spike protein. Because the decoy replicates the spike protein target interface in hACE2, it is intrinsically resilient to viral mutational escape. A bivalent decoy, CTC-445.2d, shows ~10-fold improvement in binding. CTC-445.2d potently neutralizes SARS-CoV-2 infection of cells in vitro and a single intranasal prophylactic dose of decoy protected Syrian hamsters from a subsequent lethal SARS-CoV-2 challenge.

https://science.sciencemag.org/content/sci/early/2020/11/19/science.abe0075.full.pdf

De novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2

Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure-sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/pro.2610

Rapid search for tertiary fragments reveals protein sequence–structure relationships

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

https://www.pnas.org/content/pnas/117/2/1059.full.pdf

A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures

Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.

https://www.pnas.org/content/pnas/113/47/E7438.full.pdf

Tertiary alphabet for the observable protein structural universe

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1.

The ability to routinely design functional proteins, in a targeted manner, would have enormous implications for biomedical research and therapeutic development. Computational protein design (CPD) offers the potential to fulfill this need, and though recent years have brought considerable progress in the field, major limitations remain. Current state-of-the-art approaches to CPD aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a robust general solution to CPD has yet to be found. Here we propose a fundamentally novel design framework - one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of inter-atomic interactions. Specifically, we systematically decompose the target structure to be designed into structural building blocks we call TERMs (tertiary motifs) and use rapid structure search against the Protein Data Bank (PDB) to identify sequence patterns associated with each TERM from known protein structures that contain it. These results are then combined to produce a sequence-level pseudo-energy model that can score any sequence for compatibility with the target structure. This model can then be used to extract the optimal-scoring sequence via combinatorial optimization or otherwise sample the sequence space predicted to be well compatible with folding to the target. Here we carry out extensive computational analyses, showing that our method, which we dub dTERMen (design with TERM energies): 1) produces native-like sequences given native crystallographic or NMR backbones, 2) produces sequence-structure compatibility scores that correlate with thermodynamic stability, and 3) is able to predict experimental success of designed sequences generated with other methods, and 4) designs sequences that are found to fold to the desired target by structure prediction more frequently than sequences designed with an atomistic method. As an experimental validation of dTERMen, we perform a total surface redesign of Red Fluorescent Protein mCherry, marking a total of 64 residues as variable. The single sequence identified as optimal by dTERMen harbors 48 mutations relative to mCherry, but nevertheless folds, is monomeric in solution, exhibits similar stability to chemical denaturation as mCherry, and even preserves the fluorescence property. Our results strongly argue that the PDB is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. This is highly significant, given that the structural database will only continue to grow, and signals the possibility of a whole host of novel data-driven CPD methods. Because such methods are likely to have orthogonal strengths relative to existing techniques, they could represent an important step towards removing remaining barriers to robust CPD.

https://www.biorxiv.org/content/biorxiv/early/2018/10/01/431635.full.pdf

Jianfu Zhou

Papers

Rapid search for tertiary fragments reveals protein sequence–structure relationships

A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures

Tertiary alphabet for the observable protein structural universe

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1.

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures