De novo backbone scaffolds for protein design

Heterologous Sarbecovirus Receptor Binding Domains as Scaffolds for SARS-CoV-2 Receptor Binding Motif Presentation

Structure-guided rational immunogen design can generate optimized immunogens that elicit a desired humoral response. Design strategies often center on targeting conserved sites on viral glycoproteins that will ultimately confer potent neutralization. For SARS-CoV-2 (SARS-2), the surface-exposed spike glycoprotein includes a broadly conserved portion, the receptor binding motif (RBM), that is required to engage the host cellular receptor, ACE2. Expanding humoral responses to this site may result in a more potent neutralizing antibody response against diverse sarbecoviruses. Here, we used a "resurfacing" approach and iterative design cycles to graft the SARS-2 RBM onto heterologous sarbecovirus scaffolds. The scaffolds were selected to vary the antigenic distance relative to SARS-2 to potentially focus responses to RBM. Multimerized versions of these immunogens elicited broad neutralization against sarbecoviruses in the context of preexisting SARS-2 immunity. These validated engineering approaches can help inform future immunogen design efforts for sarbecoviruses and are generally applicable to other viruses.

Heterologous sarbecovirus receptor binding domains as scaffolds for SARS-CoV-2 receptor binding motif presentation

Structure-guided rational immunogen design can generate optimized immunogens that elicit a desired humoral response. Design strategies often center upon targeting conserved sites on viral glycoproteins that will ultimately confer potent neutralization. For SARS-CoV-2 (SARS-2), the surface-exposed spike glycoprotein includes a broadly conserved portion, the receptor binding motif (RBM), that is required to engage the host cellular receptor, ACE2. Expanding humoral responses to this site may result in a more potently neutralizing antibody response against diverse sarbecoviruses. Here, we used a "resurfacing" approach and iterative design cycles to graft the SARS-2 RBM onto heterologous sarbecovirus scaffolds. The scaffolds were selected to vary the antigenic distance relative to SARS-2 to potentially focus responses to RBM. Multimerized versions of these immunogens elicited broad neutralization against sarbecoviruses in the context of preexisting SARS-2 immunity. These validated engineering approaches can help inform future immunogen design efforts for sarbecoviruses and are generally applicable to other viruses.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

A backbone-centred energy function of neural networks for protein design

A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it^1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions^3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.

Rational design of affinity ligands for bioseparation

Affinity adsorbents have been the cornerstone in protein purification. The selective nature of the molecular recognition interactions established between an affinity ligands and its target provide the basis for efficient capture and isolation of proteins. The plethora of affinity adsorbents available in the market reflects the importance of affinity chromatography in the bioseparation industry. Ligand discovery relies on the implementation of rational design techniques, which provides the foundation for the engineering of novel affinity ligands. The main goal for the design of affinity ligands is to discover or improve functionality, such as increased stability or selectivity. However, the methodologies must adapt to the current needs, namely to the number and diversity of biologicals being developed, and the availability of new tools for big data analysis and artificial intelligence. In this review, we offer an overview on the development of affinity ligands for bioseparation, including the evolution of rational design techniques, dating back to the years of early discovery up to the current and future trends in the field.

Designed for life: biocompatible de novo designed proteins and components

A principal goal of synthetic biology is the de novo design or redesign of biomolecular components. In addition to revealing fundamentally important information regarding natural biomolecular engineering and biochemistry, functional building blocks will ultimately be provided for applications including the manufacture of valuable products and therapeutics. To fully realize this ambitious goal, the designed components must be biocompatible, working in concert with natural biochemical processes and pathways, while not adversely affecting cellular function. For example, de novo protein design has provided us with a wide repertoire of structures and functions, including those that can be assembled and function in vivo. Here we discuss such biocompatible designs, as well as others that have the potential to become biocompatible, including non-protein molecules, and routes to achieving full biological integration.

TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones

To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.

Computational protein design with backbone plasticity

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.

Artificial Diiron Enzymes with a De Novo Designed Four-Helix Bundle Structure

A single polypeptide chain may provide an astronomical number of conformers. Nature selected only a trivial number of them through evolution, composing an alphabet of scaffolds, that can afford the complete set of chemical reactions needed to support life. These structural templates are so stable that they allow several mutations without disruption of the global folding, even having the ability to bind several exogenous cofactors. With this perspective, metal cofactors play a crucial role in the regulation and catalysis of several processes. Nature is able to modulate the chemistry of metals, adopting only a few ligands and slightly different geometries. Several scaffolds and metal-binding motifs are representing the focus of intense interest in the literature. This review discusses the widespread four-helix bundle fold, adopted as a scaffold for metal binding sites in the context of de novo protein design to obtain basic biochemical components for biosensing or catalysis. In particular, we describe the rational refinement of structure/function in diiron-oxo protein models from the due ferri (DF) family. The DF proteins were developed by us through an iterative process of design and rigorous characterization, which has allowed a shift from structural to functional models. The examples reported herein demonstrate the importance of the synergic application of de novo design methods as well as spectroscopic and structural characterization to optimize the catalytic performance of artificial enzymes.

Evolution and design of protein structure by folding nucleus symmetric expansion

Models of symmetric protein evolution typically invoke gene duplication and fusion events, in which repetition of a structural motif generates foldable, stable symmetric protein architecture. Success of such evolutionary processes suggests that the duplicated structural motif must be capable of nucleating protein folding. If correct, symmetric expansion of a folding nucleus sequence derived from an extant symmetric fold may be an elegant and computationally tractable solution to de novo protein design. We report the efficient de novo design of a β-trefoil protein by symmetric expansion of a β-trefoil folding nucleus, previously identified by ɸ-value analysis. The resulting protein, having exact sequence symmetry, exhibits superior folding properties compared to its naturally evolved progenitor-with the potential for redundant folding nuclei. In principle, folding nucleus symmetric expansion can be applied to any given symmetric protein fold (that is, nearly one-third of the known proteome) provided information of the folding nucleus is available.

Validating a Coarse-Grained Potential Energy Function through Protein Loop Modelling

Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using -carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific /-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/.

High-quality protein backbone reconstruction from alpha carbons using Gaussian mixture models

Coarse-grained protein structure models offer increased efficiency in structural modeling, but these must be coupled with fast and accurate methods to revert to a full-atom structure. Here, we present a novel algorithm to reconstruct mainchain models from C traces. This has been parameterized by fitting Gaussian mixture models (GMMs) to short backbone fragments centered on idealized peptide bonds. The method we have developed is statistically significantly more accurate than several competing methods, both in terms of RMSD values and dihedral angle differences. The method produced Ramachandran dihedral angle distributions that are closer to that observed in real proteins and better Phaser molecular replacement log-likelihood gains. Amino acid residue sidechain reconstruction accuracy using SCWRL4 was found to be statistically significantly correlated to backbone reconstruction accuracy. Finally, the PD2 method was found to produce significantly lower energy full-atom models using Rosetta which has implications for multiscale protein modeling using coarse-grained models. A webserver and C++ source code is freely available for noncommercial use from: http://www.sbg.bio.ic.ac.uk/phyre2/PD2_ca2main/.

Local conformational changes in the DNA interfaces of proteins

When a protein binds to DNA, a conformational change is often induced so that the protein will fit into the DNA structure. Therefore, quantitative analyses were conducted to understand the conformational changes in proteins. The results showed that conformational changes in DNA interfaces are more frequent than in non-interfaces, and DNA interfaces have more conformational variations in the DNA-free form. As expected, the former indicates that interaction with DNA has some influence on protein structure. The latter suggests that the intrinsic conformational flexibility of DNA interfaces is important for adjusting their conformation for DNA. The amino acid propensities of the conformationally changed regions in DNA interfaces indicate that hydrophilic residues are preferred over the amino acids that appear in the conformationally unchanged regions. This trend is true for disordered regions, suggesting again that intrinsic flexibility is of importance not only for DNA binding but also for interactions with other molecules. These results demonstrate that fragments destined to be DNA interfaces have an intrinsic flexibility and are composed of amino acids with the capability of binding to DNA. This information suggests that the prediction of DNA binding sites may be improved by the integration of amino acid preference for DNA and one for disordered regions.

Computer-aided antibody design

Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.

Analytic markovian rates for generalized protein structure evolution

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the sub-space of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, in this work we present a model for constrained evolutionary trajectories represented by a Markovian process throughout a set of protein-like structures artificially constructed to be topological intermediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. By using a coarse-grained representation for the protein structures, we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures.

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.

3D maps of RNA interhelical junctions

More than 50% of RNA secondary structure is estimated to be A-form helices, which are linked together by various junctions. Here we describe a protocol for computing three interhelical Euler angles describing the relative orientation of helices across RNA junctions. 5' and 3' helices, H1 and H2, respectively, are assigned based on the junction topology. A reference canonical helix is constructed using an appropriate molecular builder software consisting of two continuous idealized A-form helices (iH1 and iH2) with helix axis oriented along the molecular Z-direction running toward the positive direction from iH1 to iH2. The phosphate groups and the carbon and oxygen atoms of the sugars are used to superimpose helix H1 of a target interhelical junction onto the corresponding iH1 of the reference helix. A copy of iH2 is then superimposed onto the resulting H2 helix to generate iH2'. A rotation matrix R is computed, which rotates iH2' into iH2 and expresses the rotation parameters in terms of three Euler angles α(h), β(h) and γ(h). The angles are processed to resolve a twofold degeneracy and to select an overall rotation around the axis of the reference helix. The three interhelical Euler angles define clockwise rotations around the 5' (-γ(h)) and 3' (α(h)) helices and an interhelical bend angle (β(h)). The angles can be depicted graphically to provide a 'Ramachandran'-type view of RNA global structure that can be used to identify unusual conformations as well as to understand variations due to changes in sequence, junction topology and other parameters.

Direct correlation analysis improves fold recognition

The extraction of correlated mutations through the method of direct information (DI) provides predicted contact residue pairs that can be used to constrain the three dimensional structures of proteins. We apply this method to a large set of decoy protein folds consisting of many thousand well-constructed models, only tens of which have the correct fold. We find that DI is able to greatly improve the ranking of the true (native) fold but others still remain high scoring that would be difficult to discard due to small shifts in the core beta sheets.

Exploring the limits of fold discrimination by structural alignment: a large scale benchmark using decoys of known fold

Protein structure comparison by pairwise alignment is commonly used to identify highly similar substructures in pairs of proteins and provide a measure of structural similarity based on the size and geometric similarity of the match. These scores are routinely applied in analyses of protein fold space under the assumption that high statistical significance is equivalent to a meaningful relationship, however the truth of this assumption has previously been difficult to test since there is a lack of automated methods which do not rely on the same underlying principles. As a resolution to this we present a method based on the use of topological descriptions of global protein structure, providing an independent means to assess the ability of structural alignment to maintain meaningful structural correspondances on a large scale.

Using a large set of decoys of specified global fold we benchmark three widely used methods for structure comparison, SAP, TM-align and DALI, and test the degree to which this assumption is justified for these methods. Application of a topological edit distance measure to provide a scale of the degree of fold change shows that while there is a broad correlation between high structural alignment scores and low edit distances there remain many pairs of highly significant score which differ by core strand swaps and therefore are structurally different on a global level. Possible causes of this problem and its meaning for present assessments of protein fold space are discussed.

Exploring the factors determining the dynamics of different protein folds

Normal mode analyses of homologous proteins at the family and superfamily level show that slow dynamics are similar and are preserved through evolution. This study investigates how the slow dynamics of proteins is affected by variation in the protein architecture and fold. For this purpose, we have used computer-generated protein models based on idealized protein structures with varying folds. These are shown to be protein-like in their behavior, and they are used to investigate the influence of architecture and fold on the slow dynamics. We compared the dynamics of models having different folds but similar architecture and found the architecture to be the dominant factor for the slow dynamics.

Novel proteins: from fold to function

The field of de novo protein design, though only two decades old, has already reached the point where designing and selecting novel proteins that are functionally active has been achieved several times. Here we review recently reported de novo functional proteins that were developed using various approaches, including rational design, computational optimization, and selection from combinatorial libraries. The functions displayed by these proteins range from metal binding to enzymatic catalysis. Some were designed for specific applications in engineering and medicine, and others provide life-sustaining functions in vivo.

Computational design of protein-ligand interfaces: potential in therapeutic development

Computational design of protein-ligand interfaces finds optimal amino acid sequences within a small-molecule binding site of a protein for tight binding of a specific small molecule. It requires a search algorithm that can rapidly sample the vast sequence and conformational space, and a scoring function that can identify low energy designs. This review focuses on recent advances in computational design methods and their application to protein-small molecule binding sites. Strategies for increasing affinity, altering specificity, creating broad-spectrum binding, and building novel enzymes from scratch are described. Future prospects for applications in drug development are discussed, including limitations that will need to be overcome to achieve computational design of protein therapeutics with novel modes of action.

Probing designability via a generalized model of helical bundle geometry

Because the space of folded protein structures is highly degenerate, with recurring secondary and tertiary motifs, methods for representing protein structure in terms of collective physically relevant coordinates are of great interest. By collapsing structural diversity to a handful of parameters, such methods can be used to delineate the space of designable structures (i.e., conformations that can be stabilized with a large number of sequences)-a crucial task for de novo protein design. We first demonstrate this on natural α-helical coiled coils using the Crick parameterization. We show that over 95% of known coiled-coil structures are within  1-Å C(α) root mean square deviation of a Crick-ideal backbone. Derived parameters show that natural geometric space of coiled coils is highly restricted and can be represented by "allowed" conformations amidst a potential continuum of conformers. Allowed structures have (1) restricted axial offsets between helices, which differ starkly between parallel and anti-parallel structures; (2) preferred superhelical radii, which depend linearly on the oligomerization state; (3) pronounced radius-dependent a- and d-position amino acid propensities; and (4) discrete angles of rotation of helices about their axes, which are surprisingly independent of oligomerization state or orientation. In all, we estimate the space of designable coiled-coil structures to be reduced at least 160-fold relative to the space of geometrically feasible structures. To extend the benefits of structural parameterization to other systems, we developed a general mathematical framework for parameterizing arbitrary helical structures, which reduces to the Crick parameterization as a special case. The method is successfully validated on a set of non-coiled-coil helical bundles, frequent in channels and transporter proteins, which show significant helix bending but not supercoiling. Programs for coiled-coil parameter fitting and structure generation are provided via a web interface at http://www.gevorggrigoryan.com/cccp/, and code for generalized helical parameterization is available upon request.

On the evolutionary origins of "Fold Space Continuity": a study of topological convergence and divergence in mixed alpha-beta domains

Existing protein structure classifications group proteins by overall structural similarity at the highest level and by evolutionary relationships at the lowest level, deriving higher-level groups by pairwise structure comparison. For this to be successful requires that large changes in structure are relatively rare in evolution and that proteins with no detectable evolutionary relationship do not converge on similar global chain conformations since this creates conflicts between structural and evolutionary consistency. Analysis of global structural changes using core topological descriptions for 4261 domains from classes C and D of the SCOP database and new measures of topological distance and consistency of classification showed that the topological consistency of SCOP folds is highly variable with some folds having no consistent description and significant overlaps between groups including some members of separate folds with identical topological descriptions. Topological clustering shows that including sufficient indels to allow family members to be joined would also require joining several distinct folds. We conclude that evolutionary changes in the global topology of protein domains are the root cause of many difficulties for present approaches to structure classification using pairwise comparison. As a resolution we propose that a purely structural classification should be created using an approach similar to that adopted by the Gene Ontology in which proteins are assigned labels describing structure.

Re-evaluating the "rules" of protein topology

It is well known that the set of observed topological arrangements of secondary structures in globular proteins is highly limited. These limitations have been explained as the consequence of several rules of thumb including a strong preference for right-handed connections, against crossing loops and certain beta strand patterns. We present a critical evaluation of the power of these rules to distinguish known from possible topologies in a large set of two- and three-layer protein structures and determine that although these rules are still largely valid, an increasing number of exceptions can be found to many of them. The rules are then used to construct a generalised linear model for assessing the probability of occurrence of an arbitrary topology in the PDB. Application of the model to a large set of topologies generated during structure prediction showed that many had a similar probability of occurrence to known PDB folds.

Structural alphabets derived from attractors in conformational space

Background

The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.

Results

A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.

Conclusions

The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics.