Designing proteins for therapeutic applications

Fundamentals to function: Quantitative and scalable approaches for measuring protein stability

Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.

A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation

A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.

Industrial Production of Therapeutic Proteins: Cell Lines, Cell Culture, and Purification

A central pillar of the biotechnology and pharmaceutical industries continues to be the development of biological drug products manufactured from engineered mammalian cell lines. Since the hugely successful launch of human tissue plasminogen activator in 1987 and erythropoietin in 1988, the biopharmaceutical market has grown immensely. In 2014, biotherapeutics made up a significant portion of global drug sales as 7 of the top 10 and 21 of top 50 selling pharmaceuticals in the world were biologics with over US$100 billion in global sales (Table 1, [1]).

Protein thermostability engineering

Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.

Cis-suppression to arrest protein aggregation in mammalian cells

Protein misfolding and aggregation are implicated in numerous human diseases and significantly lower production yield of proteins expressed in mammalian cells. Despite the importance of understanding and suppressing protein aggregation in mammalian cells, a protein design and selection strategy to modulate protein misfolding/aggregation in mammalian cells has not yet been reported. In this work, we address the particular challenge presented by mutation-induced protein aggregation in mammalian cells. We hypothesize that an additional mutation(s) can be introduced in an aggregation-prone protein variant, spatially near the original mutation, to suppress misfolding and aggregation (cis-suppression). As a model protein, we chose human copper, zinc superoxide dismutase mutant (SOD1(A4V) ) containing an alanine to valine mutation at residue 4, associated with the familial form of amyotrophic lateral sclerosis. We used the program RosettaDesign to identify Phe20 in SOD1(A4V) as a key residue responsible for SOD1(A4V) conformational destabilization. This information was used to rationally develop a pool of candidate mutations at the Phe20 site. After two rounds of mammalian-cell based screening of the variants, three novel SOD1(A4V) variants with a significantly reduced aggregation propensity inside cells were selected. The enhanced stability and reduced aggregation propensity of the three novel SOD1(A4V) variants were verified using cell fractionation and in vitro stability assays.

Endoglucanases: insights into thermostability for biofuel applications

Obtaining bioethanol from cellulosic biomass involves numerous steps, among which the enzymatic conversion of the polymer to individual sugar units has been a main focus of the biotechnology industry. Among the cellulases that break down the polymeric cellulose are endoglucanases that act synergistically for subsequent hydrolytic reactions. The endoglucanases that have garnered relatively more attention are those that can withstand high temperatures, i.e., are thermostable. Although our understanding of thermostability in endoglucanases is incomplete, some molecular features that are responsible for increased thermostability have been recently identified. This review focuses on the investigations of endoglucanases and their implications for biofuel applications.

PROTS-RF: a robust model for predicting mutation-induced protein stability changes

The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.

Chimeric glutathione S-transferases containing inserts of kininogen peptides: potential novel protein therapeutics

The study of synthetic peptides corresponding to discrete regions of proteins has facilitated the understanding of protein structure-activity relationships. Short peptides can also be used as powerful therapeutic agents. However, in many instances, small peptides are prone to rapid degradation or aggregation and may lack the conformation required to mimic the functional motifs of the protein. For peptides to function as pharmacologically active agents, efficient production or expression, high solubility, and retention of biological activity through purification and storage steps are required. We report here the design, expression, and functional analysis of eight engineered GST proteins (denoted GSHKTs) in which peptides ranging in size from 8 to 16 amino acids and derived from human high molecular weight kininogen (HK) domain 5 were inserted into GST (between Gly-49 and Leu-50). Peptides derived from HK are known to inhibit cell proliferation, angiogenesis, and tumor metastasis, and the biological activity of the HK peptides was dramatically (>50-fold) enhanced following insertion into GST. GSHKTs are soluble and easily purified from Escherichia coli by affinity chromatography. Functionally, these hybrid proteins cause inhibition of endothelial cell proliferation. Crystallographic analysis of GSHKT10 and GSHKT13 (harboring 10- and 13-residue HK peptides, respectively) showed that the overall GST structure was not perturbed. These results suggest that the therapeutic efficacy of short peptides can be enhanced by insertion into larger proteins that are easily expressed and purified and that GST may potentially be used as such a carrier.

PROTS: a fragment based protein thermo-stability potential

Designing proteins with enhanced thermo-stability has been a main focus of protein engineering because of its theoretical and practical significance. Despite extensive studies in the past years, a general strategy for stabilizing proteins still remains elusive. Thus effective and robust computational algorithms for designing thermo-stable proteins are in critical demand. Here we report PROTS, a sequential and structural four-residue fragment based protein thermo-stability potential. PROTS is derived from a nonredundant representative collection of thousands of thermophilic and mesophilic protein structures and a large set of point mutations with experimentally determined changes of melting temperatures. To the best of our knowledge, PROTS is the first protein stability predictor based on integrated analysis and mining of these two types of data. Besides conventional cross validation and blind testing, we introduce hypothetical reverse mutations as a means of testing the robustness of protein thermo-stability predictors. In all tests, PROTS demonstrates the ability to reliably predict mutation induced thermo-stability changes as well as classify thermophilic and mesophilic proteins. In addition, this white-box predictor allows easy interpretation of the factors that influence mutation induced protein stability changes at the residue level.

A signal processing-based bioinformatics approach to assessing drug resistance: human immunodeficiency virus as a case study

Measuring drug resistance is one of the challenging and essential pharmaceutical activities. It is a laborious and costly laboratory-based experimentation. Various clinical and experimental analyses for measuring drug resistance have been carried out. Results have been obtained for different types of therapeutic agents as a consequence of changes in the amino acids compositions in the sequence (mutation) of the organisms involved. In the same manner, the positions of these amino acids alterations and the level of resistance (folds) have also been experimentally identified. For example, G36S and V38M mutation in the Human Immunodeficiency Virus (HIV) Transmembrane glycoprotein (gp41) has been found to cause 100-fold resistance. However, there does not seem to have bioinformatics method developed in which the amino acid information of the proteins involved in the studies were used to computationally assess the degree of drug resistance without involving laboratory-based experimental procedure. The post-genomic era has witnessed the relevance of Bioinformatics approaches in the analysis of huge biomedical data. One such approach is the analysis of protein residues using digital signal processing technique such as informational spectrum method (ISM). Therefore, we propose a new bioinformatics method that is capable of assessing drug resistance without the use of any laboratory-based experiments. This new method incorporates ISM, sequence information of the proteins and other relevant information. By using the ISM and EIIP amino acid scale, the technique was applied in three classes of anti-HIV/AIDS drugs as a case study. It is observed that the protein residues of the susceptible strains attained the maximal peak amplitude at the consensus frequency while the resistant strains maintained lower amplitudes. This result signifies lower contribution from the resistant strains due to the mutation. The findings are consistent with those of the experimental ones and therefore suggest that the approach taken can be used to help assess drug resistance without laboratory-based experimentation. It should also be noted that the method can be applied in other drug resistance studies where sequence information of proteins is available and help design a computer-aided drug resistance calculator.

Structural insights into conformational stability of wild-type and mutant beta1-adrenergic receptor

Recent experiments to derive a thermally stable mutant of turkey beta-1-adrenergic receptor (beta1AR) have shown that a combination of six single point mutations resulted in a 20 degrees C increase in thermal stability in mutant beta1AR. Here we have used the all-atom force-field energy function to calculate a stability score to detect stabilizing point mutations in G-protein coupled receptors. The calculated stability score shows good correlation with the measured thermal stability for 76 single point mutations and 22 multiple mutants in beta1AR. We have demonstrated that conformational sampling of the receptor for various mutants improve the prediction of thermal stability by 50%. Point mutations Y227A5.58, V230A5.61, and F338M7.48 in the thermally stable mutant m23-beta1AR stabilizes key microdomains of the receptor in the inactive conformation. The Y227A5.58 and V230A5.61 mutations stabilize the ionic lock between R139(3.50) on transmembrane helix3 and E285(6.30) on transmembrane helix6. The mutation F338M7.48 on TM7 alters the interaction of the conserved motif NPxxY(x)5,6F with helix8 and hence modulates the interaction of TM2-TM7-helix8 microdomain. The D186-R317 salt bridge (in extracellular loops 2 and 3) is stabilized in the cyanopindolol-bound wild-type beta1AR, whereas the salt bridge between D184-R317 is preferred in the mutant m23. We propose that this could be the surrogate to a similar salt bridge found between the extracellular loop 2 and TM7 in beta2AR reported recently. We show that the binding energy difference between the inactive and active states is less in m23 compared to the wild-type, which explains the activation of m23 at higher norepinephrine concentration compared to the wild-type. Results from this work throw light into the mechanism behind stabilizing mutations. The computational scheme proposed in this work could be used to design stabilizing mutations for other G-protein coupled receptors.

Combined use of experimental and computational screens to characterize protein stability

One of the primary goals of protein design is to engineer proteins with improved stability. Protein stability is a key issue for chemical, biotechnology and pharmaceutical industries. The development of robust proteins/enzymes with the ability to withstand the potentially harsh conditions of industrial operations is of high importance. A number of strategies are currently being employed to achieve this goal. Two particular approaches, (i) directed evolution and (ii) computational protein design, are quite powerful yet have only recently been combined or applied and analyzed in parallel. In directed evolution, libraries of variants are searched experimentally for clones possessing the desired properties. With computational methods, protein design algorithms are utilized to perform in silico screening for stable protein sequences. Here, we used gene libraries of an unstable variant of streptococcal protein G (Gbeta1) and an in vivo screening method to identify stabilized variants. Many variants with notably increased thermal stabilities were isolated and characterized. Concomitantly, computational techniques and protein design algorithms were used to perform in silico screening of the same destabilized variant of Gbeta1. The combined use, and critical analysis, of these methods promises to advance the field of protein design.

Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins

Identification of the characteristic structural patterns responsible for protein thermostability is theoretically important and practically useful but largely remains an open problem. These patterns may be revealed through comparative study on thermophilic and mesophilic proteins that have distinct thermostability. In this study, we constructed several distance-dependant potentials from thermophilic and mesophilic proteins. These potentials were then used to evaluate the structural difference between thermophilic and mesophilic proteins. We found that using the subtraction or division of the potentials derived from thermophilic and mesophilic proteins can dramatically increase the discriminatory ability. This approach revealed that the ability to distinct the subtle structural features responsible for protein thermostability may be effectively enhanced through rationally designed comparative study.

Glycosylation of therapeutic proteins: an effective strategy to optimize efficacy

During their development and administration, protein-based drugs routinely display suboptimal therapeutic efficacies due to their poor physicochemical and pharmacological properties. These innate liabilities have driven the development of molecular strategies to improve the therapeutic behavior of protein drugs. Among the currently developed approaches, glycoengineering is one of the most promising, because it has been shown to simultaneously afford improvements in most of the parameters necessary for optimization of in vivo efficacy while allowing for targeting to the desired site of action. These include increased in vitro and in vivo molecular stability (due to reduced oxidation, cross-linking, pH-, chemical-, heating-, and freezing-induced unfolding/denaturation, precipitation, kinetic inactivation, and aggregation), as well as modulated pharmacodynamic responses (due to altered potencies from diminished in vitro enzymatic activities and altered receptor binding affinities) and improved pharmacokinetic profiles (due to altered absorption and distribution behaviors, longer circulation lifetimes, and decreased clearance rates). This article provides an account of the effects that glycosylation has on the therapeutic efficacy of protein drugs and describes the current understanding of the mechanisms by which glycosylation leads to such effects.

Increasing protein stability by improving beta-turns

Our goal was to gain a better understanding of how protein stability can be increased by improving beta-turns. We studied 22 beta-turns in nine proteins with 66-370 residues by replacing other residues with proline and glycine and measuring the stability. These two residues are statistically preferred in some beta-turn positions. We studied: Cold shock protein B (CspB), Histidine-containing phosphocarrier protein, Ubiquitin, Ribonucleases Sa2, Sa3, T1, and HI, Tryptophan synthetase alpha-subunit, and Maltose binding protein. Of the 15 single proline mutations, 11 increased stability (Average = 0.8 +/- 0.3; Range = 0.3-1.5 kcal/mol), and the stabilizing effect of double proline mutants was additive. On the basis of this and our previous work, we conclude that proteins can generally be stabilized by replacing nonproline residues with proline residues at the i + 1 position of Type I and II beta-turns and at the i position in Type II beta-turns. Other turn positions can sometimes be used if the phi angle is near -60 degrees for the residue replaced. It is important that the side chain of the residue replaced is less than 50% buried. Identical substitutions in beta-turns in related proteins give similar results. Proline substitutions increase stability mainly by decreasing the entropy of the denatured state. In contrast, the large, diverse group of proteins considered here had almost no residues in beta-turns that could be replaced by Gly to increase protein stability. Improving beta-turns by substituting Pro residues is a generally useful way of increasing protein stability.

A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants

Background

The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical problem is the lack of reliable methods to estimate the relative thermostability of possible mutants.

Results

We report a novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting the relative thermostability of protein mutants. The scoring function was developed based on an elaborate analysis of a set of features calculated or predicted from 540 pairs of hyperthermophilic and mesophilic protein ortholog sequences. It was constructed by a linear combination of ten important features identified by a feature ranking procedure based on the random forest classification algorithm. The weights of these features in the scoring function were fitted by a hill-climbing algorithm. This scoring function has shown an excellent ability to discriminate hyperthermophilic from mesophilic sequences. The prediction accuracies reached 98.9% and 97.3% in discriminating orthologous pairs in training and the holdout testing datasets, respectively. Moreover, the scoring function can distinguish non-homologous sequences with an accuracy of 88.4%. Additional blind tests using two datasets of experimentally investigated mutations demonstrated that the scoring function can be used to predict the relative thermostability of proteins and their mutants at very high accuracies (92.9% and 94.4%). We also developed an amino acid substitution preference matrix between mesophilic and hyperthermophilic proteins, which may be useful in designing more thermostable proteins.

Conclusions

We have presented a novel scoring function which can distinguish not only HP/MP ortholog pairs, but also non-homologous pairs at high accuracies. Most importantly, it can be used to accurately predict the relative stability of proteins and their mutants, as demonstrated in two blind tests. In addition, the residue substitution preference matrix assembled in this study may reflect the thermal adaptation induced substitution biases. A web server implementing the scoring function and the dataset used in this study are freely available at http://www.abl.ku.edu/thermorank/.

Understanding structural/functional properties of immunoconjugates for cancer therapy by computational approaches

Monoclonal antibodies coupled to highly toxic molecules (immunoconjugates) are currently being developed for cancer therapy. We have used an in silico procedure for evaluating some physicochemical properties of two tumor-targeting anti-HER2 immunoconjugates: (a) the single-chain antibody scFv(FRP5) linked to a bacterial toxin, that has been recently progressed to phase I clinical trial in human cancer; (b) the putative molecule formed by the intrinsically stable scFv(800E6), which has been proposed as toxin carrier to cancer cells in human therapy, joined to the same toxin of (a). Structural models of the immunoconjugates have been built by homology modeling and assessed by molecular dynamics simulations. The trajectories have been analyzed to extract some biochemical properties and to assess the potential effects of the toxin on the structure and dynamics of the anti-HER2 antibodies. The results of the computational approach indicate that the antibodies maintain their correct folding even in presence of the toxin, whereas a certain stiffness in correspondence of some structural regions is observed. Furthermore, the toxin does not seem to affect the antibody solubility, whereas it enhances the structural stability. The proposed computational approach represent a promising tool for analyzing some physicochemical properties of immunoconjugates and for predicting the effects of the linked toxin on structure, dynamics, and functionality of the antibodies.

A computational framework to empower probabilistic protein design

MOTIVATION

The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult.

RESULTS

In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future.

Peptide conformational equilibria computed via a single-stage shifting protocol

We study the conformational equilibria of two peptides using a novel statistical mechanics approach designed for calculating free energy differences between highly dissimilar conformational states. Our results elucidate the contrasting roles of entropy in implicitly solvated leucine dipeptide and decaglycine. The method extends earlier work by Voter and overcomes the notorious "overlap" problem in free energy computations by constructing a mathematically equivalent calculation with high conformational similarity. The approach requires only equilibrium simulations of the two states of interest, without the need for sampling transition states. We discuss possible extensions and optimizations of the approach.

The Role of histidine residues in low-pH-mediated viral membrane fusion

A central event in the invasion of a host cell by an enveloped virus is the fusion of viral and cell membranes. For many viruses, membrane fusion is driven by specific viral surface proteins that undergo large-scale conformational rearrangements, triggered by exposure to low pH in the endosome upon internalization. Here, we present evidence suggesting that in both class I (helical hairpin proteins) and class II (beta-structure-rich proteins) pH-dependent fusion proteins the protonation of specific histidine residues triggers fusion via an analogous molecular mechanism. These histidines are located in the vicinity of positively charged residues in the prefusion conformation, and they subsequently form salt bridges with negatively charged residues in the postfusion conformation. The molecular surfaces involved in the corresponding structural rearrangements leading to fusion are highly conserved and thus might provide a suitable common target for the design of antivirals, which could be active against a diverse range of pathogenic viruses.

A Critical Evaluation of Tm(FTIR) Measurements of High-Concentration IgG1 Antibody Formulations as a Formulation Development Tool

PURPOSE

Fourier-transform infrared (FTIR) spectroscopy was applied for the determination of protein melting temperature (Tm(FTIR)) and to assess the stability predictability of a 100-mg/mL liquid IgG1 antibody formulation.

METHODS

Tm(FTIR) values of various formulations (different pH, buffers, excipients) were compared to the results of a stability study under accelerated conditions (40 degrees C/75% relative humidity), using size-exclusion high-performance liquid chromatography (SE-HPLC) and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for the detection of soluble aggregates and covalent modifications.

RESULTS

The highest Tm(FTIR) was achieved at pH 5.5, and, similarly, SE-HPLC and SDS-PAGE results suggested a pH optimum between 5.5 and 6.0. Transition temperatures were comparable for all tested buffers. However, the decrease in the monomer fraction upon thermal storage was the lowest for citrate buffers. Whereas sugars and polyols resulted in an increase in Tm(FTIR) and enhanced monomer fraction after storage, amino acids showed a destabilization according to SE-HPLC analysis, albeit no change or even an increase in the melting temperature was observed.

CONCLUSIONS

All examples gave evidence that Tm(FTIR) values did not necessarily correspond to the storage stability at 40 degrees C analyzed by means of SE-HPLC and SDS-PAGE. Tm values, e.g., determined by FTIR, should only be employed as supportive information to the results from both real-time and accelerated stability studies.

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.

Rational design of intercellular adhesion molecule-1 (ICAM-1) variants for antagonizing integrin lymphocyte function-associated antigen-1-dependent adhesion

The interaction between integrin lymphocyte function-associated antigen-1 (LFA-1) and its ligand intercellular adhesion molecule-1 (ICAM-1) is critical in immunological and inflammatory reactions but, like other adhesive interactions, is of low affinity. Here, multiple rational design methods were used to engineer ICAM-1 mutants with enhanced affinity for LFA-1. Five amino acid substitutions 1) enhance the hydrophobicity and packing of residues surrounding Glu-34 of ICAM-1, which coordinates to a Mg2+ in the LFA-1 I domain, and 2) alter associations at the edges of the binding interface. The affinity of the most improved ICAM-1 mutant for intermediate- and high-affinity LFA-1 I domains was increased by 19-fold and 22-fold, respectively, relative to wild type. Moreover, potency was similarly enhanced for inhibition of LFA-1-dependent ligand binding and cell adhesion. Thus, rational design can be used to engineer novel adhesion molecules with high monomeric affinity; furthermore, the ICAM-1 mutant holds promise for targeting LFA-1-ICAM-1 interaction for biological studies and therapeutic purposes.

Highly stable mutants of human fibroblast growth factor-1 exhibit prolonged biological action

Fibroblast growth factor 1 (FGF-1) shows strong angiogenic, osteogenic and tissue-injury repair properties that might be relevant to medical applications. Since FGF-1 is partially unfolded at physiological temperature we decided to increase significantly its conformational stability and test how such an improvement will affect its biological function. Using an homology approach and rational strategy we designed two new single FGF-1 mutations: Q40P and S47I that appeared to be the most strongly stabilizing substitutions among those reported so far, increasing the denaturation temperature by 7.8 deg. C and 9.0 deg. C, respectively. As our goal was to produce highly stable variants of the growth factor, we combined these two mutations with five previously described stabilizing substitutions. The multiple mutants showed denaturation temperatures up to 27 deg. C higher than the wild-type and exhibited full additivity of the mutational effects. All those mutants were biologically competent in several cell culture assays, maintaining typical FGF-1 activities, such as binding to specific cell surface receptors and activation of downstream signaling pathways. Thus, we demonstrate that the low denaturation temperature of wild-type FGF-1 is not related to its fundamental cellular functions, and that FGF-1 action is not affected by its stability. A more detailed analysis of the biological behavior of stable FGF-1 mutants revealed that, compared with the wild-type, their mitogenic properties, as probed by the DNA synthesis assay, were significantly increased in the absence of heparin, and that their half-lives were extensively prolonged. We found that the biological action of the mutants was dictated by their susceptibility to proteases, which strongly correlated with the stability. Mutants which were much more resistant to proteolytic degradation always displayed a significant improvement in the half-life and mitogenesis. Our results show that engineered stable growth factor variants exhibit enhanced and prolonged activity, which can be advantageous in terms of the potential therapeutic applications of FGF-1.

Data management solutions for protein therapeutic research and development

Protein therapeutics, including monoclonal antibodies, are a growing focus of drug discovery research organizations. High-throughput screening of large libraries of protein variants is therefore becoming increasingly important in R&D. As a result, there is a need to link large numbers of variant protein sequences with chemical and biological assay data. This integration will allow more efficient data mining and facilitate decision-making regarding hit identification, lead optimization and drug development. In this paper, we present an implementation in which a widely used small-molecule high-throughput screening data management system has been adapted to meet the unique needs of protein drug discovery and development.

Integrating cell-level kinetic modeling into the design of engineered protein therapeutics

Functional genomics and proteomics are identifying many potential drug targets for novel therapeutic proteins, and both rational and combinatorial protein engineering methods are available for creating drug candidates. A central challenge is the definition of the most appropriate design criteria, which will benefit critically from computational kinetic models that incorporate integration from the molecular level to the whole systems level. Interpretation of these processes will require mathematical models that are refined in combination with relevant data derived from quantitative assays, to correctly set biophysical objectives for protein design.

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.

Highly specific protein–protein interactions, evolution and negative design

We consider highly specific protein-protein interactions in proteomes of simple model proteins. We are inspired by the work of Zarrinpar et al (2003 Nature 426 676). They took a binding domain in a signalling pathway in yeast and replaced it with domains of the same class but from different organisms. They found that the probability of a protein binding to a protein from the proteome of a different organism is rather high, around one half. We calculate the probability of a model protein from one proteome binding to the protein of a different proteome. These proteomes are obtained by sampling the space of functional proteomes uniformly. In agreement with Zarrinpar et al we find that the probability of a protein binding a protein from another proteome is rather high, of order one tenth. Our results, together with those of Zarrinpar et al, suggest that designing, say, a peptide to block or reconstitute a single signalling pathway, without affecting any other pathways, requires knowledge of all the partners of the class of binding domains the peptide is designed to mimic. This knowledge is required to use negative design to explicitly design out interactions of the peptide with proteins other than its target. We also found that patches that are required to bind with high specificity evolve more slowly than those that are required only to not bind to any other patch. This is consistent with some analysis of sequence data for proteins engaged in highly specific interactions.

Designing sequence to control protein function in an EF-hand protein

The extent of conformational change that calcium binding induces in EF-hand proteins is a key biochemical property specifying Ca(2+) sensor versus signal modulator function. To understand how differences in amino acid sequence lead to differences in the response to Ca(2+) binding, comparative analyses of sequence and structures, combined with model building, were used to develop hypotheses about which amino acid residues control Ca(2+)-induced conformational changes. These results were used to generate a first design of calbindomodulin (CBM-1), a calbindin D(9k) re-engineered with 15 mutations to respond to Ca(2+) binding with a conformational change similar to that of calmodulin. The gene for CBM-1 was synthesized, and the protein was expressed and purified. Remarkably, this protein did not exhibit any non-native-like molten globule properties despite the large number of mutations and the nonconservative nature of some of them. Ca(2+)-induced changes in CD intensity and in the binding of the hydrophobic probe, ANS, implied that CBM-1 does undergo Ca(2+) sensorlike conformational changes. The X-ray crystal structure of Ca(2+)-CBM-1 determined at 1.44 A resolution reveals the anticipated increase in hydrophobic surface area relative to the wild-type protein. A nascent calmodulin-like hydrophobic docking surface was also found, though it is occluded by the inter-EF-hand loop. The results from this first calbindomodulin design are discussed in terms of progress toward understanding the relationships between amino acid sequence, protein structure, and protein function for EF-hand CaBPs, as well as the additional mutations for the next CBM design.

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.

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.

Efficient use of nonequilibrium measurement to estimate free energy differences for molecular systems

A promising method for calculating free energy differences DeltaF is to generate nonequilibrium data via "fast-growth" simulations or by experiments--and then use Jarzynski's equality. However, a difficulty with using Jarzynski's equality is that DeltaF estimates converge very slowly and unreliably due to the nonlinear nature of the calculation--thus requiring large, costly data sets. The purpose of the work presented here is to determine the best estimate for DeltaF given a (finite) set of work values previously generated by simulation or experiment. Exploiting statistical properties of Jarzynski's equality, we present two fully automated analyses of nonequilibrium data from a toy model, and various simulated molecular systems. Both schemes remove at least several k(B)T of bias from DeltaF estimates, compared to direct application of Jarzynski's equality, for modest sized data sets (100 work values), in all tested systems. Results from one of the new methods suggest that good estimates of DeltaF can be obtained using 5-40-fold less data than was previously possible. Extending previous work, the new results exploit the systematic behavior of bias due to finite sample size. A key innovation is better use of the more statistically reliable information available from the raw data.