Computational Redesign Research Articles

Insights into cellulase-lignin non-specific binding revealed by computational redesign of the surface of green fluorescent protein.

Biological-mediated conversion of pretreated lignocellulosic biomass to biofuels and biochemicals is a promising avenue toward energy sustainability. However, a critical impediment to the commercialization of cellulosic biofuel production is the high cost of cellulase enzymes needed to deconstruct biomass into fermentable sugars. One major factor driving cost is cellulase adsorption and inactivation in the presence of lignin, yet we currently have a poor understanding of the protein structure-function relationships driving this adsorption. In this work, we have systematically investigated the role of protein surface potential on lignin adsorption using a model monomeric fluorescent protein. We have designed and experimentally characterized 16 model protein variants spanning the physiological range of net charge (-24 to +16 total charges) and total charge density (0.28-0.40 charges per sequence length) typical for natural proteins. Protein designs were expressed, purified, and subjected to in silico and in vitro biophysical measurements to evaluate the relationship between protein surface potential and lignin adsorption properties. The designs were comparable to model fluorescent protein in terms of thermostability and heterologous expression yield, although the majority of the designs unexpectedly formed homodimers. Protein adsorption to lignin was studied at two different temperatures using Quartz Crystal Microbalance with Dissipation Monitoring and a subtractive mass balance assay. We found a weak correlation between protein net charge and protein-binding capacity to lignin. No other single characteristic, including apparent melting temperature and 2nd virial coefficient, showed correlation with lignin binding. Analysis of an unrelated cellulase dataset with mutations localized to a family I carbohydrate-binding module showed a similar correlation between net charge and lignin binding capacity. Overall, our study provides strategies to identify highly active, low lignin-binding cellulases by either rational design or by computational screening genomic databases. Biotechnol. Bioeng. 2017;114: 740-750. © 2016 Wiley Periodicals, Inc.

Computational Redesign of Thioredoxin Is Hypersensitive toward Minor Conformational Changes in the Backbone Template

Despite the development of powerful computational tools, the full-sequence design of proteins still remains a challenging task. To investigate the limits and capabilities of computational tools, we conducted a study of the ability of the program Rosetta to predict sequences that recreate the authentic fold of thioredoxin. Focusing on the influence of conformational details in the template structures, we based our study on 8 experimentally determined template structures and generated 120 designs from each. For experimental evaluation, we chose six sequences from each of the eight templates by objective criteria. The 48 selected sequences were evaluated based on their progressive ability to (1) produce soluble protein in Escherichia coli and (2) yield stable monomeric protein, and (3) on the ability of the stable, soluble proteins to adopt the target fold. Of the 48 designs, we were able to synthesize 32, 20 of which resulted in soluble protein. Of these, only two were sufficiently stable to be purified. An X-ray crystal structure was solved for one of the designs, revealing a close resemblance to the target structure. We found a significant difference among the eight template structures to realize the above three criteria despite their high structural similarity. Thus, in order to improve the success rate of computational full-sequence design methods, we recommend that multiple template structures are used. Furthermore, this study shows that special care should be taken when optimizing the geometry of a structure prior to computational design when using a method that is based on rigid conformations.

Computational redesign of the lipid-facing surface of the outer membrane protein OmpA

Advances in computational design methods have made possible extensive engineering of soluble proteins, but designed β-barrel membrane proteins await improvements in our understanding of the sequence determinants of folding and stability. A subset of the amino acid residues of membrane proteins interact with the cell membrane, and the design rules that govern this lipid-facing surface are poorly understood. We applied a residue-level depth potential for β-barrel membrane proteins to the complete redesign of the lipid-facing surface of Escherichia coli OmpA. Initial designs failed to fold correctly, but reversion of a small number of mutations indicated by backcross experiments yielded designs with substitutions to up to 60% of the surface that did support folding and membrane insertion.

Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries.

As the spread of antibiotic resistant bacteria steadily increases, there is an urgent need for new antibacterial agents. Because fatty acid synthesis is only used for membrane biogenesis in bacteria, the enzymes in this pathway are attractive targets for antibacterial agent development. Acetyl-CoA carboxylase catalyzes the committed and regulated step in fatty acid synthesis. In bacteria, the enzyme is composed of three distinct protein components: biotin carboxylase, biotin carboxyl carrier protein, and carboxyltransferase. Fragment-based screening revealed that amino-oxazole inhibits biotin carboxylase activity and also exhibits antibacterial activity against Gram-negative organisms. In this report, we redesigned previously identified lead inhibitors to expand the spectrum of bacteria sensitive to the amino-oxazole derivatives by including Gram-positive species. Using 9,411 small organic building blocks, we constructed a diverse combinatorial library of 1.2 × 108 amino-oxazole derivatives. A subset of 9 × 106 of these compounds were subjected to structure-based virtual screening against seven biotin carboxylase isoforms using similarity-based docking by eSimDock. Potentially broad-spectrum antibiotic candidates were selected based on the consensus ranking by several scoring functions including non-linear statistical models implemented in eSimDock and traditional molecular mechanics force fields. The analysis of binding poses of the top-ranked compounds docked to biotin carboxylase isoforms suggests that: (1) binding of the amino-oxazole anchor is stabilized by a network of hydrogen bonds to residues 201, 202 and 204; (2) halogenated aromatic moieties attached to the amino-oxazole scaffold enhance interactions with a hydrophobic pocket formed by residues 157, 169, 171 and 203; and (3) larger substituents reach deeper into the binding pocket to form additional hydrogen bonds with the side chains of residues 209 and 233. These structural insights into drug-biotin carboxylase interactions will be tested experimentally in in vitro and in vivo systems to increase the potency of amino-oxazole inhibitors towards both Gram-negative as well as Gram-positive species.

Computational redesign of metalloenzymes for catalyzing new reactions.

The ability to design novel activities in existing metalloenzyme active sites is a stringent test of our understanding of enzyme mechanisms, sheds light on enzyme evolution, and would have many practical applications. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to repurpose active sites containing metal ions for reactions of choice. The required inputs for the method are a model of the transition state(s) for the reaction and a set of crystallographic structures of proteins containing metal ions. The coordination geometry associated with the metal ion (Zn(2+), for example) is automatically detected and the transition state model is aligned to the open metal coordination site(s) in the protein. Additional interactions to the transition state model are made using RosettaMatch and the surrounding amino acid side chain identities are optimized for transition state stabilization using RosettaDesign. Validation of the design is performed using docking and molecular dynamics simulations, and candidate designs are generated for experimental validation. Computational metalloenzyme repurposing is complementary to directed evolution approaches for enzyme engineering and allows large jumps in sequence space to make concerted sequence and structural changes for introducing novel enzymatic activities and specificities.

Engineering a More Thermostable Blue Light Photo Receptor Bacillus subtilis YtvA LOV Domain by a Computer Aided Rational Design Method

The ability to design thermostable proteins offers enormous potential for the development of novel protein bioreagents. In this work, a combined computational and experimental method was developed to increase the T m of the flavin mononucleotide based fluorescent protein Bacillus Subtilis YtvA LOV domain by 31 Celsius, thus extending its applicability in thermophilic systems. Briefly, the method includes five steps, the single mutant computer screening to identify thermostable mutant candidates, the experimental evaluation to confirm the positive selections, the computational redesign around the thermostable mutation regions, the experimental reevaluation and finally the multiple mutations combination. The adopted method is simple and effective, can be applied to other important proteins where other methods have difficulties, and therefore provides a new tool to improve protein thermostability.

Computational design gains momentum in enzyme catalysis engineering

Computational protein design is becoming a powerful tool for tailoring enzymes for specific biotechnological applications. When applied to existing enzymes, computational re-design makes it possible to obtain orders of magnitude improvement in catalytic activity towards a new target substrate. Computational methods also allow the design of completely new active sites that catalyze reactions that are not known to occur in biological systems. If initial designs display modest catalytic activity, which is often the case, this may be improved by iterative cycles of computational design or by follow-up engineering through directed evolution. Compared to established protein engineering methods such as directed evolution and structure-based mutagenesis, computational design allows for much larger jumps in sequence space; for example, by introducing more than a dozen mutations in a single step or by introducing loops that provide new functional interactions. Recent advances in the computational design toolbox, which include new backbone re-design methods and the use of molecular dynamics simulations to better predict the catalytic activity of designed variants, will further enhance the use of computational tools in enzyme engineering.

Complete shift of ferritin oligomerization toward nanocage assembly via engineered protein–protein interactions

Computational redesign of a dimorphic protein nano-cage at the C3-symmetrical interfaces forces it to assemble into the monomorphic cage. These monodisperse assemblies are at least 20 °C more stable than the parent. This approach adds to the toolkit of bottom-up molecular design with applications in protein engineering and hybrid nano-materials.

Crosstalk between endogenous and synthetic components – synthetic signaling meets endogenous components

Synthetic biology uses biological components to engineer new functionality in living organisms. We have used the tools of synthetic biology to engineer detector plants that can sense man-made chemicals, such as the explosive trinitrotoluene, and induce a response detectable by eye or instrumentation. A goal of this type of work is to make the designed system orthogonal, that is, able to function independently of systems in the host. In this review, the design and function of two partially synthetic signaling pathways for use in plants is discussed. We describe observed interactions (crosstalk) with endogenous signaling components. This crosstalk can be beneficial, allowing the creation of hybrid synthetic/endogenous signaling pathways, or detrimental, resulting in system noise and/or false positives. Current approaches in the field of synthetic biology applicable to the design of orthogonal signaling systems, including the design of synthetic components, partially synthetic systems that utilize crosstalk to signal through endogenous components, computational redesign of proteins, and the use of heterologous components, are discussed.

Designing orthogonal signaling pathways: How to fit in with the surroundings

Protein–protein interactions serve as the primary means for transducing signals within cells and between cells and thus are responsible for controlling cell proliferation, morphology, differentiation, and survival. Accordingly, the ability to rationally manipulate these interactions will represent a key enabling technology for understanding the properties of natural cellular networks, for building new biological responses into cells, and even as a starting point for therapeutic intervention in certain human diseases. Successful integration into cells will require that engineered protein interactions (and later whole engineered pathways) can exist in parallel with endogenous cellular interactions (and pathways) without interference or cross-talk between the two: they must behave in a manner completely orthogonal to one another. A study in PNAS by Kapp et al. (1) reports the computational redesign of a GTPase/guanine nucleotide exchange factor (GEF) protein pair, then goes on to demonstrate the orthogonality of this pair in living cells.

Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

The ability to redesign enzymes to catalyze noncognate chemical transformations would have wide-ranging applications. We developed a computational method for repurposing the reactivity of metalloenzyme active site functional groups to catalyze new reactions. Using this method, we engineered a zinc-containing mouse adenosine deaminase to catalyze the hydrolysis of a model organophosphate with a catalytic efficiency (k(cat)/K(m)) of ~10(4) M(-1) s(-1) after directed evolution. In the high-resolution crystal structure of the enzyme, all but one of the designed residues adopt the designed conformation. The designed enzyme efficiently catalyzes the hydrolysis of the R(P) isomer of a coumarinyl analog of the nerve agent cyclosarin, and it shows marked substrate selectivity for coumarinyl leaving groups. Computational redesign of native enzyme active sites complements directed evolution methods and offers a general approach for exploring their untapped catalytic potential for new reactivities.

Automated selection of stabilizing mutations in designed and natural proteins

The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins.

Computational design of the lasso peptide antibiotic microcin J25.

Microcin J25 (MccJ25) is a 21 amino acid (aa) ribosomally synthesized antimicrobial peptide with an unusual structure in which the eight N-terminal residues form a covalently cyclized macrolactam ring through which the remaining 13 aa tail is fed. An open question is the extent of sequence space that can occupy such an extraordinary, highly constrained peptide fold. To begin answering this question, here we have undertaken a computational redesign of the MccJ25 peptide using a two-stage sequence selection procedure based on both energy minimization and fold specificity. Eight of the most highly ranked sequences from the design algorithm, each of which contained two or three amino acid substitutions, were expressed in Escherichia coli and tested for production and antimicrobial activity. Six of the eight variants were successfully produced by E.coli at production levels comparable with that of the wild-type peptide. Of these six variants, three retain detectable antimicrobial activity, although this activity is reduced relative to wild-type MccJ25. The results here build upon previous findings that even rigid, constrained structures like the lasso architecture are amenable to redesign. Furthermore, this work provides evidence that a large amount of amino acid variation is tolerated by the lasso peptide fold.

Zinc-finger hydrolase: Computational selection of a linker and a sequence towards metal activation with a synthetic αββ protein

The zinc-finger protein is targeted for computational redesign as a hydrolase enzyme. Successful in having zinc activated for hydrolase function, the study validates the stepwise approach to having the protein tuned in main-chain structure stereochemically and over side chains chemically. A leucine homopolypeptide, harboring histidines to tri coordinate zinc and d-amino-acid-nucleated α-helix and β-hairpin building blocks of an αββ protein, is taken up for modeling, first with cyana, in a mixed-chirality linker between the building blocks, and then with IDeAS, in a sequence over side chains. The designed mixed-chirality polypeptide structure is proven to order as an intended αββ fold and capture zinc to activate its role as a hydrolase catalyst. The design approach to have protein folds defined stereochemically and receptor and catalysis functions defined chemically is presented, and illustrates l- and d-α-amino-acid structures as the alphabet integrating chemical- and stereochemical-structure variables as its letters.

Computational redesign of the test section for the Boeing/AFOSR Mach 6 Quiet Tunnel

A proposed modification of the test section in the Boeing/AFOSR Mach 6 Quiet Tunnel is evaluated using CFD. The new design incorporates a section of increased diameter with the intention of enabling the tunnel to start in the presence of larger blunt models. Cone models with fixed base diameter (and hence fixed blockage ratio) are selected for this study. Cone half-angles from 15° to 75° are examined to ascertain the effect of the strength of the test model shock wave on the tunnel startup. The unsteady, laminar, compressible Navier—Stokes equations are solved. The resulting flowfields are examined to see what affect the shocks and shear layers would have on the quiet test section flow. This study indicates that cone angles ≤20° allow the tunnel to start.

Rational stabilization of enzymes by computational redesign of surface charge–charge interactions

Here, we report the application of a computational approach that allows the rational design of enzymes with enhanced thermostability while retaining full enzymatic activity. The approach is based on the optimization of the energy of charge-charge interactions on the protein surface. We experimentally tested the validity of the approach on 2 human enzymes, acylphosphatase (AcPh) and Cdc42 GTPase, that differ in size (98 vs. 198-aa residues, respectively) and tertiary structure. We show that the designed proteins are significantly more stable than the corresponding WT proteins. The increase in stability is not accompanied by significant changes in structure, oligomerization state, or, most importantly, activity of the designed AcPh or Cdc42. This success of the design methodology suggests that it can be universally applied to other enzymes, on its own or in combination with the other strategies based on redesign of the interactions in the protein core.

Computational Design of Calmodulin Mutants with up to 900-Fold Increase in Binding Specificity

Calmodulin (CaM) is a ubiquitous second messenger protein that regulates a variety of structurally and functionally diverse targets in response to changes in Ca2+ concentration. CaM-dependent protein kinase II (CaMKII) and calcineurin (CaN) are the prominent CaM targets that play an opposing role in many cellular functions including synaptic regulation. Since CaMKII and CaN compete for the available Ca2+/CaM, the differential affinity of these enzymes for CaM is crucial for achieving a balance in Ca2+ signaling. We used the computational protein design approach to modify CaM binding specificity for these two targets. Starting from the X-ray structure of CaM in complex with the CaM-binding domain of CaMKII, we optimized CaM interactions with CaMKII by introducing mutations into the CaM sequence. CaM optimization was performed with a protein design program, ORBIT, using a modified energy function that emphasized intermolecular interactions in the sequence selection procedure. Several CaM variants were experimentally constructed and tested for binding to the CaMKII and CaN peptides using the surface plasmon resonance technique. Most of our CaM mutants demonstrated small increase in affinity for the CaMKII peptide and substantial decrease in affinity for the CaN peptide compared to that of wild-type CaM. Our best CaM design exhibited an about 900-fold increase in binding specificity towards the CaMKII peptide, becoming the highest specificity switch achieved in any protein–protein interface through the computational protein design approach. Our results show that computational redesign of protein–protein interfaces becomes a reliable method for altering protein binding affinity and specificity.

Computational Redesign of a Protein–Protein Interface for High Affinity and Binding Specificity Using Modular Architecture and Naturally Occurring Template Fragments

A new method is presented for the redesign of protein–protein interfaces, resulting in specificity of the designed pair while maintaining high affinity. The design is based on modular interface architecture and was carried out on the interaction between TEM1 β-lactamase and its inhibitor protein, β-lactamase inhibitor protein. The interface between these two proteins is composed of several mostly independent modules. We previously showed that it is possible to delete a complete module without affecting the overall structure of the interface. Here, we replace a complete module with structure fragments taken from nonrelated proteins. Nature-optimized fragments were chosen from 107 starting templates found in the Protein Data Bank. A procedure was then developed to identify sets of interacting template residues with a backbone arrangement mimicking the original module. This generated a final list of 361 putative replacement modules that were ranked using a novel scoring function based on grouped atom–atom contact surface areas. The top-ranked designed complex exhibited an affinity of at least the wild-type level and a mode of binding that was remarkably specific despite the absence of negative design in the procedure. In retrospect, the combined application of three factors led to the success of the design approach: utilizing the modular construction of the interface, capitalizing on native rather than artificial templates, and ranking with an accurate atom–atom contact surface scoring function.

Computational redesign of endonuclease DNA binding and cleavage specificity

The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein-DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI using a physically realistic atomic-level forcefield. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site approximately 10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease-recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.

High-Resolution Structural Validation of the Computational Redesign of Human U1A Protein

Achieving atomic-level resolution in the computational design of a protein structure remains a challenging problem despite recent progress. Rigorous experimental tests are needed to improve protein design algorithms, yet studies of the structure and dynamics of computationally designed proteins are very few. The NMR structure and backbone dynamics of a redesigned protein of 96 amino acids are compared here with the design target, human U1A protein. We demonstrate that the redesigned protein reproduces the target structure to within the uncertainty of the NMR coordinates, even as 65 out of 96 amino acids were simultaneously changed by purely computational methods. The dynamics of the backbone of the redesigned protein also mirror those of human U1A, suggesting that the protein design algorithm captures the shape of the potential energy landscape in addition to the local energy minimum.