Protein sequence motif patterns using adaptive Fuzzy C-Means granular computing model

Region adaptive morphological reconstruction fuzzy C-means for near-field 3-D SAR image target extraction

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.

In biology the notion of motif plays a central role for describing various phenomena. For example, protein functional motifs, like the ones contained in the PROSITE database [20], e.g. [FY ]DPC[LIM ][ASG]C[ASG] , are in general represented as motifs with character classes. These motifs are collected using semi-automatic procedures, nevertheless they are still manually verified. The discovery of sequence motifs in proteins and genes is becoming increasingly important [2, 29]. Such motifs usually correspond to residues conserved during evolution due to some significant structural or functional role. Moreover the increasing availability of biological sequences, such as whole genomes, from next-generation sequencing technologies to new protein discoveries, has increased the need for automatic methods for their analysis and comparison. In order to fill this gap, researchers have developed several approaches over the years. Typically these approaches follow in popular frameworks of motif discovery

A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned Pattern (AP) Cluster to discover potential functional segments in protein sequences. We apply our algorithm to identify and display the binding segments for the Cytochrome C. and Ubiquitin protein families. The resulting AP Clusters correspond to protein binding segments that surround the binding residues. When compared to the results from the protein annotation databases, PROSITE and pFam, ours are more efficient in computation and comprehensive in quality. The significance of the AP Cluster is that it is able to capture subtle variations of the binding segments in protein families. It thus could help to reduce time-consuming simulations and experimentation in the protein analysis.KeywordsProtein AnalysisProtein Function IdentificationPattern DiscoveryPattern ClusteringHierarchical ClusteringMotif FindingLocal AlignmentApproximate String Matching

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.

Identification of conserved domains or motifs in proteins may aid in the localization and analysis of important structural and functional regions. We report here a protein sequence motif, called the bromodomain (1), that has been found in six genes from humans (CCG1 and RING3), Drosophila (fsh and brm), and yeast (SPT7 and SNF2). The fsh and brm genes are required maternally for proper expression of certain homeotic genes (1,2). The SPT7 and SNF2 genes of Saccharomyces cerevisiae encode transcriptional activators (3,4). The SNF2 and brm proteins have extensive sequence homology (1). It is not clear whether the two human genes are involved in processes. CCG1 is a DNA-binding protein that complements temperature sensitive mutations that cause cell cycle arrest in Gl (5). The RING3 gene is a newly identified human gene of unknown function, mapping to the class II region of the human major histocompatibility locus, that has substantial homology to the fsh gene (6). Each of these proteins has one or two copies of the 61 —63 amino acid bromodomain (Figure 1). The sequence identity is highest (~ 80%) between the corresponding fsh and RING3 repeats. More typically, any two motifs show 25-40% identity, with 50-60% identity between two repeats within the same protein. There are seven invariant residues, four of which are aromatic amino acids, and numerous conservative substitutions. The location of the motif(s) within the individual proteins is variable, and two motifs may be present in tandem or separated by sequences unrelated to the motif. Secondary structure prediction methods were applied to the bromodomain and revealed two strongly predicted amphipathic a helices followed by reverse turns. Four of the invariant residues are located within the turns, and the other three are located among four highly conserved proline residues at the N terminus. The functional significance of the bromodomain is unknown, and experiments with the two yeast proteins indicate that it may be dispensable in some cases (6; P.Tan and F.W., unpublished data). However, the widespread conservation of the bromodomain suggests that it is important for some aspects of protein function. We speculate that the hydrophobic surfaces of the helices and the invariant hydrophobic residues could serve as sites of intramolecular or intermolecular protein-protein interaction. Such interactions could influence the assembly or activity of multicomponent complexes involved in transcription activation or other cellular processes.

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://toyamaailab.github.io/sourcedata.html</uri> .

Communication failures of digital substations can result in loss of sampled information data. Information reconstruction algorithms are often used to reconstruct missing sample data, which causes errors in the amplitude and phase of the Fourier filter calculation. Studies have shown that the amount and location of sampled data loss has a direct impact on the size of the reconstruction error. Combining the singular value decomposition and information entropy theory, this paper comprehensively considers the influencing factors of reconstruction error, studies the correlation between reconstruction error and sampling data singular entropy, and proposes a reconstruction error estimation method based on singular entropy. This method is used to evaluate the reconstruction errors of three interpolation methods.

Calmodulin, regulatory, and essential myosin light chain are evolutionary conserved proteins that, by binding to IQ motifs of target proteins, regulate essential intracellular processes among which are efficiency of secretory vesicles release at synapsis, intracellular signaling, and regulation of cell division. The yeast Saccharomyces cerevisiae calmodulin Cmd1 and the essential myosin light chain Mlc1p share the ability to interact with the class V myosin Myo2p and Myo4 and the class II myosin Myo1p. These myosins are required for vesicle, organelle, and mRNA transport, spindle orientation, and cytokinesis. We have used the budding yeast model system to study how calmodulin and essential myosin light chain selectively regulate class V myosin function. NMR structural analysis of uncomplexed Mlc1p and interaction studies with the first three IQ motifs of Myo2p show that the structural similarities between Mlc1p and the other members of the EF-hand superfamily of calmodulin-like proteins are mainly restricted to the C-lobe of these proteins. The N-lobe of Mlc1p presents a significantly compact and stable structure that is maintained both in the free and complexed states. The Mlc1p N-lobe interacts with the IQ motif in a manner that is regulated both by the IQ motifs sequence as well as by light chain structural features. These characteristic allows a distinctive interaction of Mlc1p with the first IQ motif of Myo2p when compared with calmodulin. This finding gives us a novel view of how calmodulin and essential light chain, through a differential binding to IQ1 of class V myosin motor, regulate this activity during vegetative growth and cytokinesis.

Complexity versus regularity is an important component of appropriate joint position to retain balance but has not received much attention. The Singular value decomposition entropy (SvdEn) characterizes information content or regularity of a signal depending on the number of vectors attributed to the process. The current study aimed to investigate the effect of kinesiology tape compared to static strapping tape and no tape on ankle joint dynamics during the Y balance test. Forty-one participants (21 males; 20 females) aged between 18 and 34 years of age completed the Y-balance test with kinesiology tape, with strapping tape and without tape applied to the dominant leg. SvdEn was obtained from center of pressure values, as well as ankle and knee movement variability during the Y balance test. Center of pressure and knee joint dynamics did not change significantly between the two taped and no tape conditions during the YBT. Ankle joint SvdEn was significantly lower in the anterior-posterior (p<; .05) and superior-inferior (p<; .001) direction for both tape conditions compared to no tape. Greater regularity in the ankle joint dynamics indicates less vectors are required to describe the signal, which can be interpreted from a neurophysiological perspective as a decrease in feedforward and/or feedback input along the hierarchical sensorimotor processing pathway as an adjustment to taping and a possibly more reflex oriented response localised at the spinal cord level.

We have searched for the exclusivity of common sequence motifs of the mitochondrial uncoupling proteins (UCP1, UCP2, UCP3, UCP4, BMCP1, and plant UCP [PUMP]) within the gene family of mitochondrial anion carrier proteins. The UCP-specific sequences, "UCP signatures", were found in the first, second, and fourth alpha-helices. First: Ala/Ser-Cys/Thr/n-n/Phe-Ala/Gly-[negatively charged residue]-n/Phe-n/Cys-Thr-Phe/n; second: Gly/Ala-Ile/Leu-Gln/X-[positively charged residue]-NH-n/Cys-Ser/nphi/X-n/Ser-OH/Gly-n-[positively charged residue]-Ile/Met-Gly/Val-n/Thr; fourth: Pro-Asn/ Thr-n-X-[positively charged residue]-Asn/Ser/Ala-n-n-Ile/Leu-n-Asn/Val-Cys/n-n/Thr-[negatively charged residue]-n-n/Thr/Pro-OH/Val (n, nonpolar; phi, aromatic; (positively charged residue/negatively charged residue, charged residue). The second and part of the third signature are also present in the yeast dicarboxylate transporter. The UCP signature excluding BMCP1 was also found in the second matrix segment: [positively charged residue]-(Pro/ del-Leu/del)-[positively charged residue]-phi-X-Gly/Ser-Thr/n-X-NH/[negatively charged residue]-Ala-phi. These UCP signatures are thought to be involved in fatty acid anion binding and translocation.

9] Profile analysis

Chapter 1 of this thesis provides an overview of the structure and function of proteins as well as their investigation via NMR spectroscopy. Proteins are one of the most important building blocks of life. They are part of every organism and fulfill a variety of functions including, for example, the catalysis of many chemical reactions, which is the reason various processes in an organism become possible in the first place. Proteins are made up of amino acids, which are linked together via peptide bonds during the protein biosynthesis. The 22 proteinogenic amino acids result in an almost unlimited number of sequential combinations that give proteins their versatility. The sequence of amino acids in a protein is also called the primary structure. From this, locally limited structural motifs such as α-helices and β-sheets are formed, representing the secondary structure. Further intramolecular interactions create the tertiary structure of a protein, which can be seen as the overall three-dimensional structure of the protein. Furthermore, several individual proteins can assemble into a larger complex to form a quaternary structure. There are various methods for investigating the structure of proteins, including nuclear magnetic resonance (NMR) spectroscopy. In NMR spectroscopy, atomic nuclei with a non-zero nuclear spin are polarized by a constant magnetic field and then excited using radio frequency pulses. The emitted resonance frequencies are detected in the form of a free induction decay (FID) and converted into an NMR spectrum by a Fourier transformation. Many NMR experiments are available for researching proteins. By default, a two-dimensional 1H15N-HSQC spectrum is measured, which provides signals for each N-H bond in a protein meaning that one signal is obtained for each N-H in the backbone of the amino acids. Using 3D NMR experiments, these signals can then be assigned to the respective amino acids in the protein sequence. This makes it possible to investigate the dynamics of proteins as well as the formation of complexes with a ligand or another biomacromolecule if they interact. Further 3D NMR experiments also make it possible to assign the side-chains of the protein. With the help of the determined resonance assignments and spatial information from various NOESY spectra, a protein structure can then be calculated using a suitable computer program. Recently, the prediction of protein structures using AI (artificial intelligence)-supported programs has also become more prevalent. In particular, the artificial neural network AlphaFold has been extremely successful in predicting correct structures without any experimental data provided by the user. Chapter 2 of this thesis introduces the research field of small proteins. These are proteins with a length of usually less than 100 amino acids, although definitions in literature vary and upper limits of, for example, 50 or 200 amino acids exist as well. Various other names are also used to describe small proteins, such as μ-proteins or sORF-encoded peptides. The field of small proteins is still relatively new, as they were overlooked in research for many years. One reason for this was the lower limit of 100 codons that was set for the identification of open reading frames (ORFs) in genome projects. This served to exclude numerous biologically irrelevant ORFs from genome annotation. However, this meant that functional ORFs coding for small proteins were also ignored. Another reason was that experimental methods were not designed for the analysis of small proteins, meaning they could often not be detected at all. However, new analytical methods such as ribosome profiling and a generally increased focus on the detection of small proteins have led to their detection in all domains of life. In the research group of Prof. Dr. Schwalbe, small proteins of various prokaryotes were structurally characterized as part of a special priority program by the DFG. NMR spectroscopy was primarily used for this purpose, as it is the method of choice for the investigation of small proteins. The results of this research were published in several articles, two of which are part of this dissertation.

Given a sequence of i.i.d. integer valued random variables, we derive the distribution of the minimum sum of any W consecutive variables. We apply these results to evaluate the unusualness of matches (perfect or almost perfect) in multiply aligned sequences, and matches in all possible alignments of sequences.

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.