Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets

Reduced or simplified amino acid alphabets group the 20 naturally occurring amino acids into a smaller number of representative protein residues. To date, several reduced amino acid alphabets have been proposed, which have been derived and optimized by a variety of methods. The resulting reduced amino acid alphabets have been applied to pattern recognition, generation of consensus sequences from multiple alignments, protein folding, and protein structure prediction. In this work, amino acid substitution matrices and statistical potentials were derived based on several reduced amino acid alphabets and their performance assessed in a large benchmark for the tasks of sequence alignment and fold assessment of protein structure models, using as a reference frame the standard alphabet of 20 amino acids. The results showed that a large reduction in the total number of residue types does not necessarily translate into a significant loss of discriminative power for sequence alignment and fold assessment. Therefore, some definitions of a few residue types are able to encode most of the relevant sequence/structure information that is present in the 20 standard amino acids. Based on these results, we suggest that the use of reduced amino acid alphabets may allow to increasing the accuracy of current substitution matrices and statistical potentials for the prediction of protein structure of remote homologs.     

Reduction of protein sequence complexity by residue grouping

It is well known that there are some similarities among various naturally occurring amino acids. Thus, the complexity in protein systems could be reduced by sorting these amino acids with similarities into groups and then protein sequences can be simplified by reduced alphabets. This paper discusses how to group similar amino acids and whether there is a minimal amino acid alphabet by which proteins can be folded. Various reduced alphabets are obtained by reserving the maximal information for the simplified protein sequence compared with the parent sequence using global sequence alignment. With these reduced alphabets and simplified similarity matrices, we achieve recognition of the protein fold based on the similarity score of the sequence alignment. The coverage in dataset SCOP40 for various levels of reduction on the amino acid types is obtained, which is the number of homologous pairs detected by program BLAST to the number marked by SCOP40. For the reduced alphabets containing 10 types of amino acids, the ability to detect distantly related folds remains almost at the same level as that by the alphabet of 20 types of amino acids, which implies that 10 types of amino acids may be the degree of freedom for characterizing the complexity in proteins.     

Effect of alphabet size and foldability requirements on protein structure designability.

A number of investigators have addressed the issue of why certain protein structures are especially common by considering structure designability, defined as the number of sequences that would successfully fold into any particular native structure. One such approach, based on foldability, suggested that structures could be classified according to their maximum possible foldability and that this optimal foldability would be highly correlated with structure designability. Other approaches have focused on computing the designability of lattice proteins written with reduced two-letter amino acid alphabets. These different approaches suggested contrasting characteristics of the most designable structures. This report compares the designability of lattice proteins over a wide range of amino acid alphabets and foldability requirements. While all alphabets have a wide distribution of protein designabilities, the form of the distribution depends on how protein "viability" is defined. Furthermore, under increasing foldability requirements, the change in designabilities for all alphabets are in good agreement with the previous conclusions of the foldability approach. Most importantly, it was noticed that those structures that were highly designable for the two-letter amino acid alphabets are not especially designable with higher-letter alphabets.     

A genetic approach for building different alphabets for peptide and protein classification

Background  

In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.     

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     

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned sequences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitution matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9.     

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned sequences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitution matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9. Supported by the National Natural Science Foundation of China (Grant Nos. 90403120, 10474041 and 10021001) and the Nonlinear Project (973) of the NSM     

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.     

On reduced amino acid alphabets for phylogenetic inference

We investigate the use of Markov models of evolution for reduced amino acid alphabets or bins of amino acids. The use of reduced amino acid alphabets can ameliorate effects of model misspecification and saturation. We present algorithms for 2 different ways of automating the construction of bins: minimizing criteria based on properties of rate matrices and minimizing criteria based on properties of alignments. By simulation, we show that in the absence of model misspecification, the loss of information due to binning is found to be insubstantial, and the use of Markov models at the binned level is found to be almost as effective as the more appropriate missing data approach. By applying these approaches to real data sets where compositional heterogeneity and/or saturation appear to be causing biased tree estimation, we find that binning can improve topological estimation in practice.     

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. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Reduced alphabet for protein folding prediction

What are the key building blocks that would have been needed to construct complex protein folds? This is an important issue for understanding protein folding mechanism and guiding de novo protein design. Twenty naturally occurring amino acids and eight secondary structures consist of a 28‐letter alphabet to determine folding kinetics and mechanism. Here we predict folding kinetic rates of proteins from many reduced alphabets. We find that a reduced alphabet of 10 letters achieves good correlation with folding rates, close to the one achieved by full 28‐letter alphabet. Many other reduced alphabets are not significantly correlated to folding rates. The finding suggests that not all amino acids and secondary structures are equally important for protein folding. The foldable sequence of a protein could be designed using at least 10 folding units, which can either promote or inhibit protein folding. Reducing alphabet cardinality without losing key folding kinetic information opens the door to potentially faster machine learning and data mining applications in protein structure prediction, sequence alignment and protein design. Proteins 2015; 83:631–639. © 2015 Wiley Periodicals, Inc.     

Are Molecular Alphabets Universal Enabling Factors for the Evolution of Complex Life?

Terrestrial biosystems depend on macromolecules, and this feature is often considered as a likely universal aspect of life. While opinions differ regarding the importance of small-molecule systems in abiogenesis, escalating biological functional demands are linked with increasing complexity in key molecules participating in biosystem operations, and many such requirements cannot be efficiently mediated by relatively small compounds. It has long been recognized that known life is associated with the evolution of two distinct molecular alphabets (nucleic acid and protein), specific sequence combinations of which serve as informational and functional polymers. In contrast, much less detailed focus has been directed towards the potential universal need for molecular alphabets in constituting complex chemically-based life, and the implications of such a requirement. To analyze this, emphasis here is placed on the generalizable replicative and functional characteristics of molecular alphabets and their concatenates. A primary replicative alphabet based on the simplest possible molecular complementarity can potentially enable evolutionary processes to occur, including the encoding of secondarily functional alphabets. Very large uniquely specified (‘non-alphabetic’) molecules cannot feasibly underlie systems capable of the replicative and evolutionary properties which characterize complex biosystems. Transitions in the molecular evolution of alphabets can be related to progressive bridging of barriers which enable higher levels of biosystem organization. It is thus highly probable that molecular alphabets are an obligatory requirement for complex chemically-based life anywhere in the universe. In turn, reference to molecular alphabets should be usefully applied in current definitions of life.     

Simplifying amino acid alphabets by means of a branch and bound algorithm and substitution matrices

MOTIVATION: Protein and DNA are generally represented by sequences of letters. In a number of circumstances simplified alphabets (where one or more letters would be represented by the same symbol) have proved their potential utility in several fields of bioinformatics including searching for patterns occurring at an unexpected rate, studying protein folding and finding consensus sequences in multiple alignments. The main issue addressed in this paper is the possibility of finding a general approach that would allow an exhaustive analysis of all the possible simplified alphabets, using substitution matrices like PAM and BLOSUM as a measure for scoring. RESULTS: The computational approach presented in this paper has led to a computer program called AlphaSimp (Alphabet Simplifier) that can perform an exhaustive analysis of the possible simplified amino acid alphabets, using a branch and bound algorithm together with standard or user-defined substitution matrices. The program returns a ranked list of the highest-scoring simplified alphabets. When the extent of the simplification is limited and the simplified alphabets are maintained above ten symbols the program is able to complete the analysis in minutes or even seconds on a personal computer. However, the performance becomes worse, taking up to several hours, for highly simplified alphabets. AVAILABILITY: AlphaSimp and other accessory programs are available at http://bioinformatics.cribi.unipd.it/alphasimp     

Using K-minimum increment of diversity to predict secretory proteins of malaria parasite based on groupings of amino acids

Due to the complexity of Plasmodium falciparumis genome, predicting secretory proteins of P. falciparum is more difficult than other species. In this study, based on the measure of diversity definition, a new K-nearest neighbor method, K-minimum increment of diversity (K-MID), is introduced to predict secretory proteins. The prediction performance of the K-MID by using amino acids composition as the only input vector achieves 88.89% accuracy with 0.78 Mathew’s correlation coefficient (MCC). Further, the several reduced amino acids alphabets are applied to predict secretory proteins and the results show that the prediction results are improved to 90.67% accuracy with 0.83 MCC by using the 169 dipeptide compositions of the reduced amino acids alphabets obtained from Protein Blocks method.     

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks

Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurate prediction of IDRs is challenging because their genome wide occurrence and a low ratio of disordered residues make them difficult targets for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy which is time consuming and computationally expensive. This article describes an ab initio sequence-only prediction method—which tries to overcome the challenge of accurate prediction posed by IDRs—based on reduced amino acid alphabets and convolutional neural networks (CNNs). We experiment with six different 3-letter reduced alphabets. We argue that the dimensional reduction in the input alphabet facilitates the detection of complex patterns within the sequence by the convolutional step. Experimental results show that our proposed IDR predictor performs at the same level or outperforms other state-of-the-art methods in the same class, achieving accuracy levels of 0.76 and AUC of 0.85 on the publicly available Critical Assessment of protein Structure Prediction dataset (CASP10). Therefore, our method is suitable for proteome-wide disorder prediction yielding similar or better accuracy than existing approaches at a faster speed.     

Optimal alphabets for an RNA world

Experiments have shown that the canonical AUCG genetic alphabet is not the only possible nucleotide alphabet. In this work we address the question ''is the canonical alphabet optimal?'' We make the assumption that the genetic alphabet was determined in the RNA world. Computational tools are used to infer the RNA secondary structure (shape) from a given RNA sequence, and statistics from RNA shapes are gathered with respect to alphabet size. Then, simulations based upon the replication and selection of fixed-sized RNA populations are used to investigate the effect of alternative alphabets upon RNA''s ability to step through a fitness landscape. These results show that for a low copy fidelity the canonical alphabet is fitter than two-, six- and eight-letter alphabets. In higher copy-fidelity experiments, six-letter alphabets outperform the four-letter alphabets, suggesting that the canonical alphabet is indeed a relic of the RNA world.     

Amino acid alphabet reduction preserves fold information contained in contact interactions in proteins

To reduce complexity, understand generalized rules of protein folding, and facilitate de novo protein design, the 20‐letter amino acid alphabet is commonly reduced to a smaller alphabet by clustering amino acids based on some measure of similarity. In this work, we seek the optimal alphabet that preserves as much of the structural information found in long‐range (contact) interactions among amino acids in natively‐folded proteins. We employ the Information Maximization Device, based on information theory, to partition the amino acids into well‐defined clusters. Numbering from 2 to 19 groups, these optimal clusters of amino acids, while generated automatically, embody well‐known properties of amino acids such as hydrophobicity/polarity, charge, size, and aromaticity, and are demonstrated to maintain the discriminative power of long‐range interactions with minimal loss of mutual information. Our measurements suggest that reduced alphabets (of less than 10) are able to capture virtually all of the information residing in native contacts and may be sufficient for fold recognition, as demonstrated by extensive threading tests. In an expansive survey of the literature, we observe that alphabets derived from various approaches—including those derived from physicochemical intuition, local structure considerations, and sequence alignments of remote homologs—fare consistently well in preserving contact interaction information, highlighting a convergence in the various factors thought to be relevant to the folding code. Moreover, we find that alphabets commonly used in experimental protein design are nearly optimal and are largely coherent with observations that have arisen in this work. Proteins 2015; 83:2198–2216. © 2015 Wiley Periodicals, Inc.     

Analysis and prediction of protein local structure based on structure alphabets

In recent years, protein structure prediction using local structure information has made great progress. Many fragment libraries or structure alphabets have been developed. In this study, the entropies and correlations of local structures are first calculated. The results show that neighboring local structures are strongly correlated. Then, a dual-layer model has been designed for protein local structure prediction. The position-specific score matrix, generated by PSI-BLAST, is inputted to the first-layer classifier, whose output is further enhanced by a second-layer classifier. The neural network is selected as the classifier. Two structure alphabets are explored, which are represented in Cartesian coordinate space and in torsion angles space respectively. Testing on the nonredundant dataset shows that the dual-layer model is an efficient method for protein local structure prediction. The Q-scores are 0.456 and 0.585 for the two structure alphabets, which is a significant improvement in comparison with related works.     

Automated Alphabet Reduction for Protein Datasets

Background  

We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.     

On Subset Seeds for Protein Alignment

We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets. We then perform a comparative analysis of seeds built over those alphabets and compare them with the standard Blastp seeding method [2], [3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seeds is less expressive (but less costly to implement) than the cumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix. Finally, we perform a large-scale benchmarking of our seeds against several main databases of protein alignments. Here again, the results show a comparable or better performance of our seeds versus Blastp.