Protein sequence randomization: efficient estimation of protein stability using knowledge-based potentials

Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels

Over the past decades, the TIM-barrel fold has served as a model system for the exploration of how changes in protein sequences affect their structural, stability, and functional characteristics, and moreover, how this information can be leveraged to design proteins from the ground up. After numerous attempts to design de novo proteins with this specific fold, sTIM11 was the first validated de novo design of an idealized four-fold symmetric TIM barrel. Subsequent efforts to enhance the stability of this initial design resulted in the development of DeNovoTIMs, a family of de novo TIM barrels with various stabilizing mutations. In this study, we present an investigation into the biophysical and thermodynamic effects upon introducing a varying number of stabilizing mutations per quarter along the sequence of a four-fold symmetric TIM barrel. We compared the base design DeNovoTIM0 without any stabilizing mutations with variants containing mutations in one, two, three, and all four quarters-designated TIM1q, TIM2q, TIM3q, and DeNovoTIM6, respectively. This analysis revealed a stepwise and nonlinear change in the thermodynamic properties that correlated with the number of mutated quarters, suggesting positive nonadditive effects. To shed light on the significance of the location of stabilized quarters, we engineered two variants of TIM2q which contain the same number of mutations but positioned in different quarter locations. Characterization of these TIM2q variants revealed that the mutations exhibit varying effects on the overall protein stability, contingent upon the specific region in which they are introduced. These findings emphasize that the amount and location of stabilized interfaces among the four quarters play a crucial role in shaping the conformational stability of these four-fold symmetric TIM barrels. Analysis of de novo proteins, as described in this study, enhances our understanding of how sequence variations can finely modulate stability in both naturally occurring and computationally designed proteins.

Triosephosphate isomerase deficiency: Effect of F240L mutation on enzyme structure

Eleven missense mutations have been describe in human triosephosphate isomerase (TPI), affecting its catalytic function. Several of these mutations generate triosephosphate isomerase deficiency, the consequences of which can in some cases be lethal. The missense F240L mutation was found in a Hungarian patient showing symptoms of chronic hemolytic anemia and neuromuscular dysfunction. In vitro studies using a recombinant version of this mutant showed that it affects kinetic parameters, thermal stability and dimeric stability. Using X-ray crystal structures, the present paper describes how this mutation affected the flexibility of catalytic residues K13 and part of the (β/α) 8-barrel fold facing the dimeric interface in the TPI.

Amyloid Fibril Design: Limiting Structural Polymorphism in Alzheimer's Aβ Protofilaments

Nanoscale fibrils formed by amyloid peptides have a polymorphic character, adopting several types of molecular structures in similar growth conditions. As shown by experimental (e.g., solid-state NMR) and computational studies, amyloid fibril polymorphism hinders both the structural characterization of Alzheimer's Aβ amyloid protofilaments and fibrils at a molecular level, as well as the possible applications (e.g., development of drugs or biomarkers) that rely on similar, controlled molecular arrangements of the Aβ peptides in amyloid fibril structures. We have explored the use of several contact potentials for the efficient identification of minimal sequence mutations that could enhance the stability of specific fibril structures while simultaneously destabilizing competing topologies, controlling thus the amount of structural polymorphism in a rational way. We found that different types of contact potentials, while having only partial accuracy on their own, lead to similar results regarding ranking the compatibility of wild-type (WT) and mutated amyloid sequences with different fibril morphologies. This approach allows exhaustive screening and assessment of possible mutations and the identification of minimal consensus mutations that could stabilize fibrils with the desired topology at the expense of other topology types, a prediction that is further validated using atomistic molecular dynamics with explicit water molecules. We apply this two-step multiscale (i.e., residue and atomistic-level) approach to predict and validate mutations that could bias either parallel or antiparallel packing in the core Alzheimer's Aβ_9-40 amyloid fibril models based on solid-state NMR experiments. Besides shedding new light on the molecular origins of structural polymorphism in WT Aβ fibrils, our study could also lead to efficient tools for assisting future experimental approaches for amyloid fibril determination, and for the development of biomarkers or drugs aimed at interfering with the stability of amyloid fibrils, as well as for the future design of amyloid fibrils with a controlled (e.g., reduced) level of structural polymorphism.

Frustration, function and folding

Natural protein molecules are exceptional polymers. Encoded in apparently random strings of amino-acids, these objects perform clear physical tasks that are rare to find by simple chance. Accurate folding, specific binding, powerful catalysis, are examples of basic chemical activities that the great majority of polypeptides do not display, and are thought to be the outcome of the natural history of proteins. Function, a concept genuine to Biology, is at the core of evolution and often conflicts with the physical constraints. Locating the frustration between discrepant goals in a recurrent system leads to fundamental insights about the chances and necessities that shape the encoding of biological information.

Advances in molecular engineering of carbohydrate-binding modules

Carbohydrate-binding modules (CBMs) are non-catalytic domains that are generally appended to carbohydrate-active enzymes. CBMs have a broadly conserved structure that allows recognition of a notable variety of carbohydrates, in both their soluble and insoluble forms, as well as in their alpha and beta conformations and with different types of bonds or substitutions. This versatility suggests a high functional plasticity that is not yet clearly understood, in spite of the important number of studies relating protein structure and function. Several studies have explored the flexibility of these systems by changing or improving their specificity toward substrates of interest. In this review, we examine the molecular strategies used to identify CBMs with novel or improved characteristics. The impact of the spatial arrangement of the functional amino acids of CBMs is discussed in terms of unexpected new functions that are not related to the original biological roles of the enzymes. Proteins 2017; 85:1602-1617. © 2017 Wiley Periodicals, Inc.

Protein Stability: Enhancement and Measurement

This article defines protein stability, emphasizes its importance and surveys the field of protein stabilization, with summary reference to a selection of 2009-2015 publications. One can enhance stability by, in particular, protein engineering strategies and by chemical modification (including conjugation) in solution. General protocols are set out on how to measure a given protein's (1) kinetic thermal stability, and (2) oxidative stability, and (3) how to undertake chemical modification of a protein in solution.

Fold stability during endolysosomal acidification is a key factor for allergenicity and immunogenicity of the major birch pollen allergen

BACKGROUND

The search for intrinsic factors, which account for a protein's capability to act as an allergen, is ongoing. Fold stability has been identified as a molecular feature that affects processing and presentation, thereby influencing an antigen's immunologic properties.

OBJECTIVE

We assessed how changes in fold stability modulate the immunogenicity and sensitization capacity of the major birch pollen allergen Bet v 1.

METHODS

By exploiting an exhaustive virtual mutation screening, we generated mutants of the prototype allergen Bet v 1 with enhanced thermal and chemical stability and rigidity. Structural changes were analyzed by means of x-ray crystallography, nuclear magnetic resonance, and molecular dynamics simulations. Stability was monitored by using differential scanning calorimetry, circular dichroism, and Fourier transform infrared spectroscopy. Endolysosomal degradation was simulated in vitro by using the microsomal fraction of JAWS II cells, followed by liquid chromatography coupled to mass spectrometry. Immunologic properties were characterized in vitro by using a human T-cell line specific for the immunodominant epitope of Bet v 1 and in vivo in an adjuvant-free BALB/c mouse model.

RESULTS

Fold stabilization of Bet v 1 was pH dependent and resulted in resistance to endosomal degradation at a pH of 5 or greater, affecting presentation of the immunodominant T-cell epitope in vitro. These properties translated in vivo into a strong allergy-promoting TH2-type immune response. Efficient TH2 cell activation required both an increased stability at the pH of the early endosome and efficient degradation at lower pH in the late endosomal/lysosomal compartment.

CONCLUSIONS

Our data indicate that differential pH-dependent fold stability along endosomal maturation is an essential protein-inherent determinant of allergenicity.

MAESTRO--multi agent stability prediction upon point mutations

BACKGROUND

Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

RESULTS

We aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.

CONCLUSIONS

MAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

A novel alkyl hydroperoxidase (AhpD) of Anabaena PCC7120 confers abiotic stress tolerance in Escherichia coli

In silico analysis together with cloning, molecular characterization and heterologous expression reports that the hypothetical protein All5371 of Anabaena sp. PCC7120 is a novel hydroperoxide scavenging protein similar to AhpD of bacteria. The presence of E(X)11CX HC(X)3H motif in All5371 confers peroxidase activity and closeness to bacterial AhpD which is also reflected by its highest 3D structure homology with Rhodospirillum rubrum AhpD. Heterologous expression of all5371 complimented for ahpC and conferred resistance in MJF178 strain (ahpCF::Km) of Escherichia coli. All5371 reduced the organic peroxide more efficiently than inorganic peroxide and the recombinant E. coli strain following exposure to H2O2, CdCl2, CuCl2, heat, UV-B and carbofuron registered increased growth over wild-type and mutant E. coli transformed with empty vector. Appreciable expression of all5371 in Anabaena sp. PCC7120 as measured by qRT-PCR under selected stresses and their tolerance against H2O2, tBOOH, CuOOH and menadione attested its role in stress tolerance. In view of the above, All5371 of Anabaena PCC7120 emerged as a new hydroperoxide detoxifying protein.

Recognition of Errors in the Refinement and Validation of Three-Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA-Web

The structural model of begomovirus AC1 protein is useful for understanding biological function at molecular level and docking study. For this study we have used the ProSA program (Protein Structure Analysis) tool to establish the structure prediction and modeling of protein. This tool was used for refinement and validation of experimental protein structures. Potential problems of protein structures based on energy plots are easily seen by ProSA and are displayed in a three-dimensional manner. In the present study we have selected different AC1 proteins of begomovirus strains (YP_003288785, YP_002004579, and YP_003288773) for structural analysis and display of energy plots that highlight potential problems spotted in protein structures. The 3D models of Rep proteins with recognized errors can be effectively used for in silico docking study for development of potential ligand molecules against begomovirus infection.

Structural model for the interaction of a designed Ankyrin Repeat Protein with the human epidermal growth factor receptor 2

Designed Ankyrin Repeat Proteins are a class of novel binding proteins that can be selected and evolved to bind to targets with high affinity and specificity. We are interested in the DARPin H10-2-G3, which has been evolved to bind with very high affinity to the human epidermal growth factor receptor 2 (HER2). HER2 is found to be over-expressed in 30% of breast cancers, and is the target for the FDA-approved therapeutic monoclonal antibodies trastuzumab and pertuzumab and small molecule tyrosine kinase inhibitors. Here, we use computational macromolecular docking, coupled with several interface metrics such as shape complementarity, interaction energy, and electrostatic complementarity, to model the structure of the complex between the DARPin H10-2-G3 and HER2. We analyzed the interface between the two proteins and then validated the structural model by showing that selected HER2 point mutations at the putative interface with H10-2-G3 reduce the affinity of binding up to 100-fold without affecting the binding of trastuzumab. Comparisons made with a subsequently solved X-ray crystal structure of the complex yielded a backbone atom root mean square deviation of 0.84-1.14 Ångstroms. The study presented here demonstrates the capability of the computational techniques of structural bioinformatics in generating useful structural models of protein-protein interactions.

Emerging role of N- and C-terminal interactions in stabilizing (β/α)8 fold with special emphasis on Family 10 xylanases

Xylanases belong to an important class of industrial enzymes. Various xylanases have been purified and characterized from a plethora of organisms including bacteria, marine algae, plants, protozoans, insects, snails and crustaceans. Depending on the source, the enzymatic activity of xylanases varies considerably under various physico-chemical conditions such as temperature, pH, high salt and in the presence of proteases. Family 10 or glycosyl hydrolase 10 (GH10) xylanases are one of the well characterized and thoroughly studied classes of industrial enzymes. The TIM-barrel fold structure which is ubiquitous in nature is one of the characteristics of family 10 xylanases. Family 10 xylanases have been used as a “model system” due to their TIM-barrel fold to dissect and understand protein stability under various conditions. A better understanding of structure-stability-function relationships of family 10 xylanases allows one to apply these governing molecular rules to engineer other TIM-barrel fold proteins to improve their stability and retain function(s) under adverse conditions. In this review, we discuss the implications of N-and C-terminal interactions, observed in family 10 xylanases on protein stability under extreme conditions. The role of metal binding and aromatic clusters in protein stability is also discussed. Studying and understanding family 10 xylanase structure and function, can contribute to our protein engineering knowledge.

Cloning, expression and functional validation of drought inducible ascorbate peroxidase (Ec-apx1) from Eleusine coracana

Eleusine coracana (finger millet) is a stress-hardy but under-utilized cereal crop that possesses an efficient antioxidant defense system. The plant is capable of enduring long durations of water deficit stress. Experiments were conducted to clone a potent stress responsive isoform of ascorbate peroxidase and validate its role under drought stress. Reverse transcriptase PCR was used to obtain the partial cDNA of apx1 gene, from a meticulously screened drought tolerant genotype of E. coracana (PR202). Using RACE strategy, the full length apx1 cDNA was cloned and sequenced. The cDNA length of the E. coracana apx1 (Ec-apx1) gene is 1,047 bp with a 750 bp ORF, encoding a 250 amino acid protein having a molecular weight of 28.5 kDa. The identity of the amino acid sequence, deduced from the cDNA, with the APX family homologs was about 74-97 %. The full-length apx1 ORF was sub-cloned in a prokaryotic expression vector pET23b. The recombinant fusion protein, Ec-apx1, had high expression level in BL21 strain of E. coli and exhibited APX enzyme activity. The structure-function relationship of the protein was deduced by modelling a three-dimensional structure of Ec-apx1, on the basis of comparative homology using SWISS-MODEL. Real time PCR analysis of Ec-apx1 expression at mRNA level showed that the transcript increased under drought stress, with maximum levels attained 5-days after imposition of stress. Our results suggest that Ec-apx1 has a distinct pattern of expression and plays a pivotal role in drought stress tolerance. Therefore, the cloned isoform of ascorbate peroxidase can be used for developing stress tolerant genotypes of important crops, through transgenic approach.

Improving the thermostability of alpha-amylase by combinatorial coevolving-site saturation mutagenesis

Background

The generation of focused mutant libraries at hotspot residues is an important strategy in directed protein evolution. Existing methods, such as combinatorial active site testing and residual coupling analysis, depend primarily on the evolutionary conserved information to find the hotspot residues. Hardly any attention has been paid to another important functional and structural determinants, the functionally correlated variation information--coevolution.

Results

In this paper, we suggest a new method, named combinatorial coevolving-site saturation mutagenesis (CCSM), in which the functionally correlated variation sites of proteins are chosen as the hotspot sites to construct focused mutant libraries. The CCSM approach was used to improve the thermal stability of α-amylase from Bacillus subtilis CN7 (Amy7C). The results indicate that the CCSM can identify novel beneficial mutation sites, and enhance the thermal stability of wild-type Amy7C by 8°C ( T5030), which could not be achieved with the ordinarily rational introduction of single or a double point mutation.

Conclusions

Our method is able to produce more thermostable mutant α-amylases with novel beneficial mutations at new sites. It is also verified that the coevolving sites can be used as the hotspots to construct focused mutant libraries in protein engineering. This study throws new light on the active researches of the molecular coevolution.

Coarse-grain Protein Models

Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.

Effects of a non-conservative sequence on the properties of β-glucuronidase from Aspergillus terreus Li-20

We cloned the β-glucuronidase gene (AtGUS) from Aspergillus terreus Li-20 encoding 657 amino acids (aa), which can transform glycyrrhizin into glycyrrhetinic acid monoglucuronide (GAMG) and glycyrrhetinic acid (GA). Based on sequence alignment, the C-terminal non-conservative sequence showed low identity with those of other species; thus, the partial sequence AtGUS(-3t) (1-592 aa) was amplified to determine the effects of the non-conservative sequence on the enzymatic properties. AtGUS and AtGUS(-3t) were expressed in E. coli BL21, producing AtGUS-E and AtGUS(-3t)-E, respectively. At the similar optimum temperature (55°C) and pH (AtGUS-E, 6.6; AtGUS(-3t)-E, 7.0) conditions, the thermal stability of AtGUS(-3t)-E was enhanced at 65°C, and the metal ions Co(2+), Ca(2+) and Ni(2+) showed opposite effects on AtGUS-E and AtGUS(-3t)-E, respectively. Furthermore, Km of AtGUS(-3t)-E (1.95 mM) was just nearly one-seventh that of AtGUS-E (12.9 mM), whereas the catalytic efficiency of AtGUS(-3t)-E was 3.2 fold higher than that of AtGUS-E (7.16 vs. 2.24 mM s(-1)), revealing that the truncation of non-conservative sequence can significantly improve the catalytic efficiency of AtGUS. Conformational analysis illustrated significant difference in the secondary structure between AtGUS-E and AtGUS(-3t)-E by circular dichroism (CD). The results showed that the truncation of the non-conservative sequence could preferably alter and influence the stability and catalytic efficiency of enzyme.

Biophysical and structural considerations for protein sequence evolution

BACKGROUND

Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field.

RESULTS

Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS < 1 and gamma-distributed rates across sites.

CONCLUSIONS

Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model.

Novel nonlinear knowledge-based mean force potentials based on machine learning

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.

Engineering protein stability

This article defines protein stability, emphasizes its importance and surveys some notable recent publications (2004-2008) in the field of protein stability/stabilization. Knowledge of the factors stabilizing proteins has emerged from denaturation studies and from study of thermophilic (and other extremophilic) proteins. One can enhance stability by protein engineering strategies, the judicious use of solutes and additives, immobilization, and chemical modification in solution. General protocols are set out on how to measure the kinetic thermal stability of a given protein and how to undertake chemical modification of a protein in solution.

Optimizing structural modeling for a specific protein scaffold: knottins or inhibitor cystine knots

Background

Knottins are small, diverse and stable proteins with important drug design potential. They can be classified in 30 families which cover a wide range of sequences (1621 sequenced), three-dimensional structures (155 solved) and functions (> 10). Inter knottin similarity lies mainly between 15% and 40% sequence identity and 1.5 to 4.5 Å backbone deviations although they all share a tightly knotted disulfide core. This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines. The prediction of structural models for all knottin sequences would open new directions for the analysis of interaction sites and to provide a better understanding of the structural and functional organization of proteins sharing this scaffold.

Results

We have designed an automated modeling procedure for predicting the three-dimensionnal structure of knottins. The different steps of the homology modeling pipeline were carefully optimized relatively to a test set of knottins with known structures: template selection and alignment, extraction of structural constraints and model building, model evaluation and refinement. After optimization, the accuracy of predicted models was shown to lie between 1.50 and 1.96 Å from native structures at 50% and 10% maximum sequence identity levels, respectively. These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template. A database of 1621 structural models for all known knottin sequences was generated and is freely accessible from our web server at http://knottin.cbs.cnrs.fr. Models can also be interactively constructed from any knottin sequence using the structure prediction module Knoter1D3D available from our protein analysis toolkit PAT at http://pat.cbs.cnrs.fr.

Conclusions

This work explores different directions for a systematic homology modeling of a diverse family of protein sequences. In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.

Standardized evaluation of protein stability

We compare mean force potential values of a large series of PDB models of proteins and peptides and find that, either as monomers or polymers, proteins longer than 200-250 residues have equivalent MFP values that are averaged to -65+/-3 kcal/aa. This value is named the standard or stability value. The standard value is reached irrespective of sequences and 3D folds. Peptides are too short to follow the rule and frequently exist as populations of conformers; one exception is peptides in amyloid fibrils. Fibrils surpass the standard value in accordance with their uppermost stability. In parallel, we calculate median MFP values of amino acids in stably folded PDB models of proteins: median values vary from -25 for Gly to -115 kcal/aa for Trp. These median values are used to score primary sequences of proteins: all sequences converge to a mean value of -63.5+/-2.5 kcal/aa, i.e., only 1.5 kcal less than the folded model standard. Sequences from unfolded proteins have lower values. This supports the conclusion that sequences carry in an important message and more specifically that diversity of amino acids in sequences is mandatory for stability. We also use the median amino acid MFP to score residue stability in 3D folds. This demonstrates that 3D folds are compromises between fragments of high and fragments of low scores and that functional residues are often but not always in the extreme score values. The approach opens to possibilities of evaluating any 3D model and of detecting functional residues and should help in conducting mutation assays.

Affinity maturation generates greatly improved xyloglucan-specific carbohydrate binding modules

BACKGROUND

Molecular evolution of carbohydrate binding modules (CBM) is a new approach for the generation of glycan-specific molecular probes. To date, the possibility of performing affinity maturation on CBM has not been investigated. In this study we show that binding characteristics such as affinity can be improved for CBM generated from the CBM4-2 scaffold by using random mutagenesis in combination with phage display technology.

RESULTS

Two modified proteins with greatly improved affinity for xyloglucan, a key polysaccharide abundant in the plant kingdom crucial for providing plant support, were generated. Both improved modules differ from other existing xyloglucan probes by binding to galactose-decorated subunits of xyloglucan. The usefulness of the evolved binders was verified by staining of plant sections, where they performed better than the xyloglucan-binding module from which they had been derived. They discriminated non-fucosylated from fucosylated xyloglucan as shown by their ability to stain only the endosperm, rich in non-fucosylated xyloglucan, but not the integument rich in fucosylated xyloglucan, on tamarind seed sections.

CONCLUSION

We conclude that affinity maturation of CBM selected from molecular libraries based on the CBM4-2 scaffold is possible and has the potential to generate new analytical tools for detection of plant carbohydrates.

Four distances between pairs of amino acids provide a precise description of their interaction

The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures.

Engineering the thermostability of a TIM-barrel enzyme by rational family shuffling

A possible approach to generate enzymes with an engineered temperature optimum is to create chimeras of homologous enzymes with different temperature optima. We tested this approach using two family-10 xylanases from Thermotoga maritima: the thermophilic xylanase A catalytic domain (TmxAcat, T(opt)=68 degrees C), and the hyperthermophilic xylanase B (TmxB, T(opt)=102 degrees C). Twenty-one different chimeric constructs were created by mimicking family shuffling in a rational manner. The measured temperature optima of the 16 enzymatically active chimeras do not monotonically increase with the percentage of residues coming from TmxB. Only four chimeras had a higher temperature optimum than TmxAcat, the most stable variant (T(opt)=80 degrees C) being the one in which both terminal segments came from TmxB. Further analysis suggests that the interaction between the N- and C-terminal segments has a disproportionately high contribution to the overall thermostability. The results may be generalizable to other enzymes where the N- and C-termini are in contact.

Predicting transmembrane helix pair configurations with knowledge-based distance-dependent pair potentials

As a first step toward a novel de novo structure prediction approach for alpha-helical membrane proteins, we developed coarse-grained knowledge-based potentials to score the mutual configuration of transmembrane (TM) helices. Using a comprehensive database of 71 known membrane protein structures, pairwise potentials depending solely on amino acid types and distances between C(alpha)-atoms were derived. To evaluate the potentials, they were used as an objective function for the rigid docking of 442 TM helix pairs. This is by far the largest test data set reported to date for that purpose. After clustering 500 docking runs for each pair and considering the largest cluster, we found solutions with a root mean squared (RMS) deviation <2 A for about 30% of all helix pairs. Encouragingly, if only clusters that contain at least 20% of all decoys are considered, a success rate >71% (with a RMS deviation <2 A) is obtained. The cluster size thus serves as a measure of significance to identify good docking solutions. In a leave-one-protein-family-out cross-validation study, more than 2/3 of the helix pairs were still predicted with an RMS deviation <2.5 A (if only clusters that contain at least 20% of all decoys are considered). This demonstrates the predictive power of the potentials in general, although it is advisable to further extend the knowledge base to derive more robust potentials in the future. When compared to the scoring function of Fleishman and Ben-Tal, a comparable performance is found by our cross-validated potentials. Finally, well-predicted "anchor helix pairs" can be reliably identified for most of the proteins of the test data set. This is important for an extension of the approach towards TM helix bundles because these anchor pairs will act as "nucleation sites" to which more helices will be added subsequently, which alleviates the sampling problem.

Dissecting contact potentials for proteins: relative contributions of individual amino acids

Knowledge-based contact potentials are routinely used in fold recognition, binding of peptides to proteins, structure prediction, and coarse-grained models to probe protein folding kinetics. The dominant physical forces embodied in the contact potentials are revealed by eigenvalue analysis of the matrices, whose elements describe the strengths of interaction between amino acid side chains. We propose a general method to rank quantitatively the importance of various inter-residue interactions represented in the currently popular pair contact potentials. Eigenvalue analysis and correlation diagrams are used to rank the inter-residue pair interactions with respect to the magnitude of their relative contributions to the contact potentials. The amino acid ranking is shown to be consistent with a mean field approximation that is used to reconstruct the original contact potentials from the most relevant amino acids for several contact potentials. By providing a general, relative ranking score for amino acids, this method permits a detailed, quantitative comparison of various contact interaction schemes. For most contact potentials, between 7 and 9 amino acids of varying chemical character are needed to accurately reconstruct the full matrix. By correlating the identified important amino acid residues in contact potentials and analysis of about 7800 structural domains in the CATH database we predict that it is important to model accurately interactions between small hydrophobic residues. In addition, only potentials that take interactions involving the protein backbone into account can predict dense packing in protein structures.

Alternative non-antibody scaffolds for molecular recognition

Originally proposed one decade ago, the idea of engineering proteins outside the immunoglobulin family for novel binding functions has evolved as a powerful technology. Several classes of protein scaffolds proved to yield reagents with specificities and affinities in a range that was previously considered unique to antibodies. Such engineered protein scaffolds are usually obtained by designing a random library with mutagenesis focused at a loop region or at an otherwise permissible surface area and by selection of variants against a given target via phage display or related techniques. Whereas a plethora of protein scaffolds has meanwhile been proposed, only few of them were actually demonstrated to yield specificities towards different kinds of targets and to offer practical benefits such as robustness, smaller size, and ease of expression that justify their use as a true alternative to conventional antibodies or their recombinant fragments. Currently, the most promising scaffolds with broader applicability are protein A, the lipocalins, a fibronectin domain, an ankyrin consensus repeat domain, and thioredoxin. Corresponding binding proteins are not only of interest as research reagents or for separation in biotechnology but also as potential biopharmaceuticals, especially in the areas of cancer, autoimmune and infectious diseases as well as for in vivo diagnostics. The medical prospects have boosted high commercial expectations, and many of the promising scaffolds are under development by biotech start-up companies. Although some issues still have to be addressed, for example immunogenicity, effector functions, and plasma half-life in the context of therapeutic use or low-cost high-throughput selection for applications in proteomics research, it has become clear that scaffold-derived binding proteins will play an increasing role in biotechnology and medicine.

Affilin-novel binding molecules based on human gamma-B-crystallin, an all beta-sheet protein

The concept of novel binding proteins as an alternative to antibodies has undergone rapid development and is now ready for practical use in a wide range of applications. Alternative binding proteins, based on suitable scaffolds with desirable properties, are selected from combinatorial libraries in vitro. Here, we describe an approach using a beta-sheet of human gamma-B-crystallin to generate a universal binding site through randomization of eight solvent-exposed amino acid residues selected according to structural and sequence analyses. Specific variants, so-called Affilin, have been isolated from a phage display library against a variety of targets that differ considerably in size and structure. The isolated Affilin variants can be produced in Escherichia coli as soluble proteins and have a high level of thermodynamic stability. The crystal structures of the human wild-type gamma-B-crystallin and a selected Affilin variant have been determined to 1.7 A and 2.0 A resolution, respectively. Comparison of the two molecules indicates that the human gamma-B-crystallin tolerates amino acid exchanges with no major structural change. We conclude that the intrinsically stable and easily expressed gamma-B-crystallin provides a suitable framework for the generation of novel binding molecules.

ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins

A major problem in structural biology is the recognition of errors in experimental and theoretical models of protein structures. The ProSA program (Protein Structure Analysis) is an established tool which has a large user base and is frequently employed in the refinement and validation of experimental protein structures and in structure prediction and modeling. The analysis of protein structures is generally a difficult and cumbersome exercise. The new service presented here is a straightforward and easy to use extension of the classic ProSA program which exploits the advantages of interactive web-based applications for the display of scores and energy plots that highlight potential problems spotted in protein structures. In particular, the quality scores of a protein are displayed in the context of all known protein structures and problematic parts of a structure are shown and highlighted in a 3D molecule viewer. The service specifically addresses the needs encountered in the validation of protein structures obtained from X-ray analysis, NMR spectroscopy and theoretical calculations. ProSA-web is accessible at https://prosa.services.came.sbg.ac.at

Novel knowledge-based mean force potential at the profile level

Background

The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials.

Results

The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Φ/Ψ dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5–6%). The contact and Φ/Ψ dihedral angle potential only get a slight improvement (1–2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading.

Conclusion

The knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

Evaluation of models for the evolution of protein sequences and functions under structural constraint

In the field of evolutionary structural genomics, methods are needed to evaluate why genomes evolved to contain the fold distributions that are observed. In order to study the effects of population dynamics in the evolved genomes we need fast and accurate evolutionary models which can analyze the effects of selection, drift and fixation of a protein sequence in a population that are grounded by physical parameters governing the folding and binding properties of the sequence. In this study, various knowledge-based, force field, and statistical methods for protein folding have been evaluated with four different folds: SH2 domains, SH3 domains, Globin-like, and Flavodoxin-like, to evaluate the speed and accuracy of the energy functions. Similarly, knowledge-based and force field methods have been used to predict ligand binding specificity in SH2 domain. To demonstrate the applicability of these methods, the dynamics of evolution of new binding capabilities by an SH2 domain is demonstrated.

Structure-guided consensus approach to create a more thermostable penicillin G acylase

The thermostabilization of penicillin G acylase (PGA) is a difficult problem due to the large size of the protein and its complex maturation process. We developed a data-driven protein design method that requires fewer homologous sequences than the traditional consensus approach and utilizes structural information to limit the number of variants created. Approximately 50% of our 21 single-point mutants were found experimentally to be more thermostable than the wild-type PGA, two had almost threefold longer half-life at 50 degrees C, with very little effect on activity. An analysis of four programs that predict the thermostability conferred by point mutations shows little agreement between the programs and with the experimental data, emphasizing that the chosen stabilizing mutations are very difficult to predict, but that our data-driven design method should prove useful.

A new generation of protein display scaffolds for molecular recognition

Engineered antibodies and their fragments are invaluable tools for a vast range of biotechnological and pharmaceutical applications. However, they are facing increasing competition from a new generation of protein display scaffolds, specifically selected for binding virtually any target. Some of them have already entered clinical trials. Most of these nonimmunoglobulin proteins are involved in natural binding events and have amazingly diverse origins, frameworks, and functions, including even intrinsic enzyme activity. In many respects, they are superior over antibody-derived affinity molecules and offer an ever-extending arsenal of tools for, e.g., affinity purification, protein microarray technology, bioimaging, enzyme inhibition, and potential drug delivery. As excellent supporting frameworks for the presentation of polypeptide libraries, they can be subjected to powerful in vitro or in vivo selection and evolution strategies, enabling the isolation of high-affinity binding reagents. This article reviews the generation of these novel binding reagents, describing validated and advanced alternative scaffolds as well as the most recent nonimmunoglobulin libraries. Characteristics of these protein scaffolds in terms of structural stability, tolerance to multiple substitutions, ease of expression, and subsequent applications as specific targeting molecules are discussed. Furthermore, this review shows the close linkage between these novel protein tools and the constantly developing display, selection, and evolution strategies using phage display, ribosome display, mRNA display, cell surface display, or IVC (in vitro compartmentalization). Here, we predict the important role of these novel binding reagents as a toolkit for biotechnological and biomedical applications.

Strategies and computational tools for improving randomized protein libraries

In the last decade, directed evolution has become a routine approach for engineering proteins with novel or altered properties. Concurrently, a trend away from purely 'blind' randomization strategies and towards more 'semi-rational' approaches has also become apparent. In this review, we discuss ways in which structural information and predictive computational tools are playing an increasingly important role in guiding the design of randomized libraries: web servers such as ConSurf-HSSP and SCHEMA allow the prediction of sites to target for producing functional variants, while algorithms such as GLUE, PEDEL and DRIVeR are useful for estimating library completeness and diversity. In addition, we review recent methodological developments that facilitate the construction of unbiased libraries, which are inherently more diverse than biased libraries and therefore more likely to yield improved variants.