Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.     

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.     

IPRO: an iterative computational protein library redesign and optimization procedure

A number of computational approaches have been developed to reengineer promising chimeric proteins one at a time through targeted point mutations. In this article, we introduce the computational procedure IPRO (iterative protein redesign and optimization procedure) for the redesign of an entire combinatorial protein library in one step using energy-based scoring functions. IPRO relies on identifying mutations in the parental sequences, which when propagated downstream in the combinatorial library, improve the average quality of the library (e.g., stability, binding affinity, specific activity, etc.). Residue and rotamer design choices are driven by a globally convergent mixed-integer linear programming formulation. Unlike many of the available computational approaches, the procedure allows for backbone movement as well as redocking of the associated ligands after a prespecified number of design iterations. IPRO can also be used, as a limiting case, for the redesign of a single or handful of individual sequences. The application of IPRO is highlighted through the redesign of a 16-member library of Escherichia coli/Bacillus subtilis dihydrofolate reductase hybrids, both individually and through upstream parental sequence redesign, for improving the average binding energy. Computational results demonstrate that it is indeed feasible to improve the overall library quality as exemplified by binding energy scores through targeted mutations in the parental sequences.     

Designing novel spectral classes of proteins with a tryptophan-expanded genetic code

Fluorescence methods are now well-established and powerful tools to study biological macromolecules. The canonical amino acid tryptophan (Trp), encoded by a single UGG triplet, is the main reporter of intrinsic fluorescence properties of most natural proteins and peptides and is thus an attractive target for tailoring their spectral properties. Recent advances in research have provided substantial evidence that the natural protein translational machinery can be genetically reprogrammed to introduce a large number of non-coded (i.e. noncanonical) Trp analogues and surrogates into various proteins. Especially attractive targets for such an engineering approach are fluorescent proteins in which the chromophore is formed post-translationally from an amino acid sequence, like the green fluorescent protein from Aequorea victoria. With the currently available translationally active fluoro-, hydroxy-, amino-, halogen-, and chalcogen-containing Trp analogues and surrogates, the traditional methods for protein engineering and design can be supplemented or even fully replaced by these novel approaches. Future research will provide a further increase in the number of Trp-like amino acids that are available for redesign (by engineering of the genetic code) of native Trp residues and enable novel strategies to generate proteins with tailored spectral properties.     

Molecular recognition with designed peptides and proteins

The design of proteins and peptides as molecular receptors is a rapidly growing area of research. Two primary approaches have been utilized, involving the minimization of known protein binding motifs or the de novo design of binding pockets within well-folded protein structures. These approaches are complementary and help define the minimum requirements necessary for biomolecular recognition. Recent advances in this area include the design of cavities within helix bundles for the binding of anesthetics, the design of beta-hairpins for the recognition of nucleotides and oligonucleotides, the redesign of protein binding sites for unique ligands, and the design of mini-proteins via protein grafting for the recognition of proteins and DNA. These advances provide exciting new opportunities to develop novel biosensors, de novo designed catalysts, exogenously triggered synthetic signal transduction cascades, and novel approaches to therapeutic treatments.     

Longer action means better drug: tuning up protein therapeutics

An increasing number of proteins are currently available on the market as therapeutics and this branch of the pharmaceutical industry will expand substantially during the coming years. As many diseases result from dysfunction of proteins forming multicomponent complexes, protein drugs with their inherent high specificity and affinity seem to be optimal medical agents. On the other hand, proteins are often highly instable and sensitive to degradation, which questions their applicability as effective therapeutics. Therefore, redesign and engineering of proteins is usually a required step in the present day drug development.Several approaches have been applied to optimize the protein properties central to their pharmaceutical use. This review focuses on different strategies that improve two crucial factors influencing protein drug efficiency: protein stability and its in vivo half-life. We provide examples of successful genetic and chemical modifications applied in the design of effective protein therapeutics.     

Designing proteins from the inside out

Globular proteins are characterized by the specific and tight packing of hydrophobic side-chains in the so-called "hydrophobic core." Formation of the core is key in folding, stabilization, and conformational specificity. The critical role of hydrophobic cores in maintaining the highly ordered structures present in natural proteins justifies the tremendous efforts devoted to their redesign. Both experimental and computational combinatorial-based approaches have been reported in the last years as powerful protein design tools. These manage to explore large regions of the sequence/conformational space, allowing the search for alternative protein core arrangements displaying native-like properties. The overall results obtained from core design projects have contributed significantly to our present knowledge of protein folding and function. In addition, core design has worked as a benchmark for the development of ambitious protein design projects that nowadays are allowing the de novo design of novel protein structures and functions.     

Optimizing non-natural protein function with directed evolution

Developing technologies such as unnatural amino acid mutagenesis, non-natural cofactor engineering, and computational design are generating proteins with novel functions; these proteins, however, often do not reach performance targets and would benefit from further optimization. Evolutionary methods can complement these approaches: recent work combining unnatural amino acid mutagenesis and phage selection has created useful proteins of novel composition. Weak initial activity in a computationally designed enzyme has been improved by iterative rounds of mutagenesis and screening. A marriage of ingenuity and evolution will expand the scope of protein function well beyond Mother Nature's designs.     

Advances in computational protein design

Computational protein design continues to experience a variety of methodological advances. Several improvements have been suggested for the objective functions used to quantify sequence/structure compatibility. Disparate design strategies based upon dead-end elimination, simulated annealing and statistical design have each recently yielded striking successes involving de novo designed proteins with sizes on the order of 100 residues or greater. Such methods may be used to design new proteins, as well as to redesign natural proteins to facilitate structural and biophysical studies.     

Expansion of the genetic code enables design of a novel "gold" class of green fluorescent proteins

Much effort has been dedicated to the design of significantly red shifted variants of the green fluorescent protein (GFP) from Aequoria victora (av). These approaches have been based on classical engineering with the 20 canonical amino acids. We report here an expansion of these efforts by incorporation of an amino substituted variant of tryptophan into the "cyan" GFP mutant, which turned it into a "gold" variant. This variant possesses a red shift in emission unprecedented for any avFP, similar to "red" FPs, but with enhanced stability and a very low aggregation tendency. An increasing number of non-natural amino acids are available for chromophore redesign (by engineering of the genetic code) and enable new general strategies to generate novel classes of tailor-made GFP proteins.     

Structure-based optimization of designed Armadillo-repeat proteins

The armadillo domain is a right‐handed super‐helix of repeating units composed of three α‐helices each. Armadillo repeat proteins (ArmRPs) are frequently involved in protein–protein interactions, and because of their modular recognition of extended peptide regions they can serve as templates for the design of artificial peptide binding scaffolds. On the basis of sequential and structural analyses, different consensus‐designed ArmRPs were synthesized and show high thermodynamic stabilities, compared to naturally occurring ArmRPs. We determined the crystal structures of four full‐consensus ArmRPs with three or four identical internal repeats and two different designs for the N‐ and C‐caps. The crystal structures were refined at resolutions ranging from 1.80 to 2.50 Å for the above mentioned designs. A redesign of our initial caps was required to obtain well diffracting crystals. However, the structures with the redesigned caps caused domain swapping events between the N‐caps. To prevent this domain swap, 9 and 6 point mutations were introduced in the N‐ and C‐caps, respectively. Structural and biophysical analysis showed that this subsequent redesign of the N‐cap prevented domain swapping and improved the thermodynamic stability of the proteins. We systematically investigated the best cap combinations. We conclude that designed ArmRPs with optimized caps are intrinsically stable and well‐expressed monomeric proteins and that the high‐resolution structures provide excellent structural templates for the continuation of the design of sequence‐specific modular peptide recognition units based on armadillo repeats.     

What makes a protein a protein? Hydrophobic core designs that specify stability and structural properties.

Here we describe how the systematic redesign of a protein's hydrophobic core alters its structure and stability. We have repacked the hydrophobic core of the four-helix-bundle protein, Rop, with altered packing patterns and various side chain shapes and sizes. Several designs reproduce the structure and native-like properties of the wild-type, while increasing the thermal stability. Other designs, either with similar sizes but different shapes, or with decreased sizes of the packing residues, destabilize the protein. Finally, overpacking the core with the larger side chains causes a loss of native-like structure. These results allow us to further define the roles of tight residue packing and the burial of hydrophobic surface area in the construction of native-like proteins.     

Protein design.

Several noteworthy papers have been published in the past year in which the creation of interesting novel proteins, either by de novo design or the redesign of existing proteins, has been reported. Highlights include the successful design of proteins for binding specific ligands.     

Exploring amino-acid radical chemistry: protein engineering and de novo design

Amino-acid radical enzymes are often highly complex structures containing multiple protein subunits and cofactors. These properties have in many cases hampered the detailed characterization of their amino-acid redox cofactors. To address this problem, a range of approaches has recently been developed in which a common strategy is to reduce the complexity of the radical-containing system. This work will be reviewed and it includes the light-induced generation of aromatic radicals in small-molecule and peptide systems. Natural redox proteins, including the blue copper protein azurin and a bacterial photosynthetic reaction center, have been engineered to introduce amino-acid radical chemistry. The redesign strategies to achieve this remarkable change in the properties of these proteins will be described. An additional approach to gain insights into the properties of amino-acid radicals is to synthesize de novo designed model proteins in which the redox chemistry of these species can be studied. Here we describe the design, synthesis and characteristics of monomeric three-helix bundle and four-helix bundle proteins designed to study the redox chemistry of tryptophan and tyrosine. This work demonstrates that de novo protein design combined with structural, electrochemical and quantum chemical analyses can provide detailed information on how the protein matrix tunes the thermodynamic properties of tryptophan.     

Exploring amino-acid radical chemistry: protein engineering and de novo design

Amino-acid radical enzymes are often highly complex structures containing multiple protein subunits and cofactors. These properties have in many cases hampered the detailed characterization of their amino-acid redox cofactors. To address this problem, a range of approaches has recently been developed in which a common strategy is to reduce the complexity of the radical-containing system. This work will be reviewed and it includes the light-induced generation of aromatic radicals in small-molecule and peptide systems. Natural redox proteins, including the blue copper protein azurin and a bacterial photosynthetic reaction center, have been engineered to introduce amino-acid radical chemistry. The redesign strategies to achieve this remarkable change in the properties of these proteins will be described. An additional approach to gain insights into the properties of amino-acid radicals is to synthesize de novo designed model proteins in which the redox chemistry of these species can be studied. Here we describe the design, synthesis and characteristics of monomeric three-helix bundle and four-helix bundle proteins designed to study the redox chemistry of tryptophan and tyrosine. This work demonstrates that de novo protein design combined with structural, electrochemical and quantum chemical analyses can provide detailed information on how the protein matrix tunes the thermodynamic properties of tryptophan.     

Knowledge-based potentials in protein design

Knowledge-based potentials are statistical parameters derived from databases of known protein properties that empirically capture aspects of the physical chemistry of protein structure and function. These potentials play a key role in protein design by improving the accuracy of physics-based models of interatomic interactions and enhancing the computational efficiency of the design process by limiting the complexity of searching sequence space. Recently, knowledge-based potentials (in isolation or in combination with physics-based potentials) have been applied to the modification of existing protein function, the redesign of natural protein folds and the complete design of a non-natural protein fold. In addition, knowledge-based potentials appear to be providing important information about the global topology of amino acid interactions in natural proteins. A detailed study of the methods and products of these protein design efforts promises to greatly expand our understanding of proteins and the evolutionary process that created them.     

RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Folding aggregated proteins into functionally active forms

The successful expression and purification of proteins in an active form is essential for structural and biochemical studies. With rapid advances in genome sequencing and high-throughput structural biology, an increasing number of proteins are being identified as potential drug targets but are difficult to obtain in a form suitable for structural or biochemical studies. Although prokaryotic recombinant expression systems are often used, proteins obtained in this way are typically found to be insoluble. Several experimental approaches have therefore been developed to refold these aggregated proteins into a biologically active form, often suitable for structural studies. The major refolding strategies adopt one of two approaches - chromatographic methods or refolding in free solution - and both routes have been successfully used to refold a range of proteins. Future advances are likely to involve the development of automated approaches for protein refolding and purification.     

Membrane protein complexes

A wide range of membrane protein structures have been published during the past two years. These have included proteins from both eucaryotic and heterologously overexpressed sources. Whereas some of these proteins were crystallised using conventional techniques, others employed the new methods of lipidic cubic phase crystallisation and antibody fragment co-crystallisation. These and other new approaches to the expression and crystallisation of membrane proteins are accelerating structural studies of membrane protein complexes.