de novo computational enzyme design

Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination

Enzymes are indispensable biocatalysts for numerous industrial applications, yet stability, selectivity, and restricted substrate recognition present limitations for their use. Despite the importance of enzyme engineering in overcoming these limitations, success is often challenged by the intricate architecture of enzymes derived from natural sources. Recent advances in computational methods have enabled the de novo design of simplified scaffolds with specific functional sites. Such scaffolds may be advantageous as platforms for enzyme engineering. Here, we present a strategy for the de novo design of a simplified scaffold of an endo-α-N-acetylgalactosaminidase active site, a glycoside hydrolase from the GH101 enzyme family. Using a combination of trRosetta hallucination, iterative cycles of deep-learning-based structure prediction, and ProteinMPNN sequence design, we designed proteins with 290 amino acids incorporating the active site while reducing the molecular weight by over 100 kDa compared to the initial endo-α-N-acetylgalactosaminidase. Of 11 tested designs, six were expressed as soluble monomers, displaying similar or increased thermostabilities compared to the natural enzyme. Despite lacking detectable enzymatic activity, the experimentally determined crystal structures of a representative design closely matched the design with a root-mean-square deviation of 1.0 Å, with most catalytically important side chains within 2.0 Å. The results highlight the potential of scaffold hallucination in designing proteins that may serve as a foundation for subsequent enzyme engineering.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study, we employ this paradigm to answer a basic question: in enzyme superfamilies, where the catalytic mechanism, active sites, and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as representatives of the conserved protein tyrosine phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of the catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55 ± 4.7, 84 ± 4.8, and 79 ± 5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2 ± 0.19, 6.0 ± 0.68, and 14 ± 2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study we employ this paradigm to answer a basic question: in enzyme superfamilies where the catalytic mechanism, active sites and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as the representatives of the conserved Protein Tyrosine Phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

Enzyme redesign and genetic code expansion

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Acyl Transfer Catalytic Activity in De Novo Designed Protein with N-Terminus of α-Helix As Oxyanion-Binding Site

The design of catalytic proteins with functional sites capable of specific chemistry is gaining momentum and a number of artificial enzymes have recently been reported, including hydrolases, oxidoreductases, retro-aldolases, and others. Our goal is to develop a peptide ligase for robust catalysis of amide bond formation that possesses no stringent restrictions to the amino acid composition at the ligation junction. We report here the successful completion of the first step in this long-term project by building a completely de novo protein with predefined acyl transfer catalytic activity. We applied a minimalist approach to rationally design an oxyanion hole within a small cavity that contains an adjacent thiol nucleophile. The N-terminus of the α-helix with unpaired hydrogen-bond donors was exploited as a structural motif to stabilize negatively charged tetrahedral intermediates in nucleophilic addition-elimination reactions at the acyl group. Cysteine acting as a principal catalytic residue was introduced at the second residue position of the α-helix N-terminus in a designed three-α-helix protein based on structural informatics prediction. We showed that this minimal set of functional elements is sufficient for the emergence of catalytic activity in a de novo protein. Using peptide-αthioesters as acyl-donors, we demonstrated their catalyzed amidation concomitant with hydrolysis and proved that the environment at the catalytic site critically influences the reaction outcome. These results represent a promising starting point for the development of efficient catalysts for protein labeling, conjugation, and peptide ligation.

Growth amplification in ultrahigh-throughput microdroplet screening increases sensitivity of clonal enzyme assays and minimizes phenotypic variation

Microfluidic ultrahigh-throughput screening of enzyme activities provides information on libraries with millions of variants in a day. Each individual library member is represented by a recombinant single cell, compartmentalised in an emulsion droplet, in which an activity assay is carried out. Key to the success of this approach is the precision and sensitivity of the assay. Assay quality is most profoundly challenged when initially weak, promiscuous activities are to be enhanced in early rounds of directed evolution or when entirely novel catalysts are to be identified from metagenomic sources. Implementation of measures to widen the dynamic range of clonal assays would increase the chances of finding and generating new biocatalysts. Here, we demonstrate that the assay sensitivity and DNA recovery can be improved by orders of magnitude by growth of initially singly compartmentalised cells in microdroplets. Homogeneous cell growth is achieved by continuous oxygenation and recombinant protein expression is regulated by diffusion of an inducer from the oil phase. Reaction conditions are adjusted by directed droplet coalescence to enable full control of buffer composition and kinetic incubation time, creating level playing field conditions for library selections. The clonal amplification multiplies the product readout because more enzyme is produced per compartment. At the same time, phenotypic variation is reduced by measuring monoclonal populations rather than single cells and recovery efficiency is increased. Consequently, this workflow increases the efficiency of lysate-based microfluidic enzyme assays and will make it easier for protein engineers to identify or evolve new enzymes for applications in synthetic and chemical biology.

Computational Design of PDZ-Peptide Binding

This chapter describes two computational methods for PDZ-peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for conformational sampling with a Poisson-Boltzmann (PB) Linear Interaction Energy for scoring. A new CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative binding free energies. A detailed protocol is described based on the Proteus CPD software. The medium-throughput approach can be performed with standard MD and PB software, such as NAMD and Charmm. For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around 0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of parameter fitting before it can be applied, and its transferability to other protein families is still untested.

Artificial Enzymes for Diels-Alder Reactions

The Diels-Alder (DA) reaction is a cycloaddition of a conjugated diene and an alkene (dienophile) leading to the formation of a cyclohexene derivative through a concerted mechanism. As DA reactions generally proceed with a high degree of regio- and stereoselectivity, they are widely used in synthetic organic chemistry. Considering eco-conscious public and governmental movements, efforts are now directed towards the development of synthetic processes that meet environmental concerns. Artificial enzymes, which can be developed to catalyze abiotic reactions, appear to be important synthetic tools in the synthetic biology field. This review describes the different strategies used to develop protein-based artificial enzymes for DA reactions, including for in cellulo approaches.

Interplay between Conformational Strain and Intramolecular Interaction in Protein Structures: Which of Them Is Evolutionarily Conserved?

By computing strain energies of peptide fragments within protein structures and their intramolecular interaction energies, we attempt to reveal general biophysical trends behind the secondary structure formation in the context of protein evolution. Our "protein basis set" consisted of 1143 representatives of different folds obtained from curated SCOPe database, and for each member of the set, the strain and intramolecular energy was calculated on the "rolling tripeptide" basis, employing the DFT-D3/COSMO-RS method for the former and the QM-calibrated force field method (MM) for the latter. The calculated data, strain and interactions, were correlated with the conservation of amino acid residues in secondary structure elements and also with the level of the residue burial within the protein three-dimensional structure. It allowed us to formulate several observations concerning fundamental differences between two main secondary structure motifs: α-helices and β-strands. We have shown that a strong interaction is one of the determining characteristics of the β-sheet formation, at least at the level of tripeptides (and likely penta- or heptapeptides, too), and that the β-strand is a prevailing secondary structure in the strongly-interacting regions of the protein folds conserved by evolution. On the other hand, low strain was neither proven to be an important physicochemical property conserved by evolution nor does it correlate with the propensity for the α-helix and β-strand. Finally, it has been demonstrated that the strong interaction has a certain level of connection with residue burial; however, we demonstrate that these two characteristics should be rather regarded as two complementary factors. These findings represent an important contribution to understanding protein folding from first principles, which is a complementary approach to ongoing efforts to solve the protein folding problem by knowledge-based approaches and machine-learning.

Attempts to develop an enzyme converting DHIV to KIV

Dihydroxy-acid dehydratase (DHAD) catalyzes the dehydration of R-2,3-dihydroxyisovalerate (DHIV) to 2-ketoisovalerate (KIV) using an Fe-S cluster as a cofactor, which is sensitive to oxidation and expensive to synthesize. In contrast, sugar acid dehydratases catalyze the same chemical reactions using a magnesium ion. Here, we attempted to substitute the high-cost DHAD with a cost-efficient engineered sugar acid dehydratase using computational protein design (CPD). First, we tried without success to modify the binding pocket of a sugar acid dehydratase to accommodate the smaller, more hydrophobic DHIV. Then, we used a chemically activated substrate analog to react with sugar acid dehydratases or other enolase superfamily enzymes. Mandelate racemase from Pseudomonas putida (PpManR) and the putative sugar acid dehydratase from Salmonella typhimurium (StPutD) showed beta-elimination activity towards chlorolactate (CLD). CPD combined with medium-throughput selection improved the PpManR kcat/KM for CLD by four-fold. However, these enzyme variants did not show dehydration activity towards DHIV. Lastly, assuming phosphorylation could also be a good activation mechanism, we found that mevalonate-3-kinase (M3K) from Picrophilus torridus (PtM3K) exhibited adenosine triphosphate (ATP) hydrolysis activity when mixed with DHIV, indicating phosphorylation activity towards DHIV. Engineering PpManR or StPutD to accept 3-phospho-DHIV as a substrate was performed, but no variants with the desired activity were obtained.

Quantification of biases in predictions of protein stability changes upon mutations

Motivation

Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations.

Results

We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (ΔΔG0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured ΔΔG0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used ΔΔG0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed.

Supplementary information

Supplementary data are available at Bioinformatics online.

Note

The article 10.1093/bioinformatics/bty340/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.

Improvements of thermophilic enzymes: From genetic modifications to applications

Thermozymes (from thermophiles or hyperthermophiles) offer obvious advantages due to their excellent thermostability, broad pH adaptation, and hydrolysis ability, resulting in diverse industrial applications including food, paper, and textile processing, biofuel production. However, natural thermozymes with low yield and poor adaptability severely hinder their large-scale applications. Extensive studies demonstrated that using genetic modifications such as directed evolution, semi-rational design, and rational design, expression regulations and chemical modifications effectively improved enzyme's yield, thermostability and catalytic efficiency. However, mechanism-based techniques for thermozymes improvements and applications need more attention. In this review, stabilizing mechanisms of thermozymes are summarized for thermozymes improvements, and these improved thermozymes eventually have large-scale industrial applications.

Hydrolytic zinc metallopeptides using a computational multi-state design approach

Combination of multi-state design and long-timescale conformational dynamics as a powerful strategy to obtain metalloenzymes.

Molecular modeling of conformational dynamics and its role in enzyme evolution

With increasing computational power, biomolecular simulations have become an invaluable tool for understanding enzyme mechanisms and the origins of enzyme catalysis. More recently, computational studies have started to focus on understanding how enzyme activity itself evolves, both in terms of enhancing the native or new activities on existing enzyme scaffolds, or completely de novo on previously non-catalytic scaffolds. In this context, both experiment and molecular modeling provided strong evidence for an important role of conformational dynamics in the evolution of enzyme functions. This contribution will present a brief overview of the current state of the art for computationally exploring enzyme conformational dynamics in enzyme evolution, and, using several showcase studies, illustrate the ways molecular modeling can be used to shed light on how enzyme function evolves, at the most fundamental molecular level.

De Novo-Designed α-Helical Barrels as Receptors for Small Molecules

We describe de novo-designed α-helical barrels (αHBs) that bind and discriminate between lipophilic biologically active molecules. αHBs have five or more α-helices arranged around central hydrophobic channels the diameters of which scale with oligomer state. We show that pentameric, hexameric, and heptameric αHBs bind the environmentally sensitive dye 1,6-diphenylhexatriene (DPH) in the micromolar range and fluoresce. Displacement of the dye is used to report the binding of nonfluorescent molecules: palmitic acid and retinol bind to all three αHBs with submicromolar inhibitor constants; farnesol binds the hexamer and heptamer; but β-carotene binds only the heptamer. A co-crystal structure of the hexamer with farnesol reveals oriented binding in the center of the hydrophobic channel. Charged side chains engineered into the lumen of the heptamer facilitate binding of polar ligands: a glutamate variant binds a cationic variant of DPH, and introducing lysine allows binding of the biosynthetically important farnesol diphosphate.

Hierarchical design of artificial proteins and complexes toward synthetic structural biology

In multiscale structural biology, synthetic approaches are important to demonstrate biophysical principles and mechanisms underlying the structure, function, and action of bio-nanomachines. A central goal of "synthetic structural biology" is the design and construction of artificial proteins and protein complexes as desired. In this paper, I review recent remarkable progress of an array of approaches for hierarchical design of artificial proteins and complexes that signpost the path forward toward synthetic structural biology as an emerging interdisciplinary field. Topics covered include combinatorial and protein-engineering approaches for directed evolution of artificial binding proteins and membrane proteins, binary code strategy for structural and functional de novo proteins, protein nanobuilding block strategy for constructing nano-architectures, protein-metal-organic frameworks for 3D protein complex crystals, and rational and computational approaches for design/creation of artificial proteins and complexes, novel protein folds, ideal/optimized protein structures, novel binding proteins for targeted therapeutics, and self-assembling nanomaterials. Protein designers and engineers look toward a bright future in synthetic structural biology for the next generation of biophysics and biotechnology.

Rapid Estimation of Catalytic Efficiency by Cumulative Atomic Multipole Moments: Application to Ketosteroid Isomerase Mutants

We propose a simple atomic multipole electrostatic model to rapidly evaluate the effects of mutation on enzyme activity and test its performance on wild-type and mutant ketosteroid isomerase. The predictions of our atomic multipole model are similar to those obtained with symmetry-adapted perturbation theory at a fraction of the computational cost. We further show that this approach is relatively insensitive to the precise amino acid side chain conformation in mutants and may thus be useful in computational enzyme (re)design.

The Generation and Exploitation of Protein Mutability Landscapes for Enzyme Engineering

The increasing number of enzyme applications in chemical synthesis calls for new engineering methods to develop the biocatalysts of the future. An interesting concept in enzyme engineering is the generation of large-scale mutational data in order to chart protein mutability landscapes. These landscapes allow the important discrimination between beneficial mutations and those that are neutral or detrimental, thus providing detailed insight into sequence-function relationships. As such, mutability landscapes are a powerful tool with which to identify functional hotspots at any place in the amino acid sequence of an enzyme. These hotspots can be used as targets for combinatorial mutagenesis to yield superior enzymes with improved catalytic properties, stability, or even new enzymatic activities. The generation of mutability landscapes for multiple properties of one enzyme provides the exciting opportunity to select mutations that are beneficial either for one or for several of these properties. This review presents an overview of the recent advances in the construction of mutability landscapes and discusses their importance for enzyme engineering.

Catalytic supramolecular self-assembled peptide nanostructures for ester hydrolysis

Essential amino acids in catalytic sites of native enzymes are important in nature inspired catalyst designs. Active sites of enzymes contain the coordinated assembly of multiple amino acids, and catalytic action is generated by the dynamic interactions among multiple residues. However, catalysis studies are limited by the complex and dynamic structure of the enzyme; and it is difficult to exclusively attribute a given function to a specific residue. Minimalistic approaches involving artificial catalytic sites are promising for the investigation of the enzyme function in the absence of non-essential protein components, and self-assembling peptide nanostructures are especially advantageous in this context. Here we demonstrate the design and characterization of an enzyme-mimetic catalytic nanosystem presenting essential residues (Ser, His, Asp). The function of each residue and its combinations on the nanostructures in hydrolysis reaction was studied. The catalytic self-assembled nanostructures were used for efficient ester hydrolysis such as a model substrate (pNPA) and a natural substrate (acetylcholine) highlighting the key role of self-assembly in catalytic domain formation to test the efficiency of the de novo designed catalyst as a catalytic triad model.

In vitro phosphorylation as tool for modification of silk and keratin fibrous materials

An overview is given of the recent work on in vitro enzymatic phosphorylation of silk fibroin and human hair keratin. Opposing to many chemical "conventional" approaches, enzymatic phosphorylation is in fact a mild reaction and the treatment falls within "green chemistry" approach. Silk and keratin are not phosphorylated in vivo, but in vitro. This enzyme-driven modification is a major technological breakthrough. Harsh chemical chemicals are avoided, and mild conditions make enzymatic phosphorylation a real "green chemistry" approach. The current communication presents a novel approach stating that enzyme phosphorylation may be used as a tool to modify the surface charge of biocompatible materials such as keratin and silk.

Computationally Designed Bispecific Antibodies using Negative State Repertoires

A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.

Proteus and the Design of Ligand Binding Sites

This chapter describes the organization and use of Proteus, a multitool computational suite for the optimization of protein and ligand conformations and sequences, and the calculation of pK α shifts and relative binding affinities. The software offers the use of several molecular mechanics force fields and solvent models, including two generalized Born variants, and a large range of scoring functions, which can combine protein stability, ligand affinity, and ligand specificity terms, for positive and negative design. We present in detail the steps for structure preparation, system setup, construction of the interaction energy matrix, protein sequence and structure optimizations, pK α calculations, and ligand titration calculations. We discuss illustrative examples, including the chemical/structural optimization of a complex between the MHC class II protein HLA-DQ8 and the vinculin epitope, and the chemical optimization of the compstatin analog Ac-Val4Trp/His9Ala, which regulates the function of protein C3 of the complement system.

Cytotoxic activity of chimeric protein PD-L4UWSCI(tr) does not appear be affected by specificity of inhibition mediated by anti-protease WSCI domain

In a previously study, a type 1 ribosome inactivating protein (PD-L4) and a wheat subtilisin/chymotrypsin inhibitor (WSCI) were engineered into a chimeric protein (PD-L4UWSCI) that presented in addition to the same properties of both domains an intriguing selective cytotoxic action on murine tumor cells. This finding supported the idea that the protection of C-terminal region of PD-L4 could amplify its cytotoxic action by virtue of a greater resistance to proteases. Several authors indeed revealed that the cytotoxicity of RIPs depends not only on the intracellular routing, but also on the intrinsic resistance to proteolysis. In this regard in the present work we have produced a variant of chimeric protein, named PD-L4UWSCI(tr), changing the inhibitory specificity of WSCI domain. The purpose of this approach was to check if the cytotoxicity of the chimeric protein was altered depending on the properties of protease inhibitor domain or by a different fold of whole protein. Data collected supposedly indicate that WSCI domain contributes to cytotoxicity of chimeric protein exclusively from a structural point of view.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Enzyme architecture: optimization of transition state stabilization from a cation-phosphodianion pair

The side chain cation of R269 lies at the surface of l-glycerol 3-phosphate dehydrogenase (GPDH) and forms an ion pair to the phosphodianion of substrate dihydroxyacetone phosphate (DHAP), which is buried at the nonpolar protein interior. The R269A mutation of GPDH results in a 110-fold increase in K_m (2.8 kcal/mol effect) and a 41 000-fold decrease in k_cat (6.3 kcal/mol effect), which corresponds to a 9.1 kcal/mol destabilization of the transition state for GPDH-catalyzed reduction of DHAP by NADH. There is a 6.7 kcal/mol stabilization of the transition state for the R269A mutant GPDH-catalyzed reaction by 1.0 M guanidinium ion, and the transition state for the reaction of the substrate pieces is stabilized by an additional 2.4 kcal/mol by their covalent attachment at wildtype GPDH. These results provide strong support for the proposal that GPDH invests the 11 kcal/mol intrinsic phosphodianion binding energy of DHAP in trapping the substrate at a nonpolar active site, where strong electrostatic interactions are favored, and obtains a 9 kcal/mol return from stabilizing interactions between the side chain cation and transition state trianion. We propose a wide propagation for the catalytic motif examined in this work, which enables strong transition state stabilization from enzyme–phosphodianion pairs.

Synthetic biology advances for pharmaceutical production

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Synthetic biology is quickly moving from proof of concept to industrial application.

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Pharmaceuticals are a promising target for advanced genetic engineering.

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Genome sequence data indicate vast underexploited biosynthetic capacity.

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Synthetic biology can create libraries of novel chemicals enriched for bioactivity.

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Synthetic biology expands the range of available chassis organisms for industry.

Synthetic biology enables a new generation of microbial engineering for the biotechnological production of pharmaceuticals and other high-value chemicals. This review presents an overview of recent advances in the field, describing new computational and experimental tools for the discovery, optimization and production of bioactive molecules, and outlining progress towards the application of these tools to pharmaceutical production systems.

Metabolic engineering strategies for microbial synthesis of oleochemicals

Microbial synthesis of oleochemicals has advanced significantly in the last decade. Microbes have been engineered to convert renewable substrates to a wide range of molecules that are ordinarily made from plant oils. This approach is attractive because it can reduce a motivation for converting tropical rainforest into farmland while simultaneously enabling access to molecules that are currently expensive to produce from oil crops. In the last decade, enzymes responsible for producing oleochemicals in nature have been identified, strategies to circumvent native regulation have been developed, and high yielding strains have been designed, built, and successfully demonstrated. This review will describe the metabolic pathways that lead to the diverse molecular features found in natural oleochemicals, highlight successful metabolic engineering strategies, and comment on areas where future work could further advance the field.

Proteins without unique 3D structures: biotechnological applications of intrinsically unstable/disordered proteins

Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are functional proteins or regions that do not have unique 3D structures under functional conditions. Therefore, from the viewpoint of their lack of stable 3D structure, IDPs/IDPRs are inherently unstable. As much as structure and function of normal ordered globular proteins are determined by their amino acid sequences, the lack of unique 3D structure in IDPs/IDPRs and their disorder-based functionality are also encoded in the amino acid sequences. Because of their specific sequence features and distinctive conformational behavior, these intrinsically unstable proteins or regions have several applications in biotechnology. This review introduces some of the most characteristic features of IDPs/IDPRs (such as peculiarities of amino acid sequences of these proteins and regions, their major structural features, and peculiar responses to changes in their environment) and describes how these features can be used in the biotechnology, for example for the proteome-wide analysis of the abundance of extended IDPs, for recombinant protein isolation and purification, as polypeptide nanoparticles for drug delivery, as solubilization tools, and as thermally sensitive carriers of active peptides and proteins.

Synthetic biology and metabolic engineering for marine carotenoids: new opportunities and future prospects

Carotenoids are a class of diverse pigments with important biological roles such as light capture and antioxidative activities. Many novel carotenoids have been isolated from marine organisms to date and have shown various utilizations as nutraceuticals and pharmaceuticals. In this review, we summarize the pathways and enzymes of carotenoid synthesis and discuss various modifications of marine carotenoids. The advances in metabolic engineering and synthetic biology for carotenoid production are also reviewed, in hopes that this review will promote the exploration of marine carotenoid for their utilizations.