Designed Heme-Cage β-Sheet Miniproteins

The structure and function of naturally occurring proteins are governed by a large number of amino acids (≥100). The design of miniature proteins with desired structures and functions not only substantiates our knowledge about proteins but can also contribute to the development of novel applications. Excellent progress has been made towards the design of helical proteins with diverse functions. However, the development of functional β-sheet proteins remains challenging. Herein, we describe the construction and characterization of four-stranded β-sheet miniproteins made up of about 19 amino acids that bind heme inside a hydrophobic binding pocket or “heme cage” by bis-histidine coordination in an aqueous environment. The designed miniproteins bound to heme with high affinity comparable to that of native heme proteins. Atomic-resolution structures confirmed the presence of a four-stranded β-sheet fold. The heme–protein complexes also exhibited high stability against thermal and chaotrope-induced unfolding.     

Carbon monoxide binding by de novo heme proteins derived from designed combinatorial libraries.

Carbon monoxide binding was studied in a collection of de novo heme proteins derived from combinatorial libraries of sequences designed to fold into 4-helix bundles. The design of the de novo sequences was based on the previously reported "binary code" strategy, in which the patterning of polar and nonpolar amino acids is specified explicitly, but the exact identities of the side chains are varied extensively.(1) The combinatorial mixture of amino acids included histidine and methionine, which ligate heme iron in natural proteins. However, no attempt was made to explicitly design a heme binding site. Nonetheless, as reported previously, approximately half of the binary code proteins bind heme.(2) This collection of novel heme proteins provides a unique opportunity for an unbiased assessment of the functional potentialities of heme proteins that have not been prejudiced either by explicit design or by evolutionary selection. To assess the capabilities of the de novo heme proteins to bind diatomic ligands, we measured the affinity for CO, the kinetics of CO binding and release, and the resonance Raman spectra of the CO complexes for eight de novo heme proteins from two combinatorial libraries. The CO binding affinities for all eight proteins were similar to that of myoglobin, with dissociation constants (K(d)) in the low nanomolar range. The CO association kinetics (k(on)) revealed that the heme environment in all eight of the de novo proteins is partially buried, and the resonance Raman studies indicated that the local environment around the bound CO is devoid of hydrogen-bonding groups. Overall, the CO binding properties of the de novo heme proteins span a narrow range of values near the center of the range observed for diverse families of natural heme proteins. The measured properties of the de novo heme proteins can be considered as a "default" range for CO binding in alpha-helical proteins that have neither been designed to bind heme or CO, nor subjected to genetic selections for heme or CO binding.     

A statistical model for predicting protein folding rates from amino acid sequence with structural class information

Prediction of protein folding rates from amino acid sequences is one of the most important challenges in molecular biology. In this work, I have related the protein folding rates with physical-chemical, energetic and conformational properties of amino acid residues. I found that the classification of proteins into different structural classes shows an excellent correlation between amino acid properties and folding rates of two- and three-state proteins, indicating the importance of native state topology in determining the protein folding rates. I have formulated a simple linear regression model for predicting the protein folding rates from amino acid sequences along with structural class information and obtained an excellent agreement between predicted and experimentally observed folding rates of proteins; the correlation coefficients are 0.99, 0.96 and 0.95, respectively, for all-alpha, all-beta and mixed class proteins. This is the first available method, which is capable of predicting the protein folding rates just from the amino acid sequence with the aid of generic amino acid properties and structural class information.     

Fusion proteins from artificial and natural structural modules

The purpose of preparing fusion proteins from designed and natural sequences is mainly twofold; it aims at the stabilization of structure and at the modification of biological activity. Fusion with beta-galactosidase, for example, can increase the intracellular stability and DDT-degrading activity of an artificial DDT-binding peptide, and fusions with a leucine zipper produce mono- and bifunctional single-chain variable domain antibody fragments or homodimeric and heterodimeric DNA-binding proteins like an artificial homodimeric HIV-1 enhancer-binding protein with increased binding specificity and repressor activity. Of importance are also short leader sequences that mediate the translocation of proteins across the cytoplasmic and the nuclear membrane. An interesting by-product of the leucine zipper-mediated dimerization of an HIV-1 enhancer-binding protein was the synthesis and the structural as well as functional characterization of a retro-leucine zipper.     

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {a_k} and {b_l} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.     

A statistical method for predicting protein unfolding rates from amino acid sequence

The prediction of protein unfolding rates from amino acid sequences is one of the most important challenges in computational biology and chemistry. The analysis on the relationship between protein unfolding rates and physical-chemical, energetic, and conformational properties of amino acid residues provides valuable information to understand and predict the unfolding rates of two- and three-state proteins. We found that the classification of proteins into different structural classes shows an excellent correlation between amino acid properties and unfolding rates of two- and three-state proteins, indicating the importance of native-state topology in determining the protein unfolding rates. We have formulated three independent linear regression equations to different structural classes of proteins for predicting their unfolding rates from amino acid sequences and obtained an excellent agreement between predicted and experimentally observed unfolding rates of proteins; the correlation coefficients are 0.999, 0.990, and 0.992, respectively, for all-alpha, all-beta, and mixed-class proteins. Further, we have derived a general equation applicable to all structural classes of proteins, which can be used for predicting the unfolding rates for proteins of an unknown structural class. We observed a correlation of 0.987 and 0.930, respectively, for back-check and jack-knife tests. These accuracy levels are better than those of other methods in the literature.     

ProSeg: a database of local structures of protein segments

Integration of knowledge on the sequence-structure correlation of proteins provides a basis for the structural design of artificial novel proteins. As one of strategies, it is effective to consider a short segment, whose size is in between an amino acid and a domain, as a correlation unit for exploring the structure-to-sequence relationship. Here we report the development of a database called ProSeg, which consists of two sub-databases, Segment DB and Cluster DB. Segment DB contains tens of thousands of segments that were prepared by dividing the primary sequences of 370 proteins using a sliding L-residue window (L = 5, 9, 11, 15). These segments were classified into several thousands of clusters according to their three-dimensional structural resemblance. Cluster DB contains much cluster-related information, which includes image, rank, frequency, secondary structure assignment, sequence profile, etc. Users can search for a suitable cluster by inputting an appropriate parameter (i.e., PDB ID, dihedral angles, or DSSP symbols), which identifies the backbone structure of a query segment. Analogous to a language, ProSeg could be regarded as a ‘structure-sequence dictionary’ that contains over 10,000 ‘protein words’. ProSeg is freely accessible through the Internet ().     

Why similar protein sequences encode similar three-dimensional structures?

Evolutionarily related proteins have similar sequences. Such similarity is called homology and can be described using substitution matrices such as Blosum 60. Naturally occurring homologous proteins usually have similar stable tertiary structures and this fact is used in so-called homology modeling. In contrast, the artificial protein designed by the Regan group has 50% identical sequence to the B1 domain of Streptococcal IgG-binding protein and a structure similar to the protein Rop. In this study, we asked the question whether artificial similar protein sequences (pseudohomologs) tend to encode similar protein structures, such as proteins existing in nature. To answer this question, we designed sets of protein sequences (pseudohomologs) homologous to sequences having known three-dimensional structures (template structures), same number of identities, same composition and equal level of homology, according to Blosum 60 substitution matrix as the known natural homolog. We compared the structural features of homologs and pseudohomologs by fitting them to the template structure. The quality of such structures was evaluated by threading potentials. The packing quality was measured using three-dimensional homology models. The packing quality of the models was worse for the “pseudohomologs” than for real homologs. The native homologs have better threading potentials (indicating better sequence-structure fit) in the native structure than the designed sequences. Therefore, we have shown that threading potentials and proper packing are evolutionarily more strongly conserved than sequence homology measured using the Blosum 60 matrix. Our results indicate that three-dimensional protein structure is evolutionarily more conserved than expected due to sequence conservation.     

Prediction of protein structural classes by a new measure of information discrepancy

Since it was observed that the structural class of a protein is related to its amino acid composition, various methods based on amino acid composition have been proposed to predict protein structural classes. Though those methods are effective to some degree, their predictive quality is confined because amino acid composition cannot sufficiently include the information of protein sequences. In this paper, a measure of information discrepancy is applied to the prediction of protein structural classes; different from the previous methods, this new approach is based on the comparisons of subsequence distributions; therefore, the effect of residue order on protein structure is taken into account. The predictive results of the new approach on the same data set are better than those of the previous methods. As to a data set of 1401 sequences with no more than 30% redundancy, the overall correctness rates of resubstitution test and Jackknife test are 99.4 and 75.02%, respectively, and to other data sets the similar results are also obtained. All tests demonstrate that the residue order along protein sequences plays an important role on recognition of protein structural classes, especially for alpha/beta proteins and alpha+beta proteins. In addition, the tests also show that the new method is simple and efficient.     

Protein-based materials,toward a new level of structural control

Through billions of years of evolution nature has created and refined structural proteins for a wide variety of specific purposes. Amino acid sequences and their associated folding patterns combine to create elastic, rigid or tough materials. In many respects, nature's intricately designed products provide challenging examples for materials scientists, but translation of natural structural concepts into bio-inspired materials requires a level of control of macromolecular architecture far higher than that afforded by conventional polymerization processes. An increasingly important approach to this problem has been to use biological systems for production of materials. Through protein engineering, artificial genes can be developed that encode protein-based materials with desired features. Structural elements found in nature, such as beta-sheets and alpha-helices, can be combined with great flexibility, and can be outfitted with functional elements such as cell binding sites or enzymatic domains. The possibility of incorporating non-natural amino acids increases the versatility of protein engineering still further. It is expected that such methods will have large impact in the field of materials science, and especially in biomedical materials science, in the future.     

Letter to the Editor

A new two-dimensional graphical representation of protein sequences is introduced. Twenty concentric evenly spaced circles divided by n radial lines into equal divisions are selected to represent any protein sequence of length n. Each circle represents one of the different 20 amino acids, and each radial line represents a single amino acid of the protein sequence. An efficient numerical method based on the graph is proposed to measure the similarity between two protein sequences. To prove the accuracy of our approach, the method is applied to NADH dehydrogenase subunit 5 (ND5) proteins of nine different species and 24 transferrin sequences from vertebrates. High values of correlation coefficient between our results and the results of ClustalW are obtained (approximately perfect correlations). These values are higher than the values obtained in many other related works.     

A new protein graph model for function prediction

As several structural proteomic projects are producing an increasing number of protein structures with unknown function, methods that can reliably predict protein functions from protein structures are in urgent need. In this paper, we present a method to explore the clustering patterns of amino acids on the 3-dimensional space for protein function prediction. First, amino acid residues on a protein structure are clustered into spatial groups using hierarchical agglomerative clustering, based on the distance between them. Second, the protein structure is represented using a graph, where each node denotes a cluster of amino acids. The nodes are labeled with an evolutionary profile derived from the multiple alignment of homologous sequences. Then, a shortest-path graph kernel is used to calculate similarities between the graphs. Finally, a support vector machine using this graph kernel is used to train classifiers for protein function prediction. We applied the proposed method to two separate problems, namely, prediction of enzymes and prediction of DNA-binding proteins. In both cases, the results showed that the proposed method outperformed other state-of-the-art methods.     

Direct meso‐Alkynylation of Metalloporphyrins Through Gold Catalysis for Hemoprotein Engineering

A method was developed for the direct functionalization of metalloporphyrins at the methine protons (meso positions) to yield asymmetric alkynylated derivatives by using gold catalysis and hypervalent iodine reagents. This single‐step procedure was applied to b‐type heme and the product was incorporated into a gas‐sensor heme protein. The terminal alkyne allows fluorophore labeling through copper(I)‐catalyzed azide–alkyne cycloaddition (CuAAC). Hemoproteins with this type of engineered cofactor have several potential applications in labeling and imaging technologies. Additionally, the alkyne provides a handle for modulating porphyrin electron density, which affects cofactor redox potential and ligand affinity. This method will be helpful for investigating the chemistry of natural heme proteins and for designing artificial variants with altered properties and reactivities.     

Water in Ras Superfamily Evolution

The Ras GTPase superfamily of proteins coordinates a diverse set of cellular outcomes, including cell morphology, vesicle transport, and cell proliferation. Primary amino acid sequence analysis has identified Specificity determinant positions (SDPs) that drive diversified functions specific to the Ras, Rho, Rab, and Arf subfamilies (Rojas et al. 2012, J Cell Biol 196 :189–201). The inclusion of water molecules in structural and functional adaptation is likely to be a major response to the selection pressures that drive evolution, yet hydration patterns are not included in phylogenetic analysis. This article shows that conserved crystallographic water molecules coevolved with SDP residues in the differentiation of proteins within the Ras superfamily of small GTPases. The patterns of water conservation between protein subfamilies parallel those of sequence-based evolutionary trees. Thus, hydration patterns have the potential to help elucidate functional significance in the evolution of amino acid residues observed in phylogenetic analysis of homologous proteins. © 2019 Wiley Periodicals, Inc.     

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering,and structural motif identification and discovery of protein kinases,hydrolases, and SARS-CoV-2’s protein via the application of amino acid grouping

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.     

Templated biological synthesis of polymers of abiological monomers

A general method for the in vivo incorporation of amino acid analogues into artificial proteins is described. The method involves the construction of an artificial gene encoding the sequence of interest (with the corresponding natural amino acid encoded in place of the analogue), transformation of a bacterial host strain that cannot synthesize the natural amino acid, and induction of protein synthesis in a host culture enriched in the analogue. Results are described for the amino acid analogues selenomethionine, p-fluorophenylalanine, trifluoroleucine and 3-thienylalanine.     

Organic Ligand Binding by a Hydrophobic Cavity in a Designed Tetrameric Coiled‐Coil Protein

The design and characterization of a hydrophobic cavity in de novo designed proteins provides a wide range of information about the functions of de novo proteins. We designed a de novo tetrameric coiled‐coil protein with a hydrophobic pocketlike cavity. Tetrameric coiled coils with hydrophobic cavities have previously been reported. By replacing one Leu residue at the a position with Ala, hydrophobic cavities that did not flatten out due to loose peptide chains were reliably created. To perform a detailed examination of the ligand‐binding characteristics of the cavities, we originally designed two other coiled‐coil proteins: AM2, with eight Ala substitutions at the adjacent a and d positions at the center of a bundled structure, and AM2W, with one Trp and seven Ala substitutions at the same positions. To increase the association of the helical peptides, each helical peptide was connected with flexible linkers, which resulted in a single peptide chain. These proteins exhibited CD spectra corresponding to superhelical structures, despite weakened hydrophobic packing. AM2W exhibited binding affinity for size‐complementary organic compounds. The dissociation constants, K_d, of AM2W were 220 nM for adamantane, 81 μM for 1‐adamantanol, and 294 μM for 1‐adamantaneacetic acid, as measured by fluorescence titration analyses. Although it was contrary to expectations, AM2 did not exhibit any binding affinity, probably due to structural defects around the designed hydrophobic cavity. Interestingly, AM2W exhibited incremental structure stability through ligand binding. Plugging of structural defects with organic ligands would be expected to facilitate protein folding.     

Extracellular oxidases purified fromCoriolus versicolor

Studies on the extracellular enzymes ofCoriolus versicolor have resulted in the isolation and purification of several proteins that have the potential to act as redox enzymes.C. versicolor was cultured on a glucose-amino acid medium in a large-scale fermenter (60 L) with 2,5-xylidine added to induce the production of extracellular laccase. Proteins were precipitated from the growth medium with ammonium sulfate, and separated by ion-exchange chromatography on DEAE-Sephadex. Further separation of glycoproteins was achieved by affinity chromatography on Concanavalin-A-Sepharose. Polyacrylamide gel electrophoresis on SDS (sodium dodecyl sulfate) and LDS (lithium dodecyl sulfate) gels, isoelectric focusing, and chromatofocusing have been used to establish purity of the proteins and their isoelectric points. Laccase B has been isolated and separated into five fractions by chromatofocusing, with isoelectric points of the fractions varying between pH 4.5 and 6.5. The relative specificity of these fractions towards monophenolic and diphenolic substrates has been investigated. Laccase A was found to differ from laccase B in showing only two bands on isoelectric focusing, with isoelectric points between pH 3.0 and 3.5. Two other proteins isolated from the growth medium were both hemecontaining proteins with interesting spectral properties. One was a “peroxidasetype” heme that could bind carbon monoxide to the iron in the heme, suggesting that the heme may bind oxygen and so function as an oxidase. It reacted with hydrogen peroxide to liberate hydroxyl radicals, but this reaction with hydrogen peroxide resulted in the destruction of the heme center. The real role of this protein is unclear, but several possibilities will be investigated. The second heme-containing protein isolated had different spectral properties from the “peroxidase-type” heme previously described. It had spectral characteristics of a b-type cytochrome in association with a flavin prosthetic group. It appeared to have some similarities to cellobiose oxidase, a heme flavoprotein previously isolated fromSporotrichum pulverulentum, although its molecular weight was 50,100 daltons compared with the 93,000 reported for cellobiose oxidase. Further characterization of this protein will be described.     

STRUCTURE OF THE MITOCHONDRIAL URFA6L GENE AND tRNA~(Lys) GENE FROM CARP

The carp mitochondrial URFA6L gene consists of 165 base pairs. The overall structural organization of the gene is very similar to that of the Xenopus URFA6L gene. Their nucleotide sequences exhibit 68% homology. The carp URFA6L gene encodes a protein of 54 amino acids. The amino acid composition of the protein is unusual because almost half of the residues consist of 5 hydrophobic amino acids(proline, tryptophan, leueine, isoleueine and tyrosine). A comparison between the amino acid sequences of 5 vertebrate URFA6L proteins and the yeast ATPase8 showed that they have weak but very important common structural features, suggesting that the vertebrate URFA6L proteins may function asATPase8. The nucleotide sequence of the lysine tRNA gene from carp has been determined and represented in cloverleaf secondary structure. Similar to amphibian and mammalian mitochondrial tRNA~(Lys) genes, the carp mitochondrial tRNA~(Tys) gene also has some unusual structural features as compared with its cytoplasmic counterpart