Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity

We investigate the conservation of amino acid residue sequences in 21 DNA-binding protein families and study the effects that mutations have on DNA-sequence recognition. The observations are best understood by assigning each protein family to one of three classes: (i) non-specific, where binding is independent of DNA sequence; (ii) highly specific, where binding is specific and all members of the family target the same DNA sequence; and (iii) multi-specific, where binding is also specific, but individual family members target different DNA sequences. Overall, protein residues in contact with the DNA are better conserved than the rest of the protein surface, but there is a complex underlying trend of conservation for individual residue positions. Amino acid residues that interact with the DNA backbone are well conserved across all protein families and provide a core of stabilising contacts for homologous protein-DNA complexes. In contrast, amino acid residues that interact with DNA bases have variable levels of conservation depending on the family classification. In non-specific families, base-contacting residues are well conserved and interactions are always found in the minor groove where there is little discrimination between base types. In highly specific families, base-contacting residues are highly conserved and allow member proteins to recognise the same target sequence. In multi-specific families, base-contacting residues undergo frequent mutations and enable different proteins to recognise distinct target sequences. Finally, we report that interactions with bases in the target sequence often follow (though not always) a universal code of amino acid-base recognition and the effects of amino acid mutations can be most easily understood for these interactions.     

A Combinatorial Approach to Detect Coevolved Amino Acid Networks in Protein Families of Variable Divergence

Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.     

Evolutionarily conserved networks of residues mediate allosteric communication in proteins

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Here, we describe a sequence-based statistical method for quantitatively mapping the global network of amino acid interactions in a protein. Application of this method for three structurally and functionally distinct protein families (G protein-coupled receptors, the chymotrypsin class of serine proteases and hemoglobins) reveals a surprisingly simple architecture for amino acid interactions in each protein family: a small subset of residues forms physically connected networks that link distant functional sites in the tertiary structure. Although small in number, residues comprising the network show excellent correlation with the large body of mechanistic data available for each family. The data suggest that evolutionarily conserved sparse networks of amino acid interactions represent structural motifs for allosteric communication in proteins.     

Identification of latent periodicity in amino acid sequences of protein families

For detection of the latent periodicity of the protein families responsible for various biological functions, methods of information decomposition, cyclic profile alignment, and the method of noise decomposition have been used. The latent periodicity, being specific to a particular family, is recognized in 94 of 110 analyzed protein families. Family specific periodicity was found for more than 70% of amino acid sequences in each of these families. Based on such sequences the characteristic profile of the latent periodicity has been deduced for each family. Possible relationship between the recognized latent periodicity, evolution of proteins, and their structural organization is discussed.     

Principal components analysis of protein sequence clusters

Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large variable space, i.e. the number of amino acids that make up the protein sequence, and PCA is powerful at reducing the dimensionality of complex problems by projecting the data into an eigenspace that represents the directions of greatest variation. However, PCA of aligned protein sequence families is complicated by the fact that protein sequences are traditionally represented by single letter alphabetic codes, whereas PCA of protein sequence families requires conversion of sequence information into a numerical representation. Here, we introduce a new amino acid sequence conversion algorithm optimized for PCA data input. The method is demonstrated using a small artificial dataset to illustrate the characteristics and performance of the algorithm, as well as a small protein sequence family consisting of nine members, COG2263, and finally with a large protein sequence family, Pfam04237, which contains more than 1,800 sequences that group into two sub-clusters.     

Modelling and mutational evidence identify the substrate binding site and functional elements in APC amino acid transporters

The Amino acid-Polyamine-Organocation (APC) superfamily is the main family of amino acid transporters found in all domains of life and one of the largest families of secondary transporters. Here, using a sensitive homology threading approach and modelling we show that the predicted structure of APC members is extremely similar to the crystal structures of several prokaryotic transporters belonging to evolutionary distinct protein families with different substrate specificities. All of these proteins, despite having no primary amino acid sequence similarity, share a similar structural core, consisting of two V-shaped domains of five transmembrane domains each, intertwined in an antiparallel topology. Based on this model, we reviewed available data on functional mutations in bacterial, fungal and mammalian APCs and obtained novel mutational data, which provide compelling evidence that the amino acid binding pocket is located in the vicinity of the unwound part of two broken helices, in a nearly identical position to the structures of similar transporters. Our analysis is fully supported by the evolutionary conservation and specific amino acid substitutions in the proposed substrate binding domains. Furthermore, it allows predictions concerning residues that might be crucial in determining the specificity profile of APC members. Finally, we show that two cytoplasmic loops constitute important functional elements in APCs. Our work along with different kinetic and specificity profiles of APC members in easily manipulated bacterial and fungal model systems could form a unique framework for combining genetic, in-silico and structural studies, for understanding the function of one of the most important transporter families.     

The S. cerevisiae architectural HMGB protein NHP6A complexed with DNA: DNA and protein conformational changes upon binding

NHP6A is a non-sequence-specific DNA-binding protein from Saccharomyces cerevisiae which belongs to the HMGB protein family. Previously, we have solved the structure of NHP6A in the absence of DNA and modeled its interaction with DNA. Here, we present the refined solution structures of the NHP6A-DNA complex as well as the free 15bp DNA. Both the free and bound forms of the protein adopt the typical L-shaped HMGB domain fold. The DNA in the complex undergoes significant structural rearrangement from its free form while the protein shows smaller but significant conformational changes in the complex. Structural and mutational analysis as well as comparison of the complex with the free DNA provides insight into the factors that contribute to binding site selection and DNA deformations in the complex. Further insight into the amino acid determinants of DNA binding by HMGB domain proteins is given by a correlation study of NHP6A and 32 other HMGB domains belonging to both the DNA-sequence-specific and non-sequence-specific families of HMGB proteins. The resulting correlations can be rationalized by comparison of solved structures of HMGB proteins.     

Remote protein homology detection using recurrence quantification analysis and amino acid physicochemical properties

Remote homology detection refers to the detection of structure homology in evolutionarily related proteins with low sequence similarity. Supervised learning algorithms such as support vector machine (SVM) are currently the most accurate methods. In most of these SVM-based methods, efforts have been dedicated to developing new kernels to better use the pairwise alignment scores or sequence profiles. Moreover, amino acids’ physicochemical properties are not generally used in the feature representation of protein sequences. In this article, we present a remote homology detection method that incorporates two novel features: (1) a protein's primary sequence is represented using amino acid's physicochemical properties and (2) the similarity between two proteins is measured using recurrence quantification analysis (RQA). An optimization scheme was developed to select different amino acid indices (up to 10 for a protein family) that are best to characterize the given protein family. The selected amino acid indices may enable us to draw better biological explanation of the protein family classification problem than using other alignment-based methods. An SVM-based classifier will then work on the space described by the RQA metrics. The classification scheme is named as SVM-RQA. Experiments at the superfamily level of the SCOP1.53 dataset show that, without using alignment or sequence profile information, the features generated from amino acid indices are able to produce results that are comparable to those obtained by the published state-of-the-art SVM kernels. In the future, better prediction accuracies can be expected by combining the alignment-based features with our amino acids property-based features. Supplementary information including the raw dataset, the best-performing amino acid indices for each protein family and the computed RQA metrics for all protein sequences can be downloaded from http://ym151113.ym.edu.tw/svm-rqa.     

Amino acid similarity coefficients for protein modeling and sequence alignment derived from main-chain folding angles

A set of "similarity-parameters" was calculated that reflects the influence of the proteinogenic amino acids on the structure of the protein backbone. The parameters were derived from a detailed analysis of the amino acid specific main-chain torsion angle distributions as they are found in proteins (highly resolved protein structures from the Brookhaven Protein Data Bank). The purpose of these parameters is threefold: (1) they should help in estimating the structural effect of an amino acid substitution during the design of new mutants in protein-engineering; (2) in modeling by homology they should mark places in the protein where changes in the folding are expected; and (3) they should form a scoring matrix in protein sequence alignment superior to identity scoring. The usability of the "structure derived correlation matrix (SCM)" for these purposes is assessed and demonstrated for some examples in the paper.     

Simple alignment-free methods for protein classification: a case study from G-protein-coupled receptors

Computational methods of predicting protein functions rely on detecting similarities among proteins. However, sufficient sequence information is not always available for some protein families. For example, proteins of interest may be new members of a divergent protein family. The performance of protein classification methods could vary in such challenging situations. Using the G-protein-coupled receptor superfamily as an example, we investigated the performance of several protein classifiers. Alignment-free classifiers based on support vector machines using simple amino acid compositions were effective in remote-similarity detection even from short fragmented sequences. Although it is computationally expensive, a support vector machine classifier using local pairwise alignment scores showed very good balanced performance. More commonly used profile hidden Markov models were generally highly specific and well suited to classifying well-established protein family members. It is suggested that different types of protein classifiers should be applied to gain the optimal mining power.     

Generalized Baum-Welch Algorithm Based on the Similarity between Sequences

The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.     

Analysis of posttranslational modifications of proteins by tandem mass spectrometry

Protein activity and turnover is tightly and dynamically regulated in living cells. Whereas the three-dimensional protein structure is predominantly determined by the amino acid sequence, posttranslational modification (PTM) of proteins modulates their molecular function and the spatial-temporal distribution in cells and tissues. Most PTMs can be detected by protein and peptide analysis by mass spectrometry (MS), either as a mass increment or a mass deficit relative to the nascent unmodified protein. Tandem mass spectrometry (MS/MS) provides a series of analytical features that are highly useful for the characterization of modified proteins via amino acid sequencing and specific detection of posttranslationally modified amino acid residues. Large-scale, quantitative analysis of proteins by MS/MS is beginning to reveal novel patterns and functions of PTMs in cellular signaling networks and biomolecular structures.     

氨基酸组成对蛋白质耐热性的影响

为了研究一级结构对蛋白质耐热性的影响,利用软件DNAMAN对16个家族32种蛋白质序列进行了氨基酸含量分析,并统计分析了氨基酸组成对蛋白质耐热性的影响。通过比较同一家族的高低温蛋白质序列及16个家族中所有高温和低温蛋白质序列中氨基酸含量的变化可以推断(从低温到高温)：Ser、Cys．含量降低显著,Arg、Ile、Pro含量升高显著。由此可知高温蛋白质倾向于含有疏水性氨基酸而避免亲水性氨基酸。     

Selective constraints on amino acids estimated by a mechanistic codon substitution model with multiple nucleotide changes

Background

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices.

Results

Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins.

Conclusions/Significance

The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.     

Automatic methods for predicting functionally important residues

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily ("Tree-determinant residues"). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.     

Convex hull principle for classification and phylogeny of eukaryotic proteins

This study quantitatively validates the principle that the biological properties associated with a given genotype are determined by the distribution of amino acids. In order to visualize this central law of molecular biology, each protein was represented by a point in 250-dimensional space based on its amino acid distribution. Proteins from the same family are found to cluster together, leading to the principle that the convex hull surrounding protein points from the same family do not intersect with the convex hulls of other protein families. This principle was verified computationally for all available and reliable protein kinases and human proteins. In addition, we generated 2,328,761 figures to show that the convex hulls of different families were disjoint from each other. The classification performs well with high and robust accuracy (95.75% and 97.5%) together with reasonable phylogenetic trees validate our methods further.     

Suggestions for "safe" residue substitutions in site-directed mutagenesis

The conserved topological structure observed in various molecular families such as globins or cytochromes c allows structural equivalencing of residues in every homologous structure and defines in a coherent way a global alignment in each sequence family. A search was performed for equivalent residue pairs in various topological families that were buried in protein cores or exposed at the protein surface and that had mutated but maintained similar unmutated environments. Amino acid residues with atoms in contact with the mutated residue pairs defined the environment. Matrices of preferred amino acid exchanges were then constructed and preferred or avoided amino acid substitutions deduced. Given the conserved atomic neighborhoods, such natural in vivo substitutions are subject to similar constrains as point mutations performed in site-directed mutagenesis experiments. The exchange matrices should provide guidelines for "safe" amino acid substitutions least likely to disturb the protein structure, either locally or in its overall folding pathway, and most likely to allow probing the structural and functional significance of the substituted site.     

Relation between sequence similarity and structural similarity in proteins. Role of important properties of amino acids

In a previous paper we obtained ten (orthogonal) factors, linear combinations of which can express the properties of the 20 naturally occurring amino acids. In this paper, we assume that the most important properties (linear combinations of these ten factors) that determine the three-dimensional structure of a protein are conserved properties, i.e., are those that have been conserved during evolution. Two definitions of a conserved property are presented: (1) a conserved property for an average protein is defined as that linear combination of the ten factors that optimally expresses the similarity of one amino acid to another (hence, little change during evolution), as given by the relatedness odds matrix of Dayhoff et al.; (2) a conserved property for each position in the amino acid sequence (locus) of a specific family of homologous proteins (the cytochromec family or the globin family) is defined as that linear combination of the ten factors that is common among a set of amino acids at a given locus when the sequences are properly aligned. When the specificity at each locus is averaged over all loci, the same features are observed for three expressions of these two definitions, namely the conserved property for an average protein, the average conserved property for the cytochromec family, and the average conserved property for the globin family; we find that bulk and hydrophobicity (information about packing and long-range interactions) are more important than other properties, such as the preference for adopting a specific backbone structure (information about short-range interactions). We also demonstrate that the sequence profile of a conserved property, defined for each locus of a protein family (definition 2), corresponds uniquely to the three-dimensional structure, while the conserved property for an average protein (definition 1) is not useful for the prediction of protein structure. The amino acid sequences of numerous proteins are searched to find those that are similar, in terms of the conserved properties (definition 2), to sequences of the same size from one of the homologous families (cytochromec and globin, respectively) for whose loci the conserved properties were defined. Many similar sequences are found, the number of similarities decreasing with increasing size of the segment. However, the segments must be rather long (15 residues) before the comparisons become meaningful. As an example, one sufficiently large sequence (20 residues) from a protein of known structure (apo-liver alcohol dehydrogenase that is not a member of either family) is found to be similar in the conserved properties to a particular sequence of a member of the family of human hemoglobin chains, and the two sequences have similar structures. This means that, since conserved properties are expected to be structure determinants, we can use the conserved properties to predict an initial protein structure for subsequent energy minimization for a protein for which the conserved properties are similar to those of a family of proteins with a sufficiently large number of homologous amino acid sequences; such a large number of homologous sequences is required to define a conserved property for each locus of the homologous protein family.