New algorithms and an in silico benchmark for computational enzyme design

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.     

Automated scaffold selection for enzyme design

A major goal of computational protein design is the construction of novel functions on existing protein scaffolds. There the first question is which scaffold is suitable for a specific reaction. Given a set of catalytic residues and their spatial arrangement, one wants to identify a protein scaffold that can host this active site. Here, we present an algorithm called ScaffoldSelection that is able to rapidly search large sets of protein structures for potential attachment sites of an enzymatic motif. The method consists of two steps; it first identifies pairs of backbone positions in pocket‐like regions. Then, it combines these to complete attachment sites using a graph theoretical approach. Identified matches are assessed for their ability to accommodate the substrate or transition state. A representative set of structures from the Protein Data Bank (～3500) was searched for backbone geometries that support the catalytic residues for 12 chemical reactions. Recapitulation of native active site geometries is used as a benchmark for the performance of the program. The native motif is identified in all 12 test cases, ranking it in the top percentile in 5 out of 12. The algorithm is fast and efficient, although dependent on the complexity of the motif. Comparisons to other methods show that ScaffoldSelection performs equally well in terms of accuracy and far better in terms of speed. Thus, ScaffoldSelection will aid future computational protein design experiments by preselecting protein scaffolds that are suitable for a specific reaction type and the introduction of a predefined amino acid motif. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

A matching algorithm for catalytic residue site selection in computational enzyme design

A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction coordinate. The catalytic geometric parameters defined between functional groups of transition state (TS) and the catalytic residues are continuously optimized to identify the accurate position of the TS. Pseudo-spheres are introduced for surrounding residues, which make the algorithm take binding into account as early as during the matching process. Recapitulation of native catalytic residue sites was used as a benchmark to evaluate the novel algorithm. The calculation results for the test set show that the native catalytic residue sites were successfully identified and ranked within the top 10 designs for 7 of the 10 chemical reactions. This indicates that the matching algorithm has the potential to be used for designing industrial enzymes for desired reactions.     

Comparison of designed and randomly generated catalysts for simple chemical reactions

There has been recent success in designing enzymes for simple chemical reactions using a two-step protocol. In the first step, a geometric matching algorithm is used to identify naturally occurring protein scaffolds at which predefined idealized active sites can be realized. In the second step, the residues surrounding the transition state model are optimized to increase transition state binding affinity and to bolster the primary catalytic side chains. To improve the design methodology, we investigated how the set of solutions identified by the design calculations relate to the overall set of solutions for two different chemical reactions. Using a TIM barrel scaffold in which catalytically active Kemp eliminase and retroaldolase designs were obtained previously, we carried out activity screens of random libraries made to be compositionally similar to active designs. A small number of active catalysts were found in screens of 10(3) variants for each of the two reactions, which differ from the computational designs in that they reuse charged residues already present in the native scaffold. The results suggest that computational design considerably increases the frequency of catalyst generation for active sites involving newly introduced catalytic residues, highlighting the importance of interaction cooperativity in enzyme active sites.     

Rational design of TNFα binding proteins based on the de novo designed protein DS119

De novo protein design offers templates for engineering tailor‐made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home‐made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1‐4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence‐structure‐function studies.     

SABER: a computational method for identifying active sites for new reactions

A software suite, SABER (Selection of Active/Binding sites for Enzyme Redesign), has been developed for the analysis of atomic geometries in protein structures, using a geometric hashing algorithm (Barker and Thornton, Bioinformatics 2003;19:1644–1649). SABER is used to explore the Protein Data Bank (PDB) to locate proteins with a specific 3D arrangement of catalytic groups to identify active sites that might be redesigned to catalyze new reactions. As a proof‐of‐principle test, SABER was used to identify enzymes that have the same catalytic group arrangement present in o‐succinyl benzoate synthase (OSBS). Among the highest‐scoring scaffolds identified by the SABER search for enzymes with the same catalytic group arrangement as OSBS were L ‐Ala D/L ‐Glu epimerase (AEE) and muconate lactonizing enzyme II (MLE), both of which have been redesigned to become effective OSBS catalysts, demonstrated by experiments. Next, we used SABER to search for naturally existing active sites in the PDB with catalytic groups similar to those present in the designed Kemp elimination enzyme KE07. From over 2000 geometric matches to the KE07 active site, SABER identified 23 matches that corresponded to residues from known active sites. The best of these matches, with a 0.28 Å catalytic atom RMSD to KE07, was then redesigned to be compatible with the Kemp elimination using RosettaDesign. We also used SABER to search for potential Kemp eliminases using a theozyme predicted to provide a greater rate acceleration than the active site of KE07, and used Rosetta to create a design based on the proteins identified.     

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph‐theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.     

DARPins and other repeat protein scaffolds: advances in engineering and applications

Antibodies have long been regarded as the only class of binding proteins. With the emergence of protein engineering techniques, new binding proteins based on alternative scaffolds have been designed. Additionally, modern technologies for selection and evolution from libraries are independent of the antibody scaffold and could thus be readily used for obtaining specific binding proteins. One important group of alternative scaffolds is based on repeat proteins. Nature is widely using these proteins to modulate protein-protein interactions, and even in the adaptive immune system of jawless vertebrates; the step to their application as an alternative to antibodies seems therefore logical. In this review, progress on DARPins and other repeat protein scaffolds will be discussed. Advances in their design as well as novel applications will be highlighted.     

A computational method for design of connected catalytic networks in proteins

Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side‐chain functional groups which maximize transition‐state stabilization, and then searching for sites in protein scaffolds where the specified side‐chain–transition‐state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example, proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen‐bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw‐like two‐dimensional representation of the transition state with hydrogen‐bond donors, acceptors, and covalent interaction sites indicated, and all placements of side‐chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen‐bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully‐connected active sites in protein scaffolds. The method generates many fully‐connected active site solutions for a set of model reactions that are promising starting points for the design of fully‐preorganized enzyme catalysts.     

Residue centrality, functionally important residues, and active site shape: analysis of enzyme and non-enzyme families

The representation of protein structures as small-world networks facilitates the search for topological determinants, which may relate to functionally important residues. Here, we aimed to investigate the performance of residue centrality, viewed as a family fold characteristic, in identifying functionally important residues in protein families. Our study is based on 46 families, including 29 enzyme and 17 non-enzyme families. A total of 80% of these central positions corresponded to active site residues or residues in direct contact with these sites. For enzyme families, this percentage increased to 91%, while for non-enzyme families the percentage decreased substantially to 48%. A total of 70% of these central positions are located in catalytic sites in the enzyme families, 64% are in hetero-atom binding sites in those families binding hetero-atoms, and only 16% belong to protein-protein interfaces in families with protein-protein interaction data. These differences reflect the active site shape: enzyme active sites locate in surface clefts, hetero-atom binding residues are in deep cavities, while protein-protein interactions involve a more planar configuration. On the other hand, not all surface cavities or clefts are comprised of central residues. Thus, closeness centrality identifies functionally important residues in enzymes. While here we focus on binding sites, we expect to identify key residues for the integration and transmission of the information to the rest of the protein, reflecting the relationship between fold and function. Residue centrality is more conserved than the protein sequence, emphasizing the robustness of protein structures.     

Computational design of protein antigens that interact with the CDR H3 loop of HIV broadly neutralizing antibody 2F5

Rational design of proteins with novel binding specificities and increased affinity is one of the major goals of computational protein design. Epitope‐scaffolds are a new class of antigens engineered by transplanting viral epitopes of predefined structure to protein scaffolds, or by building protein scaffolds around such epitopes. Epitope‐scaffolds are of interest as vaccine components to attempt to elicit neutralizing antibodies targeting the specified epitope. In this study we developed a new computational protocol, MultiGraft Interface, that transplants epitopes but also designs additional scaffold features outside the epitope to enhance antibody‐binding specificity and potentially influence the specificity of elicited antibodies. We employed MultiGraft Interface to engineer novel epitope‐scaffolds that display the known epitope of human immunodeficiency virus 1 (HIV‐1) neutralizing antibody 2F5 and that also interact with the functionally important CDR H3 antibody loop. MultiGraft Interface generated an epitope‐scaffold that bound 2F5 with subnanomolar affinity (K_D = 400 pM) and that interacted with the antibody CDR H3 loop through computationally designed contacts. Substantial structural modifications were necessary to engineer this antigen, with the 2F5 epitope replacing a helix in the native scaffold and with 15% of the native scaffold sequence being modified in the design stage. This epitope‐scaffold represents a successful example of rational protein backbone engineering and protein–protein interface design and could prove useful in the field of HIV vaccine design. MultiGraft Interface can be generally applied to engineer novel binding partners with altered specificity and optimized affinity. Proteins 2014; 82:2770–2782. © 2014 Wiley Periodicals, Inc.     

Interolog interfaces in protein–protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

蛋白质骨架库的构建及其在功能蛋白质设计中的应用

利用活性位点嫁接设计新的功能蛋白质的方法需要功能模体和蛋白质骨架两种构件。出于活性位点嫁接的需要,我们构建了一个蛋白质骨架库,且提供搜索与功能模体相配骨架的工具及其评估方法。并用两个活性位点的嫁接进行了检验。     

A fast loop-closure algorithm to accelerate residue matching in computational enzyme design

Constructing an active site on an inert scaffold is still a challenge in chemical biology. Herein, we describe the incorporation of a Newton-direction-based fast loop-closure algorithm for catalytic residue matching into our enzyme design program ProdaMatch. This was developed to determine the sites and geometries of the catalytic residues as well as the position of the transition state with high accuracy in order to satisfy the geometric constraints on the interactions between catalytic residues and the transition state. Loop-closure results for 64,827 initial loops derived from 21 loops in the test set showed that 99.51 % of the initial loops closed to within 0.05 Å in fewer than 400 iteration steps, while the large majority of the initial loops closed within 100 iteration steps. The revised version of ProdaMatch containing the novel loop-closure algorithm identified all native matches for ten scaffolds in the native active-site recapitulation test. Its high speed and accuracy when matching catalytic residues with a scaffold make this version of ProdaMatch potentially useful for scaffold selection through the incorporation of more complex theoretical enzyme models which may yield higher initial activities in de novo enzyme design.     

Scaffold-mediated nucleation of protein signaling complexes: elementary principles

Proteins with multiple binding sites play important roles in cell signaling systems by nucleating protein complexes in which, for example, enzymes and substrates are co-localized. Proteins that specialize in this function are called by a variety names, including adapter, linker and scaffold. Scaffold-mediated nucleation of protein complexes can be either constitutive or induced. Induced nucleation is commonly mediated by a docking site on a scaffold that is activated by phosphorylation. Here, by considering minimalist mathematical models, which recapitulate scaffold effects seen in more mechanistically detailed models, we obtain analytical and numerical results that provide insights into scaffold function. These results elucidate how recruitment of a pair of ligands to a scaffold depends on the concentrations of the ligands, on the binding constants for ligand-scaffold interactions, on binding cooperativity, and on the milieu of the scaffold, as ligand recruitment is affected by competitive ligands and decoy receptors. For the case of a bivalent scaffold, we obtain an expression for the unique scaffold concentration that maximally recruits a pair of monovalent ligands. Through simulations, we demonstrate that a bivalent scaffold can nucleate distinct sets of ligands to equivalent extents when the scaffold is present at different concentrations. Thus, the function of a scaffold can potentially change qualitatively with a change in copy number. We also demonstrate how a scaffold can change the catalytic efficiency of an enzyme and the sensitivity of the rate of reaction to substrate concentration. The results presented here should be useful for understanding scaffold function and for engineering scaffolds to have desired properties.     

Physics‐based enzyme design: Predicting binding affinity and catalytic activity

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical–chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics‐based implicit solvent MM‐GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM‐GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α‐Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real‐world enzyme design applications. Proteins 2014; 82:3397–3409. © 2014 Wiley Periodicals, Inc.     

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

A recurring theme in protein engineering: the design, stability and folding of repeat proteins

Repeat proteins are ubiquitous and are involved in a myriad of essential processes. They are typically non-globular structures that act as diverse scaffolds for the mediation of protein-protein interactions. These excitingly different structures, which arise from tandem arrays of a repeated structural motif, have generated significant interest with respect to protein engineering and design. Recent advances have been made in the design and characterisation of repeat proteins. The highlights include re-engineering of binding specificity, quantitative models of repeat protein stability and kinetic studies of repeat protein folding.