Reduced Amino Acid Alphabet Research Articles

Assembly of Designed Oligonucleotides: a useful tool in synthetic biology for creating high-quality combinatorial DNA libraries.

The method dubbed Assembly of Designed Oligonucleotides (ADO) is a powerful tool in synthetic biology to create combinatorial DNA libraries for gene, protein, metabolic, and genome engineering. In directed evolution of proteins, ADO benefits from using reduced amino acid alphabets for saturation mutagenesis and/or DNA shuffling, but all 20 canonical amino acids can be also used as building blocks. ADO is performed in a two-step reaction. The first involves a primer-free, polymerase cycling assembly or overlap extension PCR step using carefully designed overlapping oligonucleotides. The second step is a PCR amplification using the outer primers, resulting in a high-quality and bias-free double-stranded DNA library that can be assembled with other gene fragments and/or cloned into a suitable plasmid subsequently. The protocol can be performed in a few hours. In theory, neither the length of the DNA library nor the number of DNA changes has any limits. Furthermore, with the costs of synthetic DNA dropping every year, after an initial investment is made in the oligonucleotides, these can be exchanged for alternative ones with different sequences at any point in the process, fully exploiting the potential of creating highly diverse combinatorial libraries. In the example chosen here, we show the construction of a high-quality combinatorial ADO library targeting sixteen different codons simultaneously with nonredundant degenerate codons encoding various reduced alphabets of four amino acids along the heme region of the monooxygenase P450-BM3.

Predicting the types of J-proteins using clustered amino acids.

J-proteins are molecular chaperones and present in a wide variety of organisms from prokaryote to eukaryote. Based on their domain organizations, J-proteins can be classified into 4 types, that is, Type I, Type II, Type III, and Type IV. Different types of J-proteins play distinct roles in influencing cancer properties and cell death. Thus, reliably annotating the types of J-proteins is essential to better understand their molecular functions. In the present work, a support vector machine based method was developed to identify the types of J-proteins using the tripeptide composition of reduced amino acid alphabet. In the jackknife cross-validation, the maximum overall accuracy of 94% was achieved on a stringent benchmark dataset. We also analyzed the amino acid compositions by using analysis of variance and found the distinct distributions of amino acids in each family of the J-proteins. To enhance the value of the practical applications of the proposed model, an online web server was developed and can be freely accessed.

Directed Evolution by Using Iterative Saturation Mutagenesis Based on Multiresidue Sites

Iterative saturation mutagenesis (ISM) in combination with reduced amino acid alphabets has been shown to be an efficient method for directed evolution. In order to minimize the screening effort, the number of residues in a given randomization site has thus far been restricted to two or three; this prevents oversampling from reaching astronomical numbers when 95 % library coverage is aimed for. In this study, ISM is applied for the first time by using randomization sites composed of five amino acid positions. The use of just two such sites (A and B) results in two different ISM pathways, A→B and B→A. A severely reduced amino acid alphabet (only five members) was employed for the building blocks-a minimal set of structurally representative amino acids. The Baeyer-Villiger monooxygenase PAMO was chosen as the enzyme for this proof-of-principle study. The test system employed tuning of activity and diastereoselectivity in the oxidation of 4-(bromomethylidene)cyclohexanone, which is not accepted by wild-type PAMO. Although only 8-9 % library coverage was ensured (as calculated by traditional statistics), notable activity and 99 % diastereoselectivity were obtained, thus indicating that such an ISM strategy is viable in protein engineering.

An information-theoretic classification of amino acids for the assessment of interfaces in protein–protein docking

Docking represents a versatile and powerful method to predict the geometry of protein-protein complexes. However, despite significant methodical advances, the identification of good docking solutions among a large number of false solutions still remains a difficult task. We have previously demonstrated that the formalism of mutual information (MI) from information theory can be adapted to protein docking, and we have now extended this approach to enhance its robustness and applicability. A large dataset consisting of 22,934 docking decoys derived from 203 different protein-protein complexes was used for an MI-based optimization of reduced amino acid alphabets representing the protein-protein interfaces. This optimization relied on a clustering analysis that allows one to estimate the mutual information of whole amino acid alphabets by considering all structural features simultaneously, rather than by treating them individually. This clustering approach is fast and can be applied in a similar fashion to the generation of reduced alphabets for other biological problems like fold recognition, sequence data mining, or secondary structure prediction. The reduced alphabets derived from the present work were converted into a scoring function for the evaluation of docking solutions, which is available for public use via the web service score-MI: http://score-MI.biochem.uni-erlangen.de.

Computational protein design with electrostatic focusing: Experimental characterization of a conditionally folded helical domain with a reduced amino acid alphabet

Automated methodologies to design synthetic proteins from first principles use energy computations to estimate the ability of the sequences to adopt a targeted structure. This approach is still far from systematically producing native-like sequences, due, most likely, to inaccuracies when modeling the interactions between the protein and its aqueous environment. This is particularly challenging when engineering small protein domains (with less polar pair interactions than with the solvent). We have re-designed a three-helix bundle, domain B, using a fixed backbone and a four amino acid alphabet. We have enlarged the rotamer library with conformers that increase the weight of electrostatic interactions within the design process without altering the energy function used to compute the folding free energy. Our synthetic sequences show less than 15% similarity to any Swissprot sequence. We have characterized our sequences in different solvents using circular dichroism and nuclear magnetic resonance. The targeted structure achieved is dependent on the solvent used. This method can be readily extended to larger domains. Our method will be useful for the engineering of proteins that become active only in a given solvent and for designing proteins in the context of hydrophobic solvents, an important fraction of the situations in the cell.

IHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

Heat shock proteins (HSPs) are a type of functionally related proteins present in all living organisms, both prokaryotes and eukaryotes. They play essential roles in protein–protein interactions such as folding and assisting in the establishment of proper protein conformation and prevention of unwanted protein aggregation. Their dysfunction may cause various life-threatening disorders, such as Parkinson’s, Alzheimer’s, and cardiovascular diseases. Based on their functions, HSPs are usually classified into six families: (i) HSP20 or sHSP, (ii) HSP40 or J-class proteins, (iii) HSP60 or GroEL/ES, (iv) HSP70, (v) HSP90, and (vi) HSP100. Although considerable progress has been achieved in discriminating HSPs from other proteins, it is still a big challenge to identify HSPs among their six different functional types according to their sequence information alone. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop a high-throughput computational tool in this regard. To take up such a challenge, a predictor called iHSP-PseRAAAC has been developed by incorporating the reduced amino acid alphabet information into the general form of pseudo amino acid composition. One of the remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimension disaster or overfitting problem in statistical prediction. It was observed that the overall success rate achieved by iHSP-PseRAAAC in identifying the functional types of HSPs among the aforementioned six types was more than 87%, which was derived by the jackknife test on a stringent benchmark dataset in which none of HSPs included has ⩾40% pairwise sequence identity to any other in the same subset. It has not escaped our notice that the reduced amino acid alphabet approach can also be used to investigate other protein classification problems. As a user-friendly web server, iHSP-PseRAAAC is accessible to the public at http://lin.uestc.edu.cn/server/iHSP-PseRAAAC.

Distance dependency and minimum amino acid alphabets for decoy scoring potentials

The validity and accuracy of a proposed tertiary structure of a protein can be assessed in several ways. Scoring such a structure by a knowledge-based potential is a well-known approach in molecular biophysics, an important task in structure prediction and refinement, and a key step in several experiments on protein structures. Although several parameterizations for such models have been derived over the course of time, improvements in accuracy by explicitly using continuous distance information have not been suggested yet. We close this methodological gap by formulating the parameterization of a protein structure model as a linear program. Optimization of the parameters was performed using amino acid distances calculated for the residues in topology rich 2830 protein structures. We show the capability of our derived model to discriminate between native structures and decoys for a diverse set of proteins. In addition, we discuss the effect of reduced amino acid alphabets on the model. In contrast to studies focusing on binary contact schemes (without considering distance dependencies and proposing five symbols as optimal alphabet size), we find an accurate protein alphabet size to contain at least five symbols, preferably more, to assure a satisfactory fold recognition capability.

Discrimination of Thermophilic and Mesophilic Proteins Using Reduced Amino Acid Alphabets with n-Grams

Protein thermostabilization has been the focus of recent research due to growing interest in the production of enzymes that can operate at temperatures that are industrially beneficial. Understanding the determinants of thermostabilization at the level of sequence and structure is important to design such enzymes. A bioinformatical approach was used to determine the extent by which reduced amino acid alphabets (RAAA) with n-grams (subsequences of length n) that were subjected to a t-test-based feature selection procedure can be used to discriminate proteins from thermophiles and mesophiles. Classification performance of 65 different protein alphabets with 3 different n-gram sizes was systematically evaluated using support vector machines in a test set that contained 707 proteins from mesophilic Xylella fastidosa and thermophilic Aquifex aeolicus. A classification accuracy of 91.796% was achieved with Hsdm16 RAAA with 13 features: EK-ILV-ST-A-G-F-H-Q-N-R-M-W-Y. The t-test-based feature selection procedure reduced the classification time without significantly affecting classification accuracy. The overall combination of methods in this paper is useful and computationally fast for classifying protein sequences from thermophiles and mesophiles using sequence information alone.

Prediction of ketoacyl synthase family using reduced amino acid alphabets

Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes' catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.

RAPSearch: a fast protein similarity search tool for short reads

BackgroundNext Generation Sequencing (NGS) is producing enormous corpuses of short DNA reads, affecting emerging fields like metagenomics. Protein similarity search--a key step to achieve annotation of protein-coding genes in these short reads, and identification of their biological functions--faces daunting challenges because of the very sizes of the short read datasets.ResultsWe developed a fast protein similarity search tool RAPSearch that utilizes a reduced amino acid alphabet and suffix array to detect seeds of flexible length. For short reads (translated in 6 frames) we tested, RAPSearch achieved ~20-90 times speedup as compared to BLASTX. RAPSearch missed only a small fraction (~1.3-3.2%) of BLASTX similarity hits, but it also discovered additional homologous proteins (~0.3-2.1%) that BLASTX missed. By contrast, BLAT, a tool that is even slightly faster than RAPSearch, had significant loss of sensitivity as compared to RAPSearch and BLAST.ConclusionsRAPSearch is implemented as open-source software and is accessible at http://omics.informatics.indiana.edu/mg/RAPSearch. It enables faster protein similarity search. The application of RAPSearch in metageomics has also been demonstrated.

Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet

Due to the complexity of Plasmodium falciparum (PF) genome, predicting mitochondrial proteins of PF is more difficult than other species. In this study, using the n-peptide composition of reduced amino acid alphabet (RAAA) obtained from structural alphabet named Protein Blocks as feature parameter, the increment of diversity (ID) is firstly developed to predict mitochondrial proteins. By choosing the 1-peptide compositions on the N-terminal regions with 20 residues as the only input vector, the prediction performance achieves 86.86% accuracy with 0.69 Mathew's correlation coefficient (MCC) by the jackknife test. Moreover, by combining with the hydropathy distribution along protein sequence and several reduced amino acid alphabets, we achieved maximum MCC 0.82 with accuracy 92% in the jackknife test by using the developed ID model. When evaluating on an independent dataset our method performs better than existing methods. The results indicate that the ID is a simple and efficient prediction method for mitochondrial proteins of malaria parasite.

Manipulating the Stereoselectivity of Limonene Epoxide Hydrolase by Directed Evolution Based on Iterative Saturation Mutagenesis

Limonene epoxide hydrolase from Rhodococcus erythropolis DCL 14 (LEH) is known to be an exceptional epoxide hydrolase (EH) because it has an unusual secondary structure and catalyzes the hydrolysis of epoxides by a rare one-step mechanism in contrast to the usual two-step sequence. From a synthetic organic viewpoint it is unfortunate that LEH shows acceptable stereoselectivity essentially only in the hydrolysis of the natural substrate limonene epoxide, which means that this EH cannot be exploited as a catalyst in asymmetric transformations of other substrates. In the present study, directed evolution using iterative saturation mutagenesis (ISM) has been tested as a means to engineer LEH mutants showing broad substrate scope with high stereoselectivity. By grouping individual residues aligning the binding pocket correctly into randomization sites and performing saturation mutagenesis iteratively using a reduced amino acid alphabet, mutants were obtained which catalyze the desymmetrization of cyclopentene-oxide with stereoselective formation of either the (R,R)- or the (S,S)-diol on an optional basis. The mutants prove to be excellent catalysts for the desymmetrization of other meso-epoxides and for the hydrolytic kinetic resolution of racemic substrates, without performing new mutagenesis experiments. Since less than 5000 tranformants had to be screened for achieving these results, this study contributes to the generalization of ISM as a fast and reliable method for protein engineering. In order to explain some of the stereoselective consequences of the observed mutations, a simple model based on molecular dynamics simulations has been proposed.

Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets

BackgroundPhylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.ResultsWe demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively.ConclusionsThe overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.

Distance‐dependent classification of amino acids by information theory

Reduced amino acid alphabets are useful to understand molecular evolution as they reveal basal, shared properties of amino acids, which the structures and functions of proteins rely on. Several previous studies derived such reduced alphabets and linked them to the origin of life and biotechnological applications. However, all this previous work presupposes that only direct contacts of amino acids in native protein structures are relevant. We show in this work, using information-theoretical measures, that an appropriate alphabet reduction scheme is in fact a function of the maximum distance amino acids interact at. Although for small distances our results agree with previous ones, we show how long-range interactions change the overall picture and prompt for a revised understanding of the protein design process.

A Search for Energy Minimized Sequences of Proteins

In this paper, we present numerical evidence that supports the notion of minimization in the sequence space of proteins for a target conformation. We use the conformations of the real proteins in the Protein Data Bank (PDB) and present computationally efficient methods to identify the sequences with minimum energy. We use edge-weighted connectivity graph for ranking the residue sites with reduced amino acid alphabet and then use continuous optimization to obtain the energy-minimizing sequences. Our methods enable the computation of a lower bound as well as a tight upper bound for the energy of a given conformation. We validate our results by using three different inter-residue energy matrices for five proteins from protein data bank (PDB), and by comparing our energy-minimizing sequences with 80 million diverse sequences that are generated based on different considerations in each case. When we submitted some of our chosen energy-minimizing sequences to Basic Local Alignment Search Tool (BLAST), we obtained some sequences from non-redundant protein sequence database that are similar to ours with an E-value of the order of 10-7. In summary, we conclude that proteins show a trend towards minimizing energy in the sequence space but do not seem to adopt the global energy-minimizing sequence. The reason for this could be either that the existing energy matrices are not able to accurately represent the inter-residue interactions in the context of the protein environment or that Nature does not push the optimization in the sequence space, once it is able to perform the function.

Using reduced amino acid composition to predict defensin family and subfamily: Integrating similarity measure and structural alphabet

Defensins are essentially ancient natural antibiotics with potent activity extending from lower organisms to humans. They can inhibit the growth or virulence of micro-organisms directly or indirectly enhance the host's immune system. The successful prediction of defensin peptides will provide very useful information and insights for the basic research of defensins. In this study, by selecting the N-peptide composition of reduced amino acid alphabet (RAAA) obtained from structural alphabet named Protein Blocks as the feature parameters, the increment of diversity (ID) is firstly developed to predict defensins family and subfamily. The jackknife test based on 2-peptide composition of reduced amino acid alphabet (RAAA) with 13 reduced amino acids shows that the overall accuracy of prediction are 91.36% for defensin family, and 94.21% for defensin subfamily. The results indicate that ID_RAAA is a simple and efficient prediction method for defensin peptides.

Alignment-free Comparison of Protein Sequences Based on Reduced Amino Acid Alphabets

Protein sequences are treated as stochastic processes on the basis of a reduced amino acid alphabet of 10 types of amino acids. The realization of a stochastic process is described by associated transition probability matrix that corresponds to the process uniquely. Then new distances between transition probability matrices are defined for sequences similarity analysis. Two separate datasets are prepared and tested to identify the validity of the method. The results demonstrate the new method is powerful and efficient.

Reduced amino acid alphabets exhibit an improved sensitivity and selectivity in fold assignment

Many proteins with vastly dissimilar sequences are found to share a common fold, as evidenced in the wealth of structures now available in the Protein Data Bank. One idea that has found success in various applications is the concept of a reduced amino acid alphabet, wherein similar amino acids are clustered together. Given the structural similarity exhibited by many apparently dissimilar sequences, we undertook this study looking for improvements in fold recognition by comparing protein sequences written in a reduced alphabet. We tested over 150 of the amino acid clustering schemes proposed in the literature with all-versus-all pairwise sequence alignments of sequences in the Distance mAtrix aLIgnment database. We combined several metrics from information retrieval popular in the literature: mean precision, area under the Receiver Operating Characteristic curve and recall at a fixed error rate and found that, in contrast to previous work, reduced alphabets in many cases outperform full alphabets. We find that reduced alphabets can perform at a level comparable to full alphabets in correct pairwise alignment of sequences and can show increased sensitivity to pairs of sequences with structural similarity but low-sequence identity. Based on these results, we hypothesize that reduced alphabets may also show performance gains with more sophisticated methods such as profile and pattern searches. A table of results as well as the substitution matrices and residue groupings from this study can be downloaded from (http://www.rpgroup.caltech.edu/publications/supplements/alphabets).

Shedding light on the efficacy of laboratory evolution based on iterative saturation mutagenesis

Directed evolution has emerged as a general way to engineer essentially any catalytic property of enzymes, but due to the bottleneck imposed by the necessity to screen large libraries of mutants, it is often time-consuming. In order to make this type of protein engineering faster and more efficient than in the past, improved methods for probing protein sequence space need to be developed. This review focuses on recent advances which help to solve the traditional numbers problem in laboratory evolution, as in the directed evolution of enantioselective enzymes. Our contribution in this endeavour is iterative saturation mutagenesis (ISM), which can be used to enhance the enantioselectivity and/or the thermostability of enzymes. The option to use reduced amino acid alphabets as defined by the appropriate codon degeneracies supplements in a crucial way the toolbox in this knowledge-guided approach to laboratory evolution.

Amino acid alphabet size in protein evolution experiments: better to search a small library thoroughly or a large library sparsely?

We compare the results obtained from searching a smaller library thoroughly versus searching a more diverse, larger library sparsely. We study protein evolution with reduced amino acid alphabets, by simulating directed evolution experiments at three different alphabet sizes: 20, 5 and 2. We employ a physical model for evolution, the generalized NK model, that has proved successful in modeling protein evolution, antibody evolution and T-cell selection. We find that antibodies with higher affinity are found by searching a library with a larger alphabet sparsely than by searching a smaller library thoroughly, even with well-designed reduced libraries. We also find ranked amino acid usage frequencies in agreement with observations of the CDR-H3 variable region of human antibodies.