Aligning Discovered Patterns from Protein Family Sequences

A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since aligned residues associations (ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function, factors, and variance in the source environment, we have recently developed a novel method: Aligned Residue Association Discovery and Disentanglement (ARADD) to solve this problem. ARADD first obtains from an APC an ARA Frequency Matrix and converts it to an adjusted statistical residual vector space (SRV). It then disentangles the SRV into Principal Components (PCs) and Re-projects their vectors to a SRV to reveal succinct orthogonal AR groups. In this study, we applied ARADD to class A scavenger receptors (SR-A), a subclass of a diverse protein family binding to modified lipoproteins with diverse biological functionalities not explicitly known. Our experimental results demonstrated that ARADD can unveil subtle subgroups in sequence segments with diverse functionality and highly variable sequence lengths. We also demonstrated that the ARAs captured in a Position Weight Matrix or an APC were entangled in biological function and domain location but disentangled by ARADD to reveal different subclasses without knowing their actual occurrence positions.

Data Mining is the process to extract hidden predictive information from large databases. In Bioinformatics, data mining enables researchers to meet the challenge of mining large amount of biomolecular data to discover real knowledge. Major research efforts done in the area of bioinformatics involves sequence analysis, protein structure prediction and gene finding. Proteins are said to be prominent molecules in our cells. They involve virtually in all cell functions. The activities and functions of proteins can be determined by protein sequence motifs. These protein motifs are identified from the segments of protein sequences. All segments may not be important to produce good motif patterns. The generated sequence segments do not have classes or labels. Hence, unsupervised segment selection technique is adopted to select significant segments. Therefore Singular Value Decomposition (SVD) entropy method is adopted to select significant sequence segments. In this proposed work, weighted K-Means and Adaptive Fuzzy C-Means have been applied to the selected segments to generate granules, since large amount of segments cannot be grouped or clustered as such. Each granules generated by weighted K-Means algorithm are further clustered by using the K-Means algorithm and granules generated by Adaptive Fuzzy C-Means algorithm are clustered by using Weighted K-Means. The two proposed models are compared with K-Means granular computing model. The experimental results show that Adaptive Fuzzy C-Means with Weighted K-Means technique produces better results than K-Means and weighted K-Means granular computing methods.

Identification of transmembrane segments in protein sequences is an important issue in the field of bioinformatics. In this study, a method is proposed for linear discrimination between transmembrane and non-transmembrane segments, combining chemical and statistical features of the proteins with higher-order crossings analysis for protein segment classification. The method was tested on human proteins extracted from public available databases and the results have shown a remarkable efficiency of the proposed algorithm to correctly classify the sequence segments under study into two linearly separated classes, for a wide range of transmembrane segment lengths.

The identification of sequence-based protein domains and their boundaries is often a prelude to structure determination. An accurate prediction of disordered regions, secondary structures and low complexity segments of target protein sequences can improve the efficiency of selection in structural genomics and also aid in design of constructs for directed structural biology studies. At the Center for Eukaryotic Structural Genomics (CESG) we have developed DomainView, a web tool to visualize and analyze predicted protein domains, disordered regions, secondary structures and low complexity segments of target protein sequences for selection of experimental protein structure attempts. DomainView consists of a relational database and a web graphical-user interface. The database was developed based on MySQL, which stores data from target protein sequences and their domains, disordered regions, secondary structures and low complexity segments. The program of the web user interface is a Perl CGI script. When a user searches for a target protein sequence, the script displays the combinational information about the domains and other features of that target sequence graphically on a web page by querying the database. The graphical representation for each feature is linked to a web page showing more detailed annotation information or to a new window directly running the corresponding prediction program to show further information about that feature.

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.

All known terrestrial proteins are coded as continuous strings of ≈20 amino acids. The patterns formed by the repetitions of elements in groups of finite sequences describes the natural architectures of protein families. We present a method to search for patterns and groupings of patterns in protein sequences using a mathematically precise definition for "repetition", an efficient algorithmic implementation and a robust scoring system with no adjustable parameters. We show that the sequence patterns can be well-separated into disjoint classes according to their recurrence in nested structures. The statistics of the occurrences of patterns indicate that short repetitions are sufficient to account for the differences between natural families and randomized groups of sequences by more than 10 standard deviations, while contiguous sequence patterns shorter than 5 residues are effectively random in their occurrences. A small subset of patterns is sufficient to account for a robust "familiarity" definition between arbitrary sets of sequences.

The specificity-determining residue database (SDR database) presents residue positions where mutations are predicted to have changed protein function in large protein families. Because the database pre-calculates predictions on existing protein sequence alignments, users can quickly find the predictions by selecting the appropriate protein family or searching by protein sequence. Predictions can be used to guide mutagenesis or to gain a better understanding of specificity changes in a protein family. The database is available on the web at http://paradox.harvard.edu/sdr.

BackgroundProteins convey the majority of biochemical and cellular activities in organisms. Over the course of evolution, proteins undergo normal sequence mutations as well as large scale mutations involving domain duplication and/or domain shuffling. These events result in the generation of new proteins and protein families. Processes that affect proteome evolution drive species diversity and adaptation. Herein, change over the course of metazoan evolution, as defined by birth/death and duplication/deletion events within protein families and domains, was examined using the proteomes of 9 metazoan and two outgroup species.ResultsIn studying members of the three major metazoan groups, the vertebrates, arthropods, and nematodes, we found that the number of protein families increased at the majority of lineages over the course of metazoan evolution where the magnitude of these increases was greatest at the lineages leading to mammals. In contrast, the number of protein domains decreased at most lineages and at all terminal lineages. This resulted in a weak correlation between protein family birth and domain birth; however, the correlation between domain birth and domain member duplication was quite strong. These data suggest that domain birth and protein family birth occur via different mechanisms, and that domain shuffling plays a role in the formation of protein families. The ratio of protein family birth to protein domain birth (domain shuffling index) suggests that shuffling had a more demonstrable effect on protein families in nematodes and arthropods than in vertebrates. Through the contrast of high and low domain shuffling indices at the lineages of Trichinella spiralis and Gallus gallus, we propose a link between protein redundancy and evolutionary changes controlled by domain shuffling; however, the speed of adaptation among the different lineages was relatively invariant. Evaluating the functions of protein families that appeared or disappeared at the last common ancestors (LCAs) of the three metazoan clades supports a correlation with organism adaptation. Furthermore, bursts of new protein families and domains in the LCAs of metazoans and vertebrates are consistent with whole genome duplications.ConclusionMetazoan speciation and adaptation were explored by birth/death and duplication/deletion events among protein families and domains. Our results provide insights into protein evolution and its bearing on metazoan evolution.

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

Gonad formation requires specific interactions between germ cells and specialized somatic cells, along with the elaborate morphogenetic movements of these cells to create an ovary or testis. We have identified mutations in the fear of intimacy (foi) gene that cause defects in the formation of the embryonic gonad in Drosophila. foi is of particular interest because it affects gonad formation without affecting gonad cell identity, and is therefore specifically required for the morphogenesis of this organ. foi is also required for tracheal branch fusion during tracheal development. E-cadherin/shotgun is similarly required for both gonad coalescence and tracheal branch fusion, suggesting that E-cadherin and FOI cooperate to mediate these processes. foi encodes a member of a novel family of transmembrane proteins that includes the closely related human protein LIV1. Our findings that FOI is a cell-surface protein required in the mesoderm for gonad morphogenesis shed light on the function of this new family of proteins and on the molecular mechanisms of organogenesis.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

With the quick development of gene sequencing technology, the explosion age marked by protein sequences has already come. How to deal with a huge number of protein sequences has aroused serious concern in the research field. An effective solution is to cluster homologous sequences into separated protein families. Those proteins that are affiliated to the same protein family share the similar structure and/or the functionality of genes. The known proteins will facilitate to identify various valuable evidences for discovering the unknown proteins. We present an efficient and effective algorithm called Expanding Clustered Protein Families (ECPF), which could skilfully optimise the clustered protein sequences. The results show that ECPF is capable of discovering the unknown connections between storing space and families in large-scale databases while consuming acceptable overhead of computational time. ECPF successfully expands the protein sequence network and furthermore creates a more practical protein sequence topology for promoting biological research.

The gene Rv0813c from Mycobacterium tuberculosis, which codes for a hypothetical protein of unknown function, is conserved within the order Actinomycetales but absent elsewhere. The crystal structure of Rv0813c reveals a new family of proteins that resemble the fatty acid-binding proteins (FABPs) found in eukaryotes. Rv0813c adopts the 10-stranded beta-barrel fold typical of FABPs but lacks the double-helix insert that covers the entry to the binding site in the eukaryotic proteins. The barrel encloses a deep cavity, at the bottom of which a small cyclic ligand was found to bind to the hydroxyl group of Tyr192. This residue is part of a conserved Arg-X-Tyr motif much like the triad that binds the carboxylate group of fatty acids in FABPs. Most of the residues forming the internal surface of the cavity are conserved in homologous protein sequences found in CG-rich prokaryotes, strongly suggesting that Rv0813c is a member of a new family of bacterial FABP-like proteins that may have roles in the recognition, transport, and/or storage of small molecules in the bacterial cytosol.

Since leucine-rich repeat (LRR) is known as a domain of protein specific to disease resistance genes, it is used to study the genes of rice with resistance to bacterial blight caused by Xanthomonas oryzae pv. oryzae. The CDS (coding domain sequence) and full-length cDNA sequences of the completely sequenced genes were obtained through the access from public databases of NCBI (National Center for Biotechnology Information) and KOME (Knowledge-based Oryza Molecular Biological Encyclopedia) and they were used to identify each LRR fragment in translated protein sequences through Pfam protein families database. Results showed that only Xa1, Xa21 and Xa26 genes were found to possess LRRs in their protein sequences. When bioinformatics and statistical methods were used for further comparative analysis of distribution and amino acid composition for these LRRs among Xa protein sequences, LRRs of Xa1 were loosely located after NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) in the protein sequences, whereas LRRs of Xa21 and Xa26 were densely located before NB-ARC or P-kinase (protein-kinase). It appeared that the first one or two LRRs of the gene Xa1 proteins in proximity to NB-ARC were distant from the other LRRs, but no similar phenomenon was observed within the protein sequences of the Xa21 and Xa26 genes. The LRRs of these three Xa genes mainly consisted of hydrophilic amino acids, with the highest frequency for leucine, followed by neutral serine. The LRRs of Xa1 protein sequences had different frequencies for some amino acids compared to LRRs of Xa21 and Xa26. These findings are useful in understanding of the structural variation of LRRs in protein sequences among Xa genes with resistance to bacterial blight of rice.

Tandem repeats in proteins are patterns of residues repeated directly adjacent to each other. The evolution of these repeats can be assessed by using groups of homologous sequences, which can help pointing to events of unit duplication or deletion. High pressure in a protein family for variation of a given type of repeat might point to their function. Here, we propose the analysis of protein families to calculate protein short tandem repeats (pSTRs) in each protein sequence and assess their variability within the family in terms of number of units. To facilitate this analysis, we developed the pSTR tool, a method to analyze the evolution of protein short tandem repeats in a given protein family by pairwise comparisons between evolutionarily related protein sequences. We evaluated pSTR unit number variation in protein families of 12 complete metazoan proteomes. We hypothesize that families with more dynamic ensembles of repeats could reflect particular roles of these repeats in processes that require more adaptability.