Computational design of ligand-binding proteins with high affinity and selectivity

Rational Design and Applications of Semisynthetic Modular Biosensors: SNIFITs and LUCIDs

Biosensors are used in many fields to measure the concentration of analytes, both in a cellular context and in human samples for medical care. Here, we outline the design of two types of modular biosensors: SNAP-tag-based indicators with a Fluorescent Intramolecular Tether (SNIFITs) and LUCiferase-based Indicators of Drugs (LUCIDs). These semisynthetic biosensors quantitatively measure analyte concentrations in vitro and on cell surfaces by an intramolecular competitive mechanism. We provide an overview of how to design and apply SNIFITs and LUCIDs.

Applications of Normal Mode Analysis Methods in Computational Protein Design

Recent advances in coarse-grained normal mode analysis methods make possible the large-scale prediction of the effect of mutations on protein stability and dynamics as well as the generation of biologically relevant conformational ensembles. Given the interplay between flexibility and enzymatic activity, the combined analysis of stability and dynamics using the Elastic Network Contact Model (ENCoM) method has ample applications in protein engineering in industrial and medical applications such as in computational antibody design. Here, we present a detailed tutorial on how to perform such calculations using ENCoM.

Computational Design of Ligand Binding Proteins

The ability to design novel small-molecule binding sites in proteins is a stringent test of our understanding of the principles of molecular recognition, and would have many practical applications, in synthetic biology and medicine. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to designing proteins with sites featuring predetermined interactions to ligands of choice. The required inputs for the method are a model of the small molecule and the desired interactions (e.g., hydrogen bonding, electrostatics, steric packing), and a set of crystallographic structures of proteins containing existing or predicted binding pockets. Constellations of backbones surrounding the putative pocket are searched for compatibility with the desired binding site conception using RosettaMatch and surrounding amino acid side chain identities are optimized using RosettaDesign. Validation of the design is performed using metrics that evaluate the interface energy of the predicted binding pose, the preformation of key binding site features in the apo-state, and the local compatibility of the designed sequence changes with the wild type backbone structure, and top-ranking candidate designs are generated for experimental validation. This approach can allow for the creation of novel binding sites and for the rational tuning of specificity for congeneric ligands by altering the programmed interactions by design, thus offering a general computational protocol for construction and modulation of protein-small molecule interfaces.

Insights into the molecular interaction between two polyoxygenated cinnamoylcoumarin derivatives and human serum albumin

Drug–protein interactions based on the thermodynamics approach, curve resolution analysis and computational methods at molecular levels.

Synthetic Protein Switches: Theoretical and Experimental Considerations

Synthetic protein switches with tailored response functions are finding increasing applications as tools in basic research and biotechnology. With a number of successful design strategies emerging, the construction of synthetic protein switches still frequently necessitates an integrated approach that combines detailed biochemical and biophysical characterization in combination with high-throughput screening to construct tailored synthetic protein switches. This is increasingly complemented by computational strategies that aim to reduce the need for costly empirical optimization and thus facilitate the protein design process. Successful computational design approaches range from analyzing phylogenetic data to infer useful structural, biophysical, and biochemical information to modeling the structure and function of proteins ab initio. The following chapter provides an overview over the theoretical considerations and experimental approaches that have been successful applied in the construction of synthetic protein switches.

Computational Protein Design Through Grafting and Stabilization

Computational grafting of target residues onto existing protein scaffolds is a powerful method for the design of proteins with novel function. In the grafting method side chain mutations are introduced into a preexisting protein scaffold to recreate a target functional motif. The success of this approach relies on two primary criteria: (1) the availability of compatible structural scaffolds, and (2) the introduction of mutations that do not affect the protein structure or stability. To identify compatible structural motifs we use the Erebus webserver, to search the protein data bank (PDB) for user-defined structural scaffolds. To identify potential design mutations we use the Eris webserver, which accurately predicts changes in protein stability resulting from mutations. Mutations that increase the protein stability are more likely to maintain the protein structure and therefore produce the desired function. Together these tools provide effective methods for identifying existing templates and guiding further design experiments. The software tools for scaffold searching and design are available at http://dokhlab.org .

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Computational design of ligand-binding proteins

Custom-designed ligand-binding proteins with novel functions hold the potential for numerous applications. In recent years, the developments of computational methods together with high-throughput experimental screening techniques have led to the generation of novel, high-affinity ligand-binding proteins for given ligands. In addition, naturally occurring ligand-binding proteins have been computationally designed to recognize new ligands while keeping their original biological functions at the same time. Furthermore, metalloproteins have been successfully designed for novel functions and applications. Though much has been learned in these successful design cases, advances in our understanding of protein dynamics and functions related to ligand binding and development of novel computational strategies are necessary to further increase the success rate of computational protein-ligand binding design.

Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach

Protein-ligand interaction plays a critical role in regulating the biochemical functions of proteins. Discovering protein targets for ligands is vital to new drug development. Here, we present a strategy that combines experimental and computational approaches to identify ligand-binding proteins in a proteomic scale. For the experimental part, we coupled pulse proteolysis with filter-assisted sample preparation (FASP) and quantitative mass spectrometry. Under denaturing conditions, ligand binding affected protein stability, which resulted in altered protein abundance after pulse proteolysis. For the computational part, we used the software Patch-Surfer2.0. We applied the integrated approach to identify nicotinamide adenine dinucleotide (NAD)-binding proteins in the Escherichia coli proteome, which has over 4200 proteins. Pulse proteolysis and Patch-Surfer2.0 identified 78 and 36 potential NAD-binding proteins, respectively, including 12 proteins that were consistently detected by the two approaches. Interestingly, the 12 proteins included 8 that are not previously known as NAD binders. Further validation of these eight proteins showed that their binding affinities to NAD computed by AutoDock Vina are higher than their cognate ligands and also that their protein ratios in the pulse proteolysis are consistent with known NAD-binding proteins. These results strongly suggest that these eight proteins are indeed newly identified NAD binders.

Probing the stereospecificity of tyrosyl- and glutaminyl-tRNA synthetase with molecular dynamics

The stereospecificity of aminoacyl-tRNA synthetases helps exclude d-amino acids from protein synthesis and could perhaps be engineered to allow controlled d-amino acylation of tRNA. We use molecular dynamics simulations to probe the stereospecificity of the class I tyrosyl- and glutaminyl-tRNA synthetases (TyrRS, GlnRS), including wildtype enzymes and three point mutants suggested by three different protein design methods. l/d binding free energy differences are obtained by alchemically and reversibly transforming the ligand from L to D in simulations of the protein-ligand complex. The D81Q mutation in Escherichia coli TyrRS is homologous to the D81R mutant shown earlier to have inverted stereospecificity. D81Q is predicted to lead to a rotated ligand backbone and an increased, not a decreased l-Tyr preference. The E36Q mutation in Methanococcus jannaschii TyrRS has a predicted l/d binding free energy difference ΔΔG of just 0.5±0.9kcal/mol, compared to 3.1±0.8kcal/mol for the wildtype enzyme (favoring l-Tyr). The ligand ammonium position is preserved in the d-Tyr complex, while the carboxylate is shifted. Wildtype GlnRS has a similar preference for l-glutaminyl adenylate; the R260Q mutant has an increased preference, even though Arg260 makes a large contribution to the wildtype ΔΔG value.

Computational protein design with backbone plasticity

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

Biofuel metabolic engineering with biosensors

Metabolic engineering offers the potential to renewably produce important classes of chemicals, particularly biofuels, at an industrial scale. DNA synthesis and editing techniques can generate large pathway libraries, yet identifying the best variants is slow and cumbersome. Traditionally, analytical methods like chromatography and mass spectrometry have been used to evaluate pathway variants, but such techniques cannot be performed with high throughput. Biosensors - genetically encoded components that actuate a cellular output in response to a change in metabolite concentration - are therefore a promising tool for rapid and high-throughput evaluation of candidate pathway variants. Applying biosensors can also dynamically tune pathways in response to metabolic changes, improving balance and productivity. Here, we describe the major classes of biosensors and briefly highlight recent progress in applying them to biofuel-related metabolic pathway engineering.

Design of Redox-Active Peptides: Towards Functional Materials

In nature, the majority of processes that occur in the cell involve the cycling of electrons and protons, changing the reduction and oxidation state of substrates to alter their chemical reactivity and usefulness in vivo. One of the most relevant examples of these processes is the electron transport chain, a series of oxidoreductase proteins that shuttle electrons through well-defined pathways, concurrently moving protons across the cell membrane. Inspired by these processes, researchers have sought to develop materials to mimic natural systems for a number of applications, including fuel production. The most common cofactors found in proteins to carry out electron transfer are iron sulfur clusters and porphyrin-like molecules. Both types have been studied within natural proteins, such as in photosynthetic machinery or soluble electron carriers; in parallel, an extensive literature has developed over recent years attempting to model and study these cofactors within peptide-based materials. This chapter will focus on major designs that have significantly advanced the field.

Computational approaches to investigating allostery

Allosteric regulation plays a key role in many biological processes, such as signal transduction, transcriptional regulation, and many more. It is rooted in fundamental thermodynamic and dynamic properties of macromolecular systems that are still poorly understood and are moreover modulated by the cellular context. Here we review the computational approaches used in the investigation of allosteric processes in protein systems. We outline how the models of allostery have evolved from their initial formulation in the sixties to the current views, which more fully account for the roles of the thermodynamic and dynamic properties of the system. We then describe the major classes of computational approaches employed to elucidate the mechanisms of allostery, the insights they have provided, as well as their limitations. We complement this analysis by highlighting the role of computational approaches in promising practical applications, such as the engineering of regulatory modules and identifying allosteric binding sites.

Protein Assembly: Versatile Approaches to Construct Highly Ordered Nanostructures

Nature endows life with a wide variety of sophisticated, synergistic, and highly functional protein assemblies. Following Nature's inspiration to assemble protein building blocks into exquisite nanostructures is emerging as a fascinating research field. Dictating protein assembly to obtain highly ordered nanostructures and sophisticated functions not only provides a powerful tool to understand the natural protein assembly process but also offers access to advanced biomaterials. Over the past couple of decades, the field of protein assembly has undergone unexpected and rapid developments, and various innovative strategies have been proposed. This Review outlines recent advances in the field of protein assembly and summarizes several strategies, including biotechnological strategies, chemical strategies, and combinations of these approaches, for manipulating proteins to self-assemble into desired nanostructures. The emergent applications of protein assemblies as versatile platforms to design a wide variety of attractive functional materials with improved performances have also been discussed. The goal of this Review is to highlight the importance of this highly interdisciplinary field and to promote its growth in a diverse variety of research fields ranging from nanoscience and material science to synthetic biology.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

High-Throughput Peptide Screening on a Bimodal Imprinting Chip Through MS-SPRi Integration

Screening of high affinity and high specificity peptide probes towards various targets is important in the biomedical field while traditional peptide screening procedure is manual and tedious. Herein, a bimodal imprinting microarray system to embrace the whole peptide screening process is presented. Surface Plasmon Resonance imaging (SPRi) and matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) are combined for both quantitative and qualitative identification of the peptide. The method provides a solution for high efficiency peptide screening.

Rapid protein immobilization for thin film continuous flow biocatalysis

A versatile enzyme immobilization strategy for thin film continuous flow processing is reported. Here, non-covalent and glutaraldehyde bioconjugation are used to immobilize enzymes on the surfaces of borosilicate reactors. This approach requires only ng of protein per reactor tube, with the stock protein solution readily recycled to sequentially coat >10 reactors. Confining reagents to thin films during immobilization reduced the amount of protein, piranha-cleaning solution, and other reagents by ∼96%. Through this technique, there was no loss of catalytic activity over 10 h processing. The results reported here combines the benefits of thin film flow processing with the mild conditions of biocatalysis.

Binding Preferences of Amino Acids for Gold Nanoparticles: A Molecular Simulation Study

A better understanding of the binding preference of amino acids for gold nanoparticles of different diameters could aid in the design of peptides that bind specifically to nanoparticles of a given diameter. Here we identify the binding preference of 19 natural amino acids for three gold nanoparticles with diameters of 1.0, 2.0, and 4.0 nm, and investigate the mechanisms that govern these preferences. We calculate potentials of mean force between 36 entities (19 amino acids and 17 side chains) and the three gold nanoparticles in explicit water using well-tempered metadynamics simulations. Comparing these potentials of mean force determines the amino acids' nanoparticle binding preferences and if these preferences are controlled by the backbone, the side chain, or both. Twelve amino acids prefer to bind to the 4.0 nm gold nanoparticle, and seven prefer to bind to the 2.0 nm one. We also use atomistic molecular dynamics simulations to investigate how water molecules near the nanoparticle influence the binding of the amino acids. The solvation shells of the larger nanoparticles have higher water densities than those of the smaller nanoparticles while the orientation distributions of the water molecules in the shells of all three nanoparticles are similar. The nanoparticle preferences of the amino acids depend on whether their binding free energy is determined mainly by their ability to replace or to reorient water molecules in the nanoparticle solvation shell. The amino acids whose binding free energy depends mainly on the replacement of water molecules are likely to prefer to bind to the largest nanoparticle and tend to have relatively simple side chain structures. Those whose binding free energy depends mainly on their ability to reorient water molecules prefer a smaller nanoparticle and tend to have more complex side chain structures.

Algorithms for protein design

Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.

Rapid construction of metabolite biosensors using domain-insertion profiling

Single-fluorescent protein biosensors (SFPBs) are an important class of probes that enable the single-cell quantification of analytes in vivo. Despite advantages over other detection technologies, their use has been limited by the inherent challenges of their construction. Specifically, the rational design of green fluorescent protein (GFP) insertion into a ligand-binding domain, generating the requisite allosteric coupling, remains a rate-limiting step. Here, we describe an unbiased approach, termed domain-insertion profiling with DNA sequencing (DIP-seq), that combines the rapid creation of diverse libraries of potential SFPBs and high-throughput activity assays to identify functional biosensors. As a proof of concept, we construct an SFPB for the important regulatory sugar trehalose. DIP-seq analysis of a trehalose-binding-protein reveals allosteric hotspots for GFP insertion and results in high-dynamic range biosensors that function robustly in vivo. Taken together, DIP-seq simultaneously accelerates metabolite biosensor construction and provides a novel tool for interrogating protein allostery.

In the construction of single fluorescent protein biosensors, selection of the insertion point of a fluorescent protein into a ligand-binding domain is a rate-limiting step. Here, the authors develop an unbiased, high-throughput approach, called domain insertion profiling with DNA sequencing (DIP-seq), to generate a novel trehalose biosensor.

Towards programmable plant genetic circuits

Synthetic biology enables the construction of genetic circuits with predictable gene functions in plants. Detailed quantitative descriptions of the transfer function or input-output function for genetic parts (promoters, 5' and 3' untranslated regions, etc.) are collected. These data are then used in computational simulations to determine their robustness and desired properties, thereby enabling the best components to be selected for experimental testing in plants. In addition, the process forms an iterative workflow which allows vast improvement to validated elements with sub-optimal function. These processes enable computational functions such as digital logic in living plants and follow the pathway of technological advances which took us from vacuum tubes to cell phones.

Introduction of a polar core into the de novo designed protein Top7

Design of polar interactions is a current challenge for protein design. The de novo designed protein Top7, like almost all designed proteins, has an entirely nonpolar core. Here we describe the replacing of a sizable fraction (5 residues) of this core with a designed polar hydrogen bond network. The polar core design is expressed at high levels in E. coli, has a folding free energy of 10 kcal/mol, and retains the multiphasic folding kinetics of the original Top7. The NMR structure of the design shows that conformations of three of the five residues, and the designed hydrogen bonds between them, are very close to those in the design model. The remaining two residues, which are more solvent exposed, sample a wide range of conformations in the NMR ensemble. These results show that hydrogen bond networks can be designed in protein cores, but also highlight challenges that need to be overcome when there is competition with solvent.

Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic

Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc.

Advances in NMR Methods To Map Allosteric Sites: From Models to Translation

The last five years have witnessed major developments in the understanding of the allosteric phenomenon, broadly defined as coupling between remote molecular sites. Such advances have been driven not only by new theoretical models and pharmacological applications of allostery, but also by progress in the experimental approaches designed to map allosteric sites and transitions. Among these techniques, NMR spectroscopy has played a major role given its unique near-atomic resolution and sensitivity to the dynamics that underlie allosteric couplings. Here, we highlight recent progress in the NMR methods tailored to investigate allostery with the goal of offering an overview of which NMR approaches are best suited for which allosterically relevant questions. The picture of the allosteric "NMR toolbox" is provided starting from one of the simplest models of allostery (i.e., the four-state thermodynamic cycle) and continuing to more complex multistate mechanisms. We also review how such an "NMR toolbox" has assisted the elucidation of the allosteric molecular basis for disease-related mutations and the discovery of novel leads for allosteric drugs. From this overview, it is clear that NMR plays a central role not only in experimentally validating transformative theories of allostery, but also in tapping the full translational potential of allosteric systems.

Designed multi-stranded heme binding β-sheet peptides in membrane

Designed peptides demonstrating well-defined structures and functioning in membrane environment are of significant interest in developing novel proteins for membrane active biological processes including enzymes, electron transfer, ion channels and energy conversion. Heme proteins' ability to carry out multiple functions in nature has inspired the design of several helical heme binding peptides and proteins soluble in water and also recently in membrane. Naturally occurring β-sheet proteins are both water and membrane soluble, and are known to bind heme, however, designed heme binding β-sheet proteins are yet to be reported, plausibly because of the complex folding and difficulty in introducing heme binding sites in the β-sheet structures. Here, we describe the design, NMR structures and biochemical functional characterization of four stranded and six stranded membrane soluble β-sheet peptides that bind heme and di-heme, respectively. The designed peptides contain either DP-G or DP-DA residues for the nucleation of β-turns intended to stabilize multi-stranded β-sheet topologies and ligate heme with bis-His coordination between adjacent antiparallel β-strands. Furthermore, we have optimized a high affinity heme binding pocket, Kd ∼ nM range, in the adjacent β-strands by utilizing a series of four stranded β-sheet peptides employing β- and ω-amino acids. We find that there is a progressive increase in cofactor binding affinity in the designed peptides with the alkyl chain length of ω-amino acids. Notably, the six stranded β-sheet peptide binds two molecules of heme in a cooperative fashion. The designed peptides perform peroxidase activity with varying ability and efficiently carried out electron transfer with membrane associated protein cytochrome c. The current study demonstrates the designing of functional β-sheet proteins in a membrane environment and expands the repertoire of heme protein design.

Designer protein delivery: From natural to engineered affinity-controlled release systems

Exploiting binding affinities between molecules is an established practice in many fields, including biochemical separations, diagnostics, and drug development; however, using these affinities to control biomolecule release is a more recent strategy. Affinity-controlled release takes advantage of the reversible nature of noncovalent interactions between a therapeutic protein and a binding partner to slow the diffusive release of the protein from a vehicle. This process, in contrast to degradation-controlled sustained-release formulations such as poly(lactic-co-glycolic acid) microspheres, is controlled through the strength of the binding interaction, the binding kinetics, and the concentration of binding partners. In the context of affinity-controlled release--and specifically the discovery or design of binding partners--we review advances in in vitro selection and directed evolution of proteins, peptides, and oligonucleotides (aptamers), aided by computational design.

Genetic circuit design automation

Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits forEscherichia coli(880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.

Biosensor-based engineering of biosynthetic pathways

Biosynthetic pathways provide an enzymatic route from inexpensive renewable resources to valuable metabolic products such as pharmaceuticals and plastics. Designing these pathways is challenging due to the complexities of biology. Advances in the design and construction of genetic variants has enabled billions of cells, each possessing a slightly different metabolic design, to be rapidly generated. However, our ability to measure the quality of these designs lags by several orders of magnitude. Recent research has enabled cells to report their own success in chemical production through the use of genetically encoded biosensors. A new engineering discipline is emerging around the creation and application of biosensors. Biosensors, implemented in selections and screens to identify productive cells, are paving the way for a new era of biotechnological progress.

Overcoming Challenges in Engineering the Genetic Code

Withstanding 3.5 billion years of genetic drift, the canonical genetic code remains such a fundamental foundation for the complexity of life that it is highly conserved across all three phylogenetic domains. Genome engineering technologies are now making it possible to rationally change the genetic code, offering resistance to viruses, genetic isolation from horizontal gene transfer, and prevention of environmental escape by genetically modified organisms. We discuss the biochemical, genetic, and technological challenges that must be overcome in order to engineer the genetic code.

Why reinvent the wheel? Building new proteins based on ready-made parts

We protein engineers are ambivalent about evolution: on the one hand, evolution inspires us with myriad examples of biomolecular binders, sensors, and catalysts; on the other hand, these examples are seldom well-adapted to the engineering tasks we have in mind. Protein engineers have therefore modified natural proteins by point substitutions and fragment exchanges in an effort to generate new functions. A counterpoint to such design efforts, which is being pursued now with greater success, is to completely eschew the starting materials provided by nature and to design new protein functions from scratch by using de novo molecular modeling and design. While important progress has been made in both directions, some areas of protein design are still beyond reach. To this end, we advocate a synthesis of these two strategies: by using design calculations to both recombine and optimize fragments from natural proteins, we can build stable and as of yet un-sampled structures, thereby granting access to an expanded repertoire of conformations and desired functions. We propose that future methods that combine phylogenetic analysis, structure and sequence bioinformatics, and atomistic modeling may well succeed where any one of these approaches has failed on its own.

Drugging Membrane Protein Interactions

The majority of therapeutics target membrane proteins, accessible on the surface of cells, to alter cellular signaling. Cells use membrane proteins to transduce signals into cells, transport ions and molecules, bind cells to a surface or substrate, and catalyze reactions. Newly devised technologies allow us to drug conventionally "undruggable" regions of membrane proteins, enabling modulation of protein-protein, protein-lipid, and protein-nucleic acid interactions. In this review, we survey the state of the art of high-throughput screening and rational design in drug discovery, and we evaluate the advances in biological understanding and technological capacity that will drive pharmacotherapy forward against unorthodox membrane protein targets.

A new concept for molecular engineering of artificial enzymes: a multiscale simulation

We propose a new concept for the design of artificial enzymes from synthetic protein-like copolymers and non-natural functional monomers which in terms of their affinity for water can be divided into two categories: hydrophobic and hydrophilic. Hydrophilic monomers comprise catalytically active groups similar to those in the corresponding amino acid residues. A key ingredient of our approach is that the target globular conformation of protein-like, core-shell morphology with multiple catalytic groups appears spontaneously in the course of controlled radical polymerization in a selective solvent. As a proof of concept, we construct a fully synthetic analog of serine hydrolase, e.g.α-chymotrypsin, using the conformation-dependent sequence design approach and multiscale simulation that combines the methods of "mesoscale chemistry" and atomistic molecular dynamics (MD). A 100 ns GPU-accelerated MD simulation of the designed polymer-supported catalyst in the aqueous environment provides valuable information on the structural organization of this system that has been synthesized in our Lab.

Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments

The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.

Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY

Library methods are widely used to study protein-protein interactions, and high-throughput screening or selection followed by sequencing can identify a large number of peptide ligands for a protein target. In this chapter, we describe a procedure called "SORTCERY" that can rank the affinities of library members for a target with high accuracy. SORTCERY follows a three-step protocol. First, fluorescence-activated cell sorting (FACS) is used to sort a library of yeast-displayed peptide ligands according to their affinities for a target. Second, all sorted pools are deep sequenced. Third, the resulting data are analyzed to create a ranking. We demonstrate an application of SORTCERY to the problem of ranking peptide ligands for the anti-apoptotic regulator Bcl-xL.

Rosetta and the Design of Ligand Binding Sites

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

In vitro evaluation of the conjugations of neonicotinoids with transport protein: photochemistry, ligand docking and molecular dynamics studies

The flexibility of ligand structures and the property of substituents in neonicotinoids play a pivotal role in protein–neonicotinoid and this type of biorecognition may have a great impact on the potential toxicity of these widely used agrochemicals.

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014

The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.

A general strategy to construct small molecule biosensors in eukaryotes

Biosensors for small molecules can be used in applications that range from metabolic engineering to orthogonal control of transcription. Here, we produce biosensors based on a ligand-binding domain (LBD) by using a method that, in principle, can be applied to any target molecule. The LBD is fused to either a fluorescent protein or a transcriptional activator and is destabilized by mutation such that the fusion accumulates only in cells containing the target ligand. We illustrate the power of this method by developing biosensors for digoxin and progesterone. Addition of ligand to yeast, mammalian, or plant cells expressing a biosensor activates transcription with a dynamic range of up to ~100-fold. We use the biosensors to improve the biotransformation of pregnenolone to progesterone in yeast and to regulate CRISPR activity in mammalian cells. This work provides a general methodology to develop biosensors for a broad range of molecules in eukaryotes.

DOI:http://dx.doi.org/10.7554/eLife.10606.001

Small molecules play essential roles in organisms, and so methods to sense these molecules within living cells could have wide-ranging uses in both biology and biotechnology. However, current methods for making new “biosensors” are limited and only a narrow range of small molecules can be detected.

One approach to biosensor design in yeast and other eukaryotic organisms uses proteins called ligand-binding domains, which bind to small molecules. Here, Feng, Jester, Tinberg, Mandell et al. have developed a new method to make biosensors from ligand-binding domains that could, in principle, be applied to any target small molecule. The new method involves taking a ligand-binding domain that is either engineered or occurs in nature and linking it to something that can be readily detected, such as a protein that fluoresces or that controls gene expression. This combined biosensor protein is then engineered, via mutations, such that it is unstable unless it binds to the small molecule. This means that, in the absence of the small molecule, these proteins are destroyed inside living cells. However, the binding of a target molecule to one of these proteins protects it from degradation, which allows the signal to be detected.

Feng, Jester, Tinberg, Mandell et al. use this method to create biosensors for a human hormone called progesterone and a drug called digoxin, which is used to treat heart disease. Further experiments used the biosensors to optimize the production of progesterone in yeast and to regulate the activity of a gene editing protein called Cas9 in human cells. The biosensors can be also used to produce long-term environmental sensors in plant cells.

This approach makes it possible to produce a wide variety of biosensors for different organisms. The next step is to continue to explore the ability of various proteins to be converted into biosensors, and to find out how easy it is to transfer a biosensor produced in one species to another.

DOI:http://dx.doi.org/10.7554/eLife.10606.002

Integrating gene synthesis and microfluidic protein analysis for rapid protein engineering

The capability to rapidly design proteins with novel functions will have a significant impact on medicine, biotechnology and synthetic biology. Synthetic genes are becoming a commodity, but integrated approaches have yet to be developed that take full advantage of gene synthesis. We developed a solid-phase gene synthesis method based on asymmetric primer extension (APE) and coupled this process directly to high-throughput, on-chip protein expression, purification and characterization (via mechanically induced trapping of molecular interactions, MITOMI). By completely circumventing molecular cloning and cell-based steps, APE-MITOMI reduces the time between protein design and quantitative characterization to 3–4 days. With APE-MITOMI we synthesized and characterized over 400 zinc-finger (ZF) transcription factors (TF), showing that although ZF TFs can be readily engineered to recognize a particular DNA sequence, engineering the precise binding energy landscape remains challenging. We also found that it is possible to engineer ZF–DNA affinity precisely and independently of sequence specificity and that in silico modeling can explain some of the observed affinity differences. APE-MITOMI is a generic approach that should facilitate fundamental studies in protein biophysics, and protein design/engineering.