Constraining protein sequence space: four amino acid alphabets are sufficient to recapitulate lambda repressor multimerization

Nucleic acid polymers selected from random sequence space constitute an enormous array of catalytic, diagnostic and therapeutic molecules. Despite the fact that proteins are robust polymers with far greater chemical and physical diversity, success in unlocking protein sequence space remains elusive. We have devised a combinatorial strategy for accessing nucleic acid sequence space corresponding to proteins comprising selected amino acid alphabets. Using the SynthOMIC approach (synthesis of ORFs by multimerizing in-frame codons), representative libraries comprising four amino acid alphabets were fused in-frame to the lambda repressor DNA-binding domain to provide an in vivo selection for self-interacting proteins that re-constitute lambda repressor function. The frequency of self-interactors as a function of amino acid composition ranged over five orders of magnitude, from ∼6% of clones in a library comprising the amino acid residues LARE to ∼0.6 in 10⁶ in the MASH library. Sequence motifs were evident by inspection in many cases, and individual clones from each library presented substantial sequence identity with translated proteins by BLAST analysis. We posit that the SynthOMIC approach represents a powerful strategy for creating combinatorial libraries of open reading frames that distils protein sequence space on the basis of three inherent properties: it supports the use of selected amino acid alphabets, eliminates redundant sequences and locally constrains amino acids.     

Combinatorial approaches to probe the sequence determinants of protein aggregation and amyloidogenicity

Elucidation of the molecular determinants that drive proteins to aggregate is important both to advance our fundamental understanding of protein folding and misfolding, and as a step towards successful intervention in human disease. Combinatorial strategies enable unbiased and model-free approaches to probe sequence/structure relationships. Through the use of combinatorial methods, it is possible (i) to probe the sequence determinants of natural amyloid proteins by screening libraries of amino acid substitutions (mutations) to identify those that prevent amyloid formation; and (ii) to test new hypotheses about the mechanism of formation of amyloid fibrils by using these hypotheses to guide the design of combinatorial libraries of de novo amyloid-like proteins. Here, we review how these two approaches have been used to study the molecular determinants of protein aggregation and amyloidogenicity.     

Unnatural RNA display libraries

Combinatorial peptide and protein libraries have now been developed to accommodate unnatural amino acids in a genetically encoded format via in vitro nonsense and sense suppression. General translation features and specific regioselective and stereoselective properties of the ribosome endow these libraries with a broad chemical diversity. Alternatively, amino acid residues can be chemically derivatized post-translationally to add preferred functionality to the encoded peptide. All of these efforts are advancing combinatorial peptide and protein libraries for enhanced ligands against biological targets of interest.     

Expression,purification, and characterization of proteins from high-quality combinatorial libraries of the mammalian calmodulin central linker

Combinatorial libraries offer an attractive approach towards exploring protein sequence, structure and function. Although several strategies introduce sequence diversity, the likelihood of identifying proteins with novel functions is increased when the library of genes encodes for folded and soluble structures. Here we present the first application of the binary patterning approach of combinatorial protein library design to the unique central linker region of the highly-conserved protein, calmodulin (CaM). We show that this high-quality approach translates very well to the CaM protein scaffold: All library members over-express and are functionally diverse, having a range of conformations in the presence and absence of calcium as determined by circular dichroism spectroscopy. Collectively, these data support that the binary patterning approach, when applied to the highly-conserved protein fold, can yield large collections of folded, soluble and highly-expressible proteins.     

Protein map: an alignment-free sequence comparison method based on various properties of amino acids

In this paper, we propose a new protein map which incorporates with various properties of amino acids. As a powerful tool for protein classification, this new protein map both considers phylogenetic factors arising from amino acid mutations and provides computational efficiency for the huge amount of data. The ten amino acid physico-chemical properties (the chemical composition of the side chain, two polarity measures, hydropathy, isoelectric point, volume, aromaticity, aliphaticity, hydrogenation, and hydroxythiolation) are utilized according to their relative importance. Moreover, during the course of calculation of genetic distances between pairs of proteins, this approach does not require any alignment of sequences. Therefore, the proposed model is easier and quicker in handling protein sequences than multiple alignment methods, and gives protein classification greater evolutionary significance at the amino acid sequence level.     

Protein synthesis rhythm.

The three-dimensional structure of a protein molecule appears to depend on the amino acid sequence of the protein in an as yet incompletely described manner. If the amino acid sequence is replaced by a numerical sequence of values representing a physical or chemical property of amino acids, the resulting numerical sequence is amenable to autocorrelation analysis. Further, if certain geometrical parameters are calculated from the three-dimensional structure of a protein to form a configurational series, pairs of property series and configurational series can be analyzed by cross-correlation techniques. The data base for the analysis was the three-dimensional structures of ten proteins as determined by X-ray crystallography. Such analysis yields the result that the hydrophobicity of an amino acid residue in a protein influences the orientation angle of the amino acid side chain. This result is consistent with the widely current “oil-drop” model of protein structure. Hydrophobicity also appears to influence the backbone dihedral angle φ, but not ψ Such a directional effect cannot be explained by a current model of information transfer in protein helices. The magnitude of the cross correlations does not appear to be satisfactory for construction of a transfer function model for the prediction of general features of protein structure from amino acid sequences.     

Serum protein profiling by solid phase extraction and mass spectrometry: A future diagnostics tool?

Serum protein profiling by MS is a promising method for early detection of disease. Important characteristics for serum protein profiling are preanalytical factors, analytical reproducibility and high throughput. Problems related to preanalytical factors can be overcome by using standardized and rigorous sample collection and sample handling protocols. The sensitivity of the MS analysis relies on the quality of the sample; consequently, the blood sample preparation step is crucial to obtain pure and concentrated samples and enrichment of the proteins and peptides of interest. This review focuses on the serum sample preparation step prior to protein profiling by MALDI MS analysis, with particular focus on various SPE methods. The application of SPE techniques with different chromatographic properties such as RP, ion exchange, or affinity binding to isolate specific subsets of molecules (subproteomes) is advantageous for increasing resolution and sensitivity in the subsequent MS analysis. In addition, several of the SPE sample preparation methods are simple and scalable and have proven easy to automate for higher reproducibility and throughput, which is important in a clinical proteomics setting.     

A de novo designed protein protein interface

As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid beta1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest ( approximately 300 microM), 2D-[(1)H,(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation.     

Biogenesis,quality control,and structural dynamics of proteins as explored in living cells via site‐directed photocrosslinking

Protein biogenesis and quality control are essential to maintaining a functional pool of proteins and involve numerous protein factors that dynamically and transiently interact with each other and with the substrate proteins in living cells. Conventional methods are hardly effective for studying dynamic, transient, and weak protein–protein interactions that occur in cells. Herein, we review how the site‐directed photocrosslinking approach, which relies on the genetic incorporation of a photoreactive unnatural amino acid into a protein of interest at selected individual amino acid residue positions and the covalent trapping of the interacting proteins upon ultraviolent irradiation, has become a highly efficient way to explore the aspects of protein contacts in living cells. For example, in the past decade, this approach has allowed the profiling of the in vivo substrate proteins of chaperones or proteases under both physiologically optimal and stressful (e.g., acidic) conditions, mapping residues located at protein interfaces, identifying new protein factors involved in the biogenesis of membrane proteins, trapping transiently formed protein complexes, and snapshotting different structural states of a protein. We anticipate that the site‐directed photocrosslinking approach will play a fundamental role in dissecting the detailed mechanisms of protein biogenesis, quality control, and dynamics in the future.     

Peptidomics of the larval Drosophila melanogaster central nervous system

Neuropeptides regulate most, if not all, biological processes in the animal kingdom, but only seven have been isolated and sequenced from Drosophila melanogaster. In analogy with the proteomics technology, where all proteins expressed in a cell or tissue are analyzed, the peptidomics approach aims at the simultaneous identification of the whole peptidome of a cell or tissue, i.e. all expressed peptides with their posttranslational modifications. Using nanoscale liquid chromatography combined with tandem mass spectrometry and data base mining, we analyzed the peptidome of the larval Drosophila central nervous system at the amino acid sequence level. We were able to provide biochemical evidence for the presence of 28 neuropeptides using an extract of only 50 larval Drosophila central nervous systems. Eighteen of these peptides are encoded in previously cloned or annotated precursor genes, although not all of them were predicted correctly. Eleven of these peptides were never purified before. Eight other peptides are entirely novel and are encoded in five different, not yet annotated genes. This neuropeptide expression profiling study also opens perspectives for other eukaryotic model systems, for which genome projects are completed or in progress.     

FORESST: fold recognition from secondary structure predictions of proteins

MOTIVATION: A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best. RESULTS: After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence. AVAILABILITY: FORESST web server at http://absalpha.dcrt.nih.gov:8008/ for the library of HMMs of structural families used in this paper. FORESST web server at http://www.tigr.org/ for a more extensive library of HMMs (work in progress). CONTACT: valedf@tigr.org; munson@helix.nih.gov; garnier@helix.nih.gov     

Understanding mechanisms governing protein-protein interactions from synthetic binding interfaces

Recent advances in methodologies and design of combinatorial library selection have enabled comprehensive characterization of sequence space for protein-protein interaction interfaces and generation of fully synthetic binding interfaces. By exhaustively introducing and quantitatively analyzing mutations in natural interfaces, new insights into their molecular architecture and plasticity have emerged. Minimalist combinatorial libraries based on a restricted amino acid code have produced synthetic interfaces that rival natural ones using a different set of rules. A two amino acid code composed of just tyrosine and serine in the context of antibody CDR loops is sufficient to produce high affinity and specific interactions with different classes of protein targets. Structural analyses highlight the dominant role of Tyr in forming productive interactions and demonstrate the dominance of conformational diversity over chemical diversity in producing na?ve binding interfaces. Synthetic binding proteins are beginning to be used as a powerful crystallization tool to attack important structural biology problems that are recalcitrant to crystallization using traditional methods.     

In Vacuo Esterification of Carboxyl Groups in Lyophilized Proteins

A new method is described for the esterification of carboxyl groups in proteins by reaction of the lyophilized protein in vacuo with gaseous alcohol and HCI catalyst. Carboxyl groups are rapidly esterified with no protein degradation. 13C-Methyl or 13C-ethyl esters of the alpha-, gamma- and delta-carboxyl groups could be distinguished by the distinct chemical shifts of their resonances. Within the class of gamma- or delta-esters, the chemical shifts have little variation; however, the chemical shift of a C-terminal esterified alpha-carboxyl group shows a strong dependence on the nature of the C-terminal amino acid and sequence. Iodomethane reacts with deprotonated carboxyl groups in lyophilized proteins to form methyl esters, but unlike the reaction with gaseous methanol/HCI, it does not selectively methylate carboxyl groups. The procedure permits the cost-effective incorporation of isotopic labels and provides a new approach using 13C-NMR spectroscopy for determining the number of different C-termini present in a protein preparation.     

Bioactive peptide design using the Resonant Recognition Model

With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) [1, 2] is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM [1, 2] is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists [2, 3] and human immunodeficiency virus (HIV) envelope agonists [2, 4], such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here.     

Improved modeling of side-chain--base interactions and plasticity in protein--DNA interface design

Combinatorial sequence optimization for protein design requires libraries of discrete side-chain conformations. The discreteness of these libraries is problematic, particularly for long, polar side chains, since favorable interactions can be missed. Previously, an approach to loop remodeling where protein backbone movement is directed by side-chain rotamers predicted to form interactions previously observed in native complexes (termed "motifs") was described. Here, we show how such motif libraries can be incorporated into combinatorial sequence optimization protocols and improve native complex recapitulation. Guided by the motif rotamer searches, we made improvements to the underlying energy function, increasing recapitulation of native interactions. To further test the methods, we carried out a comprehensive experimental scan of amino acid preferences in the I-AniI protein-DNA interface and found that many positions tolerated multiple amino acids. This sequence plasticity is not observed in the computational results because of the fixed-backbone approximation of the model. We improved modeling of this diversity by introducing DNA flexibility and reducing the convergence of the simulated annealing algorithm that drives the design process. In addition to serving as a benchmark, this extensive experimental data set provides insight into the types of interactions essential to maintain the function of this potential gene therapy reagent.     

An Optimal Structure-Discriminative Amino Acid Index for Protein Fold Recognition

Identifying the fold class of a protein sequence of unknown structure is a fundamental problem in modern biology. We apply a supervised learning algorithm to the classification of protein sequences with low sequence identity from a library of 174 structural classes created with the Combinatorial Extension structural alignment methodology. A class of rules is considered that assigns test sequences to structural classes based on the closest match of an amino acid index profile of the test sequence to a profile centroid for each class. A mathematical optimization procedure is applied to determine an amino acid index of maximal structural discriminatory power by maximizing the ratio of between-class to within-class profile variation. The optimal index is computed as the solution to a generalized eigenvalue problem, and its performance for fold classification is compared to that of other published indices. The optimal index has significantly more structural discriminatory power than all currently known indices, including average surrounding hydrophobicity, which it most closely resembles. It demonstrates >70% classification accuracy over all folds and nearly 100% accuracy on several folds with distinctive conserved structural features. Finally, there is a compelling universality to the optimal index in that it does not appear to depend strongly on the specific structural classes used in its computation.     

Peptidomics in Drosophila melanogaster.

In analogy with proteomics technology, where all proteins expressed in a cell or tissue are analysed, the peptidomic approach aims at the simultaneous visualisation and identification of the whole peptidome of a cell or tissue, ie all expressed peptides with their post-translational modifications. With nanoscale liquid chromatography (nanoLC), combined with mass spectrometry and subsequent database searching, the peptidome of the Drosophila larval brain has been identified at the amino acid sequence level. In a single experiment involving only 50 Drosophila larval brains, one can obtain a display of the expressed peptides. In this paper, current peptidomics technology will be explained, using Drosophila as an example. Compared with the 400,000 Drosophila whole bodies that were required as a starting material for traditional biochemical peptide purification rounds, the authors are convinced that peptidomics technology, which in the future will certainly be applied to the analysis of different physiological states, has the inherent potential to bring about a true revolution in the study of the molecular physiology of Drosophila.     

A novel four-dimensional strategy combining protein and peptide separation methods enables detection of low-abundance proteins in human plasma and serum proteomes

A novel strategy, termed protein array pixelation, is described for comprehensive profiling of human plasma and serum proteomes. This strategy consists of three sequential high-resolution protein prefractionation methods (major protein depletion, solution isoelectrofocusing, and 1-DE) followed by nanocapillary RP tryptic peptide separation prior to MS/MS analysis. The analysis generates a 2-D protein array where each pixel in the array contains a group of proteins with known pI and molecular weight range. Analysis of the HUPO samples using this strategy resulted in 575 and 2890 protein identifications from plasma and serum, respectively, based on HUPO-approved criteria for high-confidence protein assignments. Most importantly, a substantial number of low-abundance proteins (low ng/mL - pg/mL range) were identified. Although larger volumes were used in initial prefractionation steps, the protein identifications were derived from fractions equivalent to approximately 0.6 microL (45 microg) of plasma and 2.4 microL (204 microg) of serum. The time required for analyzing the entire protein array for each sample is comparable to some published shotgun analyses of plasma and serum proteomes. Therefore, protein array pixelation is a highly sensitive method capable of detecting proteins differing in abundance by up to nine orders of magnitude. With further refinement, this method has the potential for even higher capacity and higher throughput.     

Physical and chemical properties of microvitellogenin. A protein from the egg of the tobacco hornworm moth, Manduca sexta

Microvitellogenin belongs to a new class of low molecular weight female-specific proteins in insects. The protein is found in the hemolymph (blood) and egg of the tobacco hornworm, Manduca sexta. The isolation of microvitellogenin has been achieved by a combination of gel permeation, cation-exchange, and adsorption chromatographic steps. Microvitellogenin is synthesized by the fat body and appears in the hemolymph 17 days before adult emergence, or 16 days before the onset of egg development. The protein is sequestered from the hemolymph into the egg where it accumulates to a relatively high concentration. The proteins isolated from the hemolymph and the egg are identical in their molecular weight, amino acid compositions, isoelectric points, circular dichroic spectra, immunological properties, and NH2-terminal amino acid sequence. Thus, microvitellogenin does not seem to undergo any modifications before or after it is sequestered in the egg. In solution, the protein exists in a monomeric form and has a secondary structure composed of approximately 38% alpha-helix, as estimated by CD analysis. The CD spectrum of microvitellogenin is unusual in that it has a strong positive band between 220 and 240 nm that may be due to contributions from the aromatic amino acid residues. Unlike the major egg yolk protein of insects, vitellogenin, microvitellogenin does not contain measurable carbohydrate or lipid, and has no immunological, chemical, or physical similarities to vitellogenin. The amino acid composition of microvitellogenin is low in cysteine, but is rich in aspartate. The sex specificity of the protein and its accumulation in the egg justifies the name microvitellogenin, first given to an analogous protein in the egg of the giant silkmoth, Hyalophora cecropia.