Computational protein design

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

Increasing affinity of interferon-γ receptor 1 to interferon-γ by computer-aided design

We describe a computer-based protocol to design protein mutations increasing binding affinity between ligand and its receptor. The method was applied to mutate interferon-γ receptor 1 (IFN-γ-Rx) to increase its affinity to natural ligand IFN-γ, protein important for innate immunity. We analyzed all four available crystal structures of the IFN-γ-Rx/IFN-γ complex to identify 40 receptor residues forming the interface with IFN-γ. For these 40 residues, we performed computational mutation analysis by substituting each of the interface receptor residues by the remaining standard amino acids. The corresponding changes of the free energy were calculated by a protocol consisting of FoldX and molecular dynamics calculations. Based on the computed changes of the free energy and on sequence conservation criteria obtained by the analysis of 32 receptor sequences from 19 different species, we selected 14 receptor variants predicted to increase the receptor affinity to IFN-γ. These variants were expressed as recombinant proteins in Escherichia coli, and their affinities to IFN-γ were determined experimentally by surface plasmon resonance (SPR). The SPR measurements showed that the simple computational protocol succeeded in finding two receptor variants with affinity to IFN-γ increased about fivefold compared to the wild-type receptor.

The development of a thermostable CiP (Coprinus cinereus peroxidase) through in silico design

Protein thermostability is a crucial issue in the practical application of enzymes, such as inorganic synthesis and enzymatic polymerization of phenol derivatives. Much attention has been focused on the enhancement and numerous successes have been achieved through protein engineering methods. Despite fruitful results based on random mutagenesis, it was still necessary to develop a novel strategy that can reduce the time and effort involved in this process. In this study, a rapid and effective strategy is described for increasing the thermal stability of a protein. Instead of random mutagenesis, a rational strategy was adopted to theoretically stabilize the thermo labile residues of a protein using computational methods. Protein residues with high flexibility can be thermo labile due to their large range of movement. Here, residue B factor values were used to identify putatively thermo labile residues and the RosettaDesign program was applied to search for stable sequences. Coprinus cinereus (CiP) heme peroxidase was selected as a model protein for its importance in commercial applications, such as the polymerization of phenolic compounds. Eleven CiP residues with the highest B factor values were chosen as target mutation sites for thermostabilization, and then redesigned using RosettaDesign to identify sequences. Eight mutants based on the redesigns, were produced as functional enzymes and two of these (S323Y and E328D) showed increased thermal stability over the wild-type in addition to conserved catalytic activity. Thus, this strategy can be used as a rapid and effective in silico design tool for obtaining thermostable proteins.

Tradeoff between stability and multispecificity in the design of promiscuous proteins

Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions.

In nature, some proteins are more social than others, interacting with a large number of partners. These “promiscuous” proteins play key roles in cellular signaling pathways whose disruption may lead to diseases such as cancer. The amino acid sequences of such proteins must have evolved to be optimal for combined interactions with all natural partners. However, the evolutionary process leading to this promiscuity is not fully understood. We address this subject by predicting amino acid sequences that would be most compatible for interaction with each partner on its own and those most compatible for binding multiple proteins. We find that these two types of sequences are substantially different, the latter more closely resembling the natural sequences of promiscuous proteins. We also find that promiscuous proteins contain certain regions that are necessary for interfacing with all of their partners, while other regions convey specific interactions with each particular target protein. We analyze the tradeoffs required for such proteins to bind multiple partners and find that only some degree of compromise is typically needed in order to permit interactions that are seemingly antagonistic. We conclude that the simulations reported here mimic well the natural evolution of proteins that associate with multiple partners.

TRITON: a graphical tool for ligand-binding protein engineering

Summary: The new version of the TRITON program provides user-friendly graphical tools for modeling protein mutants using the external program MODELLER and for docking ligands into the mutants using the external program AutoDock. TRITON can now be used to design ligand-binding proteins, to study protein–ligand binding mechanisms or simply to dock any ligand to a protein.

Availability: Executable files of TRITON are available free of charge for academic users at http://ncbr.chemi.muni.cz/triton/

Contact: triton@chemi.muni.cz

Supplementary information: Supplementary data are available at Bioinformatics online.

Full-sequence computational design and solution structure of a thermostable protein variant

Computational protein design procedures were applied to the redesign of the entire sequence of a 51 amino acid residue protein, Drosophila melanogaster engrailed homeodomain. Various sequence optimization algorithms were compared and two resulting designed sequences were experimentally evaluated. The two sequences differ by 11 mutations and share 22% and 24% sequence identity with the wild-type protein. Both computationally designed proteins were considerably more stable than the naturally occurring protein, with midpoints of thermal denaturation greater than 99 degrees C. The solution structure was determined for one of the two sequences using multidimensional heteronuclear NMR spectroscopy, and the structure was found to closely match the original design template scaffold.

Side chain placement using estimation of distribution algorithms

OBJECTIVE

This paper presents an algorithm for the solution of the side chain placement problem.

METHODS AND MATERIALS

The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of 425 proteins.

RESULTS

For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures.

CONCLUSIONS

The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computational cost of the algorithm introduced has been presented.

Ultra-fast evaluation of protein energies directly from sequence

The structure, function, stability, and many other properties of a protein in a fixed environment are fully specified by its sequence, but in a manner that is difficult to discern. We present a general approach for rapidly mapping sequences directly to their energies on a pre-specified rigid backbone, an important sub-problem in computational protein design and in some methods for protein structure prediction. The cluster expansion (CE) method that we employ can, in principle, be extended to model any computable or measurable protein property directly as a function of sequence. Here we show how CE can be applied to the problem of computational protein design, and use it to derive excellent approximations of physical potentials. The approach provides several attractive advantages. First, following a one-time derivation of a CE expansion, the amount of time necessary to evaluate the energy of a sequence adopting a specified backbone conformation is reduced by a factor of 10⁷ compared to standard full-atom methods for the same task. Second, the agreement between two full-atom methods that we tested and their CE sequence-based expressions is very high (root mean square deviation 1.1–4.7 kcal/mol, R² = 0.7–1.0). Third, the functional form of the CE energy expression is such that individual terms of the expansion have clear physical interpretations. We derived expressions for the energies of three classic protein design targets—a coiled coil, a zinc finger, and a WW domain—as functions of sequence, and examined the most significant terms. Single-residue and residue-pair interactions are sufficient to accurately capture the energetics of the dimeric coiled coil, whereas higher-order contributions are important for the two more globular folds. For the task of designing novel zinc-finger sequences, a CE-derived energy function provides significantly better solutions than a standard design protocol, in comparable computation time. Given these advantages, CE is likely to find many uses in computational structural modeling.

Many applications in computational structural biology involve evaluating the energy of a protein adopting a specific structure. A variety of functions are used for this purpose. Statistical potentials are fast to evaluate but do not have a clear biophysical basis, whereas physics-based functions consist of well-defined terms that can be costly to compute. This paper describes how the theory of cluster expansion, originally developed to describe the energies of alloys, can be applied to generate a physical potential for proteins that is extremely fast to evaluate. Cluster expansion is a way of representing a property of a system as a discrete function of its degrees of freedom. In this paper, it is used for the problem of protein design, where the energy is determined by the identities and conformations of amino acids at different sites on a fixed protein backbone. Application of cluster expansion to three small protein folds—the α-helical coiled coil, the zinc finger, and the WW domain—shows that protein sequence can be mapped directly to energy using a surprisingly simple function that maintains high accuracy. Promising results on these small systems suggest that the theory may have utility for macromolecular modeling more generally.

A Designed Branched Three-Helix Bundle Protein Dimer

The ultimate goals of de novo protein design are the construction of novel tertiary structures and functions. Here is presented the design and synthesis of a uniquely branched three-helix bundle that folds into a well-folded dimeric protein. The branching of this protein was performed by the method of native chemical ligation, which provides a chemoselective and stable amide bond between the unprotected fragments. This ligation strategy was possible by the presented facile preparation of a peptide (43 amino acids) with a specific side chain thioester, which is synthesized by general Fmoc solid phase peptide synthesis. From the presented structural analysis, it is seen that the folded protein is present as a stable and highly helical dimer, thus forming a six-helix bundle. This unique tertiary structure, composed of a dimer of three individual alpha-helices branched together, offers different possibilities for protein engineering, such as metal and cofactor binding sites, as well as for the construction of novel functions.

A designed well-folded monomeric four-helix bundle protein prepared by Fmoc solid-phase peptide synthesis and native chemical ligation

The design and total chemical synthesis of a monomeric native-like four-helix bundle protein is presented. The designed protein, GTD-Lig, consists of 90 amino acids and is based on the dimeric structure of the de novo designed helix-loop-helix GTD-43. GTD-Lig was prepared by the native chemical ligation strategy and the fragments (45 residues long) were synthesized by applying standard fluorenylmethoxycarbonyl (Fmoc) chemistry. The required peptide-thioester fragment was prepared by anchoring the free gamma-carboxy group of Fmoc-Glu-allyl to the solid phase. After chain elongation the allyl moiety was orthogonally removed and the resulting carboxy group was functionalized with a glycine-thioester followed by standard trifluoroacetic acid (TFA) cleavage to produce the unprotected peptide-thioester. The structure of the synthetic protein was examined by far- and near-UV circular dichroism (CD), sedimentation equilibrium ultracentrifugation, and NMR and fluorescence spectroscopy. The spectroscopic methods show a highly helical and native-like monomeric protein consistent with the design. Heat-induced unfolding was studied by tryptophan absorbance and far-UV CD. The thermal unfolding of GTD-Lig occurs in two steps; a cooperative transition from the native state to an intermediate state and thereafter by noncooperative melting to the unfolded state. The intermediate exhibits the properties of a molten globule such as a retained native secondary structure and a compact hydrophobic core. The thermodynamics of GuHCl-induced unfolding were evaluated by far-UV CD monitoring and the unfolding exhibited a cooperative transition that is well-fitted by a two-state mechanism from the native to the unfolded state. GTD-Lig clearly shows the characteristics of a native protein with a well-defined structure and typical unfolding transitions. The design and synthesis presented herein is of general applicability for the construction of large monomeric proteins.

Genetic algorithms as a tool for helix design – computational and experimental studies on prion protein helix 1

Evolutionary computing is a general optimization mechanism successfully implemented for a variety of numeric problems in a variety of fields, including structural biology. We here present an evolutionary approach to optimize helix stability in peptides and proteins employing the AGADIR energy function for helix stability as scoring function. With the ability to apply masks determining positions, which are to remain constant or fixed to a certain class of amino acids, our algorithm is capable of developing stable helical scaffolds containing a wide variety of structural and functional amino acid patterns. The algorithm showed good convergence behaviour in all tested cases and can be parameterized in a wide variety of ways. We have applied our algorithm for the optimization of the stability of prion protein helix 1, a structural element of the prion protein which is thought to play a crucial role in the conformational transition from the cellular to the pathogenic form of the prion protein, and which therefore poses an interesting target for pharmacological as well as genetic engineering approaches to counter the as of yet uncurable prion diseases. NMR spectroscopic investigations of selected stabilizing and destabilizing mutations found by our algorithm could demonstrate its ability to create stabilized variants of secondary structure elements.

A novel flow cytometry-based method for analysis of expression levels in Escherichia coli, giving information about precipitated and soluble protein

A high throughput method for screening of protein expression is described. By using a flow cytometer, levels of both soluble and precipitated protein can simultaneously be assessed in vivo. Protein fragments were fused to the N-terminus of enhanced GFP and the cell samples were analysed using a flow cytometer. Data concerning whole cell fluorescence and light scattering was collected. The whole cell fluorescence is probing intracellular concentrations of soluble fusion proteins. Concurrently, forward scattered light gives data about inclusion body formation, valuable information in process optimisation. To evaluate the method, the cells were disrupted, separated into soluble and non-soluble fractions and analysed by gel electrophoresis. A clear correlation between fluorescence and soluble target protein was shown. Interestingly, the distribution of the cells regarding forward scatter (standard deviation) correlates with the amount of inclusion bodies formed. Finally, the newly developed method was used to evaluate two different purification tags, His(6) and Z(basic), and their effect on the expression pattern.

Electrostatics in computational protein design

Catalytic activity and protein-protein recognition have proven to be significant challenges for computational protein design. Electrostatic interactions are crucial for these and other protein functions, and therefore accurate modeling of electrostatics is necessary for successfully advancing protein design into the realm of protein function. This review focuses on recent progress in modeling electrostatic interactions in computational protein design, with particular emphasis on continuum models.

Coarse-graining protein energetics in sequence variables

We show that cluster expansions (CE), previously used to model solid-state materials with binary or ternary configurational disorder, can be extended to the protein design problem. We present a generalized CE framework, in which properties such as energy can be unambiguously expanded in the amino-acid sequence space. The CE coarse grains over nonsequence degrees of freedom (e.g., side-chain conformations) and thereby simplifies the problem of designing proteins, or predicting the compatibility of a sequence with a given structure, by many orders of magnitude. The CE is physically transparent, and can be evaluated through linear regression on the energies of training sequences. We show, as example, that good prediction accuracy is obtained with up to pairwise interactions for a coiled-coil backbone, and that triplet interactions are important in the energetics of a more globular zinc-finger backbone.

Artificial diiron proteins: from structure to function

De novo protein design provides an attractive approach for the construction of models to probe the features required for the function of complex metalloproteins. These minimal models contain the essential elements believed necessary for activity of the protein. In this article, we summarize the design, structure determination, and functional properties of a family of artificial diiron proteins.

Computational thermostabilization of an enzyme

Thermostabilizing an enzyme while maintaining its activity for industrial or biomedical applications can be difficult with traditional selection methods. We describe a rapid computational approach that identified three mutations within a model enzyme that produced a 10 degrees C increase in apparent melting temperature T(m) and a 30-fold increase in half-life at 50 degrees C, with no reduction in catalytic efficiency. The effects of the mutations were synergistic, giving an increase in excess of the sum of their individual effects. The redesigned enzyme induced an increased, temperature-dependent bacterial growth rate under conditions that required its activity, thereby coupling molecular and metabolic engineering.

One- and two-body decomposable Poisson-Boltzmann methods for protein design calculations

Successfully modeling electrostatic interactions is one of the key factors required for the computational design of proteins with desired physical, chemical, and biological properties. In this paper, we present formulations of the finite difference Poisson-Boltzmann (FDPB) model that are pairwise decomposable by side chain. These methods use reduced representations of the protein structure based on the backbone and one or two side chains in order to approximate the dielectric environment in and around the protein. For the desolvation of polar side chains, the two-body model has a 0.64 kcal/mol RMSD compared to FDPB calculations performed using the full representation of the protein structure. Screened Coulombic interaction energies between side chains are approximated with an RMSD of 0.13 kcal/mol. The methods presented here are compatible with the computational demands of protein design calculations and produce energies that are very similar to the results of traditional FDPB calculations.

Computational protein design is a challenge for implicit solvation models

Increasingly complex schemes for representing solvent effects in an implicit fashion are being used in computational analyses of biological macromolecules. These schemes speed up the calculations by orders of magnitude and are assumed to compromise little on essential features of the solvation phenomenon. In this work we examine this assumption. Five implicit solvation models, a surface area-based empirical model, two models that approximate the generalized Born treatment and a finite difference Poisson-Boltzmann method are challenged in situations differing from those where these models were calibrated. These situations are encountered in automatic protein design procedures, whose job is to select sequences, which stabilize a given protein 3D structure, from a large number of alternatives. To this end we evaluate the energetic cost of burying amino acids in thousands of environments with different solvent exposures belonging, respectively, to decoys built with random sequences and to native protein crystal structures. In addition we perform actual sequence design calculations. Except for the crudest surface area-based procedure, all the tested models tend to favor the burial of polar amino acids in the protein interior over nonpolar ones, a behavior that leads to poor performance in protein design calculations. We show, on the other hand, that three of the examined models are nonetheless capable of discriminating between the native fold and many nonnative alternatives, a test commonly used to validate force fields. It is concluded that protein design is a particularly challenging test for implicit solvation models because it requires accurate estimates of the solvation contribution of individual residues. This contrasts with native recognition, which depends less on solvation and more on other nonbonded contributions.

A de novo redesign of the WW domain

We have used a sequence prediction algorithm and a novel sampling method to design protein sequences for the WW domain, a small beta-sheet motif. The procedure, referred to as SPANS, designs sequences to be compatible with an ensemble of closely related polypeptide backbones, mimicking the inherent flexibility of proteins. Two designed sequences (termed SPANS-WW1 and SPANS-WW2), using only naturally occurring L-amino acids, were selected for study and the corresponding polypeptides were prepared in Escherichia coli. Circular dichroism data suggested that both purified polypeptides adopted secondary structure features related to those of the target without the aid of disulfide bridges or bound cofactors. The structure exhibited by SPANS-WW2 melted cooperatively by raising the temperature of the solution. Further analysis of this polypeptide by proton nuclear magnetic resonance spectroscopy demonstrated that at 5 degrees C, it folds into a structure closely resembling a natural WW domain. This achievement constitutes one of a small number of successful de novo protein designs through fully automated computational methods and highlights the feasibility of including backbone flexibility in the design strategy.

Computational design of receptors for an organophosphate surrogate of the nerve agent soman

We report the computational design of soluble protein receptors for pinacolyl methyl phosphonic acid (PMPA), the predominant hydrolytic product of the nerve agent soman. Using recently developed computational protein design techniques, the ligand-binding pockets of two periplasmic binding proteins, glucose-binding protein and ribose-binding protein, were converted to bind PMPA instead of their cognate sugars. The designs introduce 9-12 mutations in the parent proteins. Twelve of 20 designs tested exhibited PMPA-dependent changes in emission intensity of a fluorescent reporter with affinities between 45 nM and 10 microM. The contributions to ligand binding by individual residues were determined in two designs by alanine-scanning mutagenesis, and are consistent with the molecular models. These results demonstrate that designed receptors with radically altered binding specificities and affinities that rival or exceed those of the parent proteins can be successfully predicted. The designs vary in parent scaffold, sequence diversity, and orientation of docked ligand, suggesting that the number of possible solutions to the design problem is large and degenerate. This observation has implications for the genesis of biological function by random processes. The designed receptors reported here may have utility in the development of fluorescent biosensors for monitoring nerve agents.

Can Contact Potentials Reliably Predict Stability of Proteins?

The simplest approximation of interaction potential between amino acid residues in proteins is the contact potential, which defines the effective free energy of a protein conformation by a set of amino acid contacts formed in this conformation. Finding a contact potential capable of predicting free energies of protein states across a variety of protein families will aid protein folding and engineering in silico on a computationally tractable time-scale. We test the ability of contact potentials to accurately and transferably (across various protein families) predict stability changes of proteins upon mutations. We develop a new methodology to determine the contact potentials in proteins from experimental measurements of changes in protein's thermodynamic stabilities (DeltaDeltaG) upon mutations. We apply our methodology to derive sets of contact interaction parameters for a hierarchy of interaction models including solvation and multi-body contact parameters. We test how well our models reproduce experimental measurements by statistical tests. We evaluate the maximum accuracy of predictions obtained by using contact potentials and the correlation between parameters derived from different data-sets of experimental (DeltaDeltaG) values. We argue that it is impossible to reach experimental accuracy and derive fully transferable contact parameters using the contact models of potentials. However, contact parameters may yield reliable predictions of DeltaDeltaG for datasets of mutations confined to the same amino acid positions in the sequence of a single protein.

Paradigms for computational nucleic acid design

The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR-based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free-energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are refined further. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape.

Exploring folding free energy landscapes using computational protein design

Recent advances in computational protein design have allowed exciting new insights into the sequence dependence of protein folding free energy landscapes. Whereas most previous studies have examined the sequence dependence of protein stability and folding kinetics by characterizing naturally occurring proteins and variants of these proteins that contain a small number of mutations, it is now possible to generate and characterize computationally designed proteins that differ significantly from naturally occurring proteins in sequence and/or structure. These computer-generated proteins provide insights into the determinants of protein structure, stability and folding, and make it possible to disentangle the properties of proteins that are the consequence of natural selection from those that reflect the fundamental physical chemistry of polypeptide chains.

Computational design and characterization of a monomeric helical dinuclear metalloprotein

The de novo design of di-iron proteins is an important step towards understanding the diversity of function among this complex family of metalloenzymes. Previous designs of due ferro (DF) proteins have resulted in tetrameric and dimeric four-helix bundles having crystallographically well-defined structures and active-site geometries. Here, the design and characterization of DFsc, a 114 residue monomeric four-helix bundle, is presented. The backbone was modeled using previous oligomeric structures and appropriate inter-helical turns. The identities of 26 residues were predetermined, including the primary and secondary ligands in the active site, residues involved in active site accessibility, and the gamma beta gamma beta turn between helices 2 and 3. The remaining 88 amino acid residues were determined using statistical computer aided design, which is based upon a recent statistical theory of protein sequences. Rather than sampling sequences, the theory directly provides the site-specific amino acid probabilities, which are then used to guide sequence design. The resulting sequence (DFsc) expresses well in Escherichia coli and is highly soluble. Sedimentation studies confirm that the protein is monomeric in solution. Circular dichroism spectra are consistent with the helical content of the target structure. The protein is structured in both the apo and the holo forms, with the metal-bound form exhibiting increased stability. DFsc stoichiometrically binds a variety of divalent metal ions, including Zn(II), Co(II), Fe(II), and Mn(II), with micromolar affinities. 15N HSQC NMR spectra of both the apo and Zn(II) proteins reveal excellent dispersion with evidence of a significant structural change upon metal binding. DFsc is then a realization of complete de novo design, where backbone structure, activity, and sequence are specified in the design process.

Rational design and engineering of therapeutic proteins

An increasing number of engineered protein therapeutics are currently being developed, tested in clinical trials and marketed for use. Many of these proteins arose out of hit-and-miss efforts to discover specific mutations, fusion partners or chemical modifications that confer desired properties. Through these efforts, several useful strategies have emerged for rational optimization of therapeutic candidates. The controlled manipulation of the physical, chemical and biological properties of proteins enabled by structure-based simulation is now being used to refine established rational engineering approaches and to advance new strategies. These methods provide clear, hypothesis-driven routes to solve problems that plague many proteins and to create novel mechanisms of action. We anticipate that rational protein engineering will shape the field of protein therapeutics dramatically by improving existing products and enabling the development of novel therapeutic agents.

Combining computational and experimental screening for rapid optimization of protein properties

We present a combined computational and experimental method for the rapid optimization of proteins. Using beta-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of approximately equal 200,000 mutants. These were then constructed and experimentally screened to select for variants with improved resistance to the antibiotic cefotaxime. In a single round, we obtained variants exhibiting a 1,280-fold increase in resistance. To our knowledge, all of the mutations were novel, i.e., they have not been identified as beneficial by random mutagenesis or DNA shuffling or seen in any of the naturally occurring TEM beta-lactamases, the most prevalent type of Gram-negative beta-lactamases. This combined approach allows for the rapid improvement of any property that can be screened experimentally and provides a powerful broadly applicable tool for protein engineering.

Modulating calmodulin binding specificity through computational protein design

We report the computational redesign of the protein-binding interface of calmodulin (CaM), a small, ubiquitous Ca(2+)-binding protein that is known to bind to and regulate a variety of functionally and structurally diverse proteins. The CaM binding interface was optimized to improve binding specificity towards one of its natural targets, smooth muscle myosin light chain kinase (smMLCK). The optimization was performed using optimization of rotamers by iterative techniques (ORBIT), a protein design program that utilizes a physically based force-field and the Dead-End Elimination theorem to compute sequences that are optimal for a given protein scaffold. Starting from the structure of the CaM-smMLCK complex, the program considered 10(22) amino acid residue sequences to obtain the lowest-energy CaM sequence. The resulting eightfold mutant, CaM_8, was constructed and tested for binding to a set of seven CaM target peptides. CaM_8 displayed high binding affinity to the smMLCK peptide (1.3nM), similar to that of the wild-type protein (1.8nM). The affinity of CaM_8 to six other target peptides was reduced, as intended, by 1.5-fold to 86-fold. Hence, CaM_8 exhibited increased binding specificity, preferring the smMLCK peptide to the other targets. Studies of this type may increase our understanding of the origins of binding specificity in protein-ligand complexes and may provide valuable information that can be used in the design of novel protein receptors and/or ligands.

Transmutation of human glutathione transferase A2-2 with peroxidase activity into an efficient steroid isomerase

A major goal in protein engineering is the tailor-making of enzymes for specified chemical reactions. Successful attempts have frequently been based on directed molecular evolution involving libraries of random mutants in which variants with desired properties were identified. For the engineering of enzymes with novel functions, it would be of great value if the necessary changes of the active site could be predicted and implemented. Such attempts based on the comparison of similar structures with different substrate selectivities have previously met with limited success. However, the present work shows that the knowledge-based redesign restricted to substrate-binding residues in human glutathione transferase A2-2 can introduce high steroid double-bond isomerase activity into the enzyme originally characterized by glutathione peroxidase activity. Both the catalytic center activity (k(cat)) and catalytic efficiency (k(cat)/K(m)) match the values of the naturally evolved glutathione transferase A3-3, the most active steroid isomerase known in human tissues. The substrate selectivity of the mutated glutathione transferase was changed 7000-fold by five point mutations. This example demonstrates the functional plasticity of the glutathione transferase scaffold as well as the potential of rational active-site directed mutagenesis as a complement to DNA shuffling and other stochastic methods for the redesign of proteins with novel functions.

Combinatorial protein design

Combinatorial protein libraries permit the examination of a wide range of sequences. Such methods are being used for denovo design and to investigate the determinants of protein folding. The exponentially large number of possible sequences, however, necessitates restrictions on the diversity of sequences in a combinatorial library. Recently, progress has been made in developing theoretical tools to bias and characterize the ensemble of sequences that fold into a given structure - tools that can be applied to the design and interpretation of combinatorial experiments.

Energy estimation in protein design

The progress achieved by several groups in the field of computational protein design shows that successful design methods include two major features: efficient algorithms to deal with the combinatorial exploration of sequence space and optimal energy functions to rank sequences according to their fitness for the given fold.

Development of a cytokine analog with enhanced stability using computational ultrahigh throughput screening

Granulocyte-colony stimulating factor (G-CSF) is used worldwide to prevent neutropenia caused by high-dose chemotherapy. It has limited stability, strict formulation and storage requirements, and because of poor oral absorption must be administered by injection (typically daily). Thus, there is significant interest in developing analogs with improved pharmacological properties. We used our ultrahigh throughput computational screening method to improve the physicochemical characteristics of G-CSF. Improving these properties can make a molecule more robust, enhance its shelf life, or make it more amenable to alternate delivery systems and formulations. It can also affect clinically important features such as pharmacokinetics. Residues in the buried core were selected for optimization to minimize changes to the surface, thereby maintaining the active site and limiting the designed protein's potential for antigenicity. Using a structure that was homology modeled from bovine G-CSF, core designs of 25-34 residues were completed, corresponding to 10(21)-10(28) sequences screened. The optimal sequence from each design was selected for biophysical characterization and experimental testing; each had 10-14 mutations. The designed proteins showed enhanced thermal stabilities of up to 13 degrees C, displayed five-to 10-fold improvements in shelf life, and were biologically active in cell proliferation assays and in a neutropenic mouse model. Pharmacokinetic studies in monkeys showed that subcutaneous injection of the designed analogs results in greater systemic exposure, probably attributable to improved absorption from the subcutaneous compartment. These results show that our computational method can be used to develop improved pharmaceuticals and illustrate its utility as a powerful protein design tool.