CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins

Background

The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better.

Results

Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions.Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family.

Conclusions

CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.     

Structure‐guided approach for detecting large domain inserts in protein sequences as illustrated using the haloacid dehalogenase superfamily

In multi‐domain proteins, the domains typically run end‐to‐end, that is, one domain follows the C‐terminus of another domain. However, approximately 10% of multi‐domain proteins are formed by insertion of one domain sequence into that of another domain. Detecting such insertions within protein sequences is a fundamental challenge in structural biology. The haloacid dehalogenase superfamily (HADSF) serves as a challenging model system wherein a variable cap domain (～5–200 residues in length) accessorizes the ubiquitous Rossmann‐fold core domain, with variations in insertion site and topology corresponding to different classes of cap types. Herein, we describe a comprehensive computational strategy, CapPredictor, for determining large, variable domain insertions in protein sequences. Using a novel sequence‐alignment algorithm in conjunction with a structure‐guided sequence profile from 154 core‐domain‐only structures, more than 40,000 HADSF member sequences were assigned cap types. The resulting data set afforded insight into HADSF evolution. Notably, a similar distribution of cap‐type classes across different phyla was observed, indicating that all cap types existed in the last universal common ancestor. In addition, comparative analyses of the predicted cap‐type and functional assignments showed that different cap types carry out similar chemistries. Thus, while cap domains play a role in substrate recognition and chemical reactivity, cap‐type does not strictly define functional class. Through this example, we have shown that CapPredictor is an effective new tool for the study of form and function in protein families where domain insertion occurs. Proteins 2014; 82:1896–1906. © 2014 Wiley Periodicals, Inc.     

Two‐domain analysis of JmjN‐JmjC and PHD‐JmjC lysine demethylases: Detecting an inter‐domain evolutionary stress

Residues at different positions of a multiple sequence alignment sometimes evolve together, due to a correlated structural or functional stress at these positions. Co‐evolution has thus been evidenced computationally in multiple proteins or protein domains. Here, we wish to study whether an evolutionary stress is exerted on a sequence alignment across protein domains, i.e., on longer sequence separations than within a single protein domain. JmjC‐containing lysine demethylases were chosen for analysis, as a follow‐up to previous studies; these proteins are important multidomain epigenetic regulators. In these proteins, the JmjC domain is responsible for the demethylase activity, and surrounding domains interact with histones, DNA or partner proteins. This family of enzymes was analyzed at the sequence level, in order to determine whether the sequence of JmjC‐domains was affected by the presence of a neighboring JmjN domain or PHD finger in the protein. Multiple positions within JmjC sequences were shown to have their residue distributions significantly altered by the presence of the second domain. Structural considerations confirmed the relevance of the analysis for JmjN‐JmjC proteins, while among PHD‐JmjC proteins, the length of the linker region could be correlated to the residues observed at the most affected positions. The correlation of domain architecture with residue types at certain positions, as well as that of overall architecture with protein function, is discussed. The present results thus evidence the existence of an across‐domain evolutionary stress in JmjC‐containing demethylases, and provide further insights into the overall domain architecture of JmjC domain‐containing proteins.     

Accurate domain identification with structure‐anchored hidden Markov models,saHMMs

The ever increasing speed of DNA sequencing widens the discrepancy between the number of known gene products, and the knowledge of their function and structure. Proper annotation of protein sequences is therefore crucial if the missing information is to be deduced from sequence‐based similarity comparisons. These comparisons become exceedingly difficult as the pairwise identities drop to very low values. To improve the accuracy of domain identification, we exploit the fact that the three‐dimensional structures of domains are much more conserved than their sequences. Based on structure‐anchored multiple sequence alignments of low identity homologues we constructed 850 structure‐anchored hidden Markov models (saHMMs), each representing one domain family. Since the saHMMs are highly family specific, they can be used to assign a domain to its correct family and clearly distinguish it from domains belonging to other families, even within the same superfamily. This task is not trivial and becomes particularly difficult if the unknown domain is distantly related to the rest of the domain sequences within the family. In a search with full length protein sequences, harbouring at least one domain as defined by the structural classification of proteins database (SCOP), version 1.71, versus the saHMM database based on SCOP version 1.69, we achieve an accuracy of 99.0%. All of the few hits outside the family fall within the correct superfamily. Compared to Pfam_ls HMMs, the saHMMs obtain about 11% higher coverage. A comparison with BLAST and PSI‐BLAST demonstrates that the saHMMs have consistently fewer errors per query at a given coverage. Within our recommended E‐value range, the same is true for a comparison with SUPERFAMILY. Furthermore, we are able to annotate 232 proteins with 530 nonoverlapping domains belonging to 102 different domain families among human proteins labelled “unknown” in the NCBI protein database. Our results demonstrate that the saHMM database represents a versatile and reliable tool for identification of domains in protein sequences. With the aid of saHMMs, homology on the family level can be assigned, even for distantly related sequences. Due to the construction of the saHMMs, the hits they provide are always associated with high quality crystal structures. The saHMM database can be accessed via the FISH server at http://babel.ucmp.umu.se/fish/ . Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Manual classification strategies in the ECOD database

ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up‐to‐date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) each week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi‐domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert‐driven analysis. Proteins 2015; 83:1238–1251. © 2015 Wiley Periodicals, Inc.     

Pfam: A comprehensive database of protein domain families based on seed alignments

Databases of multiple sequence alignments are a valuable aid to protein sequence classification and analysis. One of the main challenges when constructing such a database is to simultaneously satisfy the conflicting demands of completeness on the one hand and quality of alignment and domain definitions on the other. The latter properties are best dealt with by manual approaches, whereas completeness in practice is only amenable to automatic methods. Herein we present a database based on hidden Markov model profiles (HMMs), which combines high quality and completeness. Our database, Pfam, consists of parts A and B. Pfam-A is curated and contains well-characterized protein domain families with high quality alignments, which are maintained by using manually checked seed alignments and HMMs to find and align all members. Pfam-B contains sequence families that were generated automatically by applying the Domainer algorithm to cluster and align the remaining protein sequences after removal of Pfam-A domains. By using Pfam, a large number of previously unannotated proteins from the Caenorhabditis elegans genome project were classified. We have also identified many novel family memberships in known proteins, including new kazal, Fibronectin type III, and response regulator receiver domains. Pfam-A families have permanent accession numbers and form a library of HMMs available for searching and automatic annotation of new protein sequences. Proteins: 28:405–420, 1997. © 1997 Wiley-Liss, Inc.     

SnapDRAGON: a method to delineate protein structural domains from sequence data

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.     

Systematic and Comparative Evaluation of Software Programs for Template‐Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template‐based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile‐based method, especially PSI‐BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS‐MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.     

A new size-independent score for pairwise protein structure alignment and its application to structure classification and nucleic-acid binding prediction

A structure alignment program aligns two structures by optimizing a scoring function that measures structural similarity. It is highly desirable that such scoring function is independent of the sizes of proteins in comparison so that the significance of alignment across different sizes of the protein regions aligned is comparable. Here, we developed a new score called SP‐score that fixes the cutoff distance at 4 Å and removed the size dependence using a normalization prefactor. We further built a program called SPalign that optimizes SP‐score for structure alignment. SPalign was applied to recognize proteins within the same structure fold and having the same function of DNA or RNA binding. For fold discrimination, SPalign improves sensitivity over TMalign for the chain‐level comparison by 12% and over DALI for the domain‐level comparison by 13% at the same specificity of 99.6%. The difference between TMalign and SPalign at the chain level is due to the inability of TMalign to detect single domain similarity between multidomain proteins. For recognizing nucleic acid binding proteins, SPalign consistently improves over TMalign by 12% and DALI by 31% in average value of Mathews correlation coefficients for four datasets. SPalign with default setting is 14% faster than TMalign. SPalign is expected to be useful for function prediction and comparing structures with or without domains defined. The source code for SPalign and the server are available at http://sparks.informatics.iupui.edu . Proteins 2012;. © 2012 Wiley Periodicals, Inc.     

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.     

Exhaustive enumeration of protein domain families

Domains are considered as the basic units of protein folding, evolution, and function. Decomposing each protein into modular domains is thus a basic prerequisite for accurate functional classification of biological molecules. Here, we present ADDA, an automatic algorithm for domain decomposition and clustering of all protein domain families. We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model. In all, 90% of domain boundaries are predicted within 10% of domain size when compared with the manual domain definitions given in the SCOP database. A representative database of 249,264 protein sequences were decomposed into 450,462 domains. These domains were clustered on the basis of sequence similarities into 33,879 domain families containing at least two members with less than 40% sequence identity. Validation against family definitions in the manually curated databases SCOP and PFAM indicates almost perfect unification of various large domain families while contamination by unrelated sequences remains at a low level. The global survey of protein-domain space by ADDA confirms that most large and universal domain families are already described in PFAM and/or SMART. However, a survey of the complete set of mobile modules leads to the identification of 1479 new interesting domain families which shuffle around in multi-domain proteins. The data are publicly available at ftp://ftp.ebi.ac.uk/pub/contrib/heger/adda.     

A computational method for the design of nested proteins by loop‐directed domain insertion

The computational design of novel nested proteins—in which the primary structure of one protein domain (insert) is flanked by the primary structure segments of another (parent)—would enable the generation of multifunctional proteins. Here we present a new algorithm, called Loop‐Directed Domain Insertion (LooDo), implemented within the Rosetta software suite, for the purpose of designing nested protein domain combinations connected by flexible linker regions. Conformational space for the insert domain is sampled using large libraries of linker fragments for linker‐to‐parent domain superimposition followed by insert‐to‐linker superimposition. The relative positioning of the two domains (treated as rigid bodies) is sampled efficiently by a grid‐based, mutual placement compatibility search. The conformations of the loop residues, and the identities of loop as well as interface residues, are simultaneously optimized using a generalized kinematic loop closure algorithm and Rosetta EnzymeDesign, respectively, to minimize interface energy. The algorithm was found to consistently sample near‐native conformations and interface sequences for a benchmark set of structurally similar but functionally divergent domain‐inserted enzymes from the α/β hydrolase superfamily, and discriminates well between native and nonnative conformations and sequences, although loop conformations tended to deviate from the native conformations. Furthermore, in cross‐domain placement tests, native insert‐parent domain combinations were ranked as the best‐scoring structures compared to nonnative domain combinations. This algorithm should be broadly applicable to the design of multi‐domain protein complexes with any combination of inserted or tandem domain connections.     

The sequence determinants of cadherin molecules

The sequence and structural analysis of cadherins allow us to find sequence determinants-a few positions in sequences whose residues are characteristic and specific for the structures of a given family. Comparison of the five extracellular domains of classic cadherins showed that they share the same sequence determinants despite only a nonsignificant sequence similarity between the N-terminal domain and other extracellular domains. This allowed us to predict secondary structures and propose three-dimensional structures for these domains that have not been structurally analyzed previously. A new method of assigning a sequence to its proper protein family is suggested: analysis of sequence determinants. The main advantage of this method is that it is not necessary to know all or almost all residues in a sequence as required for other traditional classification tools such as BLAST, FASTA, and HMM. Using the key positions only, that is, residues that serve as the sequence determinants, we found that all members of the classic cadherin family were unequivocally selected from among 80,000 examined proteins. In addition, we proposed a model for the secondary structure of the cytoplasmic domain of cadherins based on the principal relations between sequences and secondary structure multialignments. The patterns of the secondary structure of this domain can serve as the distinguishing characteristics of cadherins.     

Markovian domain fingerprinting: statistical segmentation of protein sequences.

MOTIVATION: Characterization of a protein family by its distinct sequence domains is crucial for functional annotation and correct classification of newly discovered proteins. Conventional Multiple Sequence Alignment (MSA) based methods find difficulties when faced with heterogeneous groups of proteins. However, even many families of proteins that do share a common domain contain instances of several other domains, without any common underlying linear ordering. Ignoring this modularity may lead to poor or even false classification results. An automated method that can analyze a group of proteins into the sequence domains it contains is therefore highly desirable. RESULTS: We apply a novel method to the problem of protein domain detection. The method takes as input an unaligned group of protein sequences. It segments them and clusters the segments into groups sharing the same underlying statistics. A Variable Memory Markov (VMM) model is built using a Prediction Suffix Tree (PST) data structure for each group of segments. Refinement is achieved by letting the PSTs compete over the segments, and a deterministic annealing framework infers the number of underlying PST models while avoiding many inferior solutions. We show that regions of similar statistics correlate well with protein sequence domains, by matching a unique signature to each domain. This is done in a fully automated manner, and does not require or attempt an MSA. Several representative cases are analyzed. We identify a protein fusion event, refine an HMM superfamily classification into the underlying families the HMM cannot separate, and detect all 12 instances of a short domain in a group of 396 sequences. CONTACT: jill@cs.huji.ac.il; tishby@cs.huji.ac.il.     

人、猴、兔BPI活性部位基因的克隆和序列分析

为比较不同动物来源杀菌／通透性增加蛋白（BPI）的活性部位在基因组成上的差异,为今后对人BPI进行分子改构打下基础,分别从人、恒河猴和家兔外周血白细胞中克隆出BPI全长或活性部位基因,进行序列分析和比较,并进行蛋白二级结构预测。结果发现：猴、兔BPI活性部位序列与人序列在核苷酸水平的同源性分别为94％和77％,在氨基酸水平的同源性分别为88％和62％;人氨基酸序列中包含一个糖基化位点,而猴、兔序列没有;兔序列比人、猴序列少一个氨基酸;3种BPI活性部位有近似的二级结构。     

Sequence and domain arrangements influence mechanical properties of elastin‐like polymeric elastomers

Elastin is the polymeric, extracellular matrix protein that provides properties of extensibility and elastic recoil to large arteries, lung parenchyma, and other tissues. Elastin assembles by crosslinking through lysine residues of its monomeric precursor, tropoelastin. Tropoelastin, as well as polypeptides based on tropoelastin sequences, undergo a process of self‐assembly that aligns lysine residues for crosslinking. As a result, both the full‐length monomer as well as elastin‐like polypeptides (ELPs) can be made into biomaterials whose properties resemble those of native polymeric elastin. Using both full‐length human tropoelastin (hTE) as well as ELPs, we and others have previously reported on the influence of sequence and domain arrangements on self‐assembly properties. Here we investigate the role of domain sequence and organization on the tensile mechanical properties of crosslinked biomaterials fabricated from ELP variants. In general, substitutions in ELPs involving similiar domain types (hydrophobic or crosslinking) had little effect on mechanical properties. However, modifications altering either the structure or the characteristic sequence style of these domains had significant effects on such properties. In addition, using a series of deletion and replacement constructs for full‐length hTE, we provide new insights into the role of conserved domains of tropoelastin in determining mechanical properties. © 2012 Wiley Periodicals, Inc. Biopolymers 99: 392–407, 2013.     

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.     

Searching for remote homologs of CAML among eukaryotes

The tryptophan rich basic protein/calcium signal‐modulating cyclophilin ligand (WRB/CAML) and Get1p/Get2p complexes, in vertebrates and yeast, respectively, mediate the final step of tail‐anchored protein insertion into the endoplasmic reticulum membrane via the Get pathway. While WRB appears to exist in all eukaryotes, CAML homologs were previously recognized only among chordates, raising the question as to how CAML's function is performed in other phyla. Furthermore, whereas WRB was recognized as the metazoan homolog of Get1, CAML and Get2, although functionally equivalent, were not considered to be homologous. CAML contains an N‐terminal basic, TRC40/Get3‐interacting, region, three transmembrane segments near the C‐terminus, and a poorly conserved region between these domains. Here, I searched the NCBI protein database for remote CAML homologs in all eukaryotes, using position‐specific iterated‐basic local alignment search tool, with the C‐terminal, the N‐terminal or the full‐length sequence of human CAML as query. The N‐terminal basic region and full‐length CAML retrieved homologs among metazoa, plants and fungi. In the latter group several hits were annotated as GET2. The C‐terminal query did not return entries outside of the animal kingdom, but did retrieve over one hundred invertebrate metazoan CAML‐like proteins, which all conserved the N‐terminal TRC40‐binding domain. The results indicate that CAML homologs exist throughout the eukaryotic domain of life, and suggest that metazoan CAML and yeast GET2 share a common evolutionary origin. They further reveal a tight link between the particular features of the metazoan membrane‐anchoring domain and the TRC40‐interacting region. The list of sequences presented here should provide a useful resource for future studies addressing structure‐function relationships in CAML proteins.