A Framework for Direct Locating and Conformational Sampling of Protein Structural Motifs

A methodology to analyze and characterize small- and large-scale shape and structural features in a macromolecule is presented. The procedure involves the construction of a continuum of spherical shape maps. This continuum is built by analyzing the overcrossing pattern of a molecular space curve from every direction in 3-space as seen by a viewer with a reduced vision field. The method derives global shape descriptors (shape maps) that characterize the backbone as a whole (large-scale structure), but also allows one to focus the analysis on details of the backbone's small-scale structure. A continuous function is proposed as a simplified descriptor derived from the shape maps. This function discriminates reasonably well among elements of secondary structure and supersecondary structural motifs in proteins. The procedure is applied to α-helices, β-sheets, and models of α-helical packing.

BackgroundProfile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available.ResultsWe develop a visual editor for profile Hidden Markov Models (HMMEditor). HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path.ConclusionHMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

9] Profile analysis

The current work has deduced the novel method for tertiary structure prediction of various important unpredicted proteins through machine learning technique neural network. Multi-layer perceptron architecture has been developed to predict the tertiary structure (Phi/Psi) of proteins. A novel binary codification system has been devised for input and output processing. Twenty physiochemical properties representation scheme for each amino acid and binary output discretization of real valued torsion angle for each angle of residues has been adopted. The proposed system has been tested with different number of neural networks, training set sizes and training epochs. The overall successful prediction of residues for tertiary structure prediction (Phi/Psi) of protein has been reported according to window size as 9(52.4% / 56.2%), 13(56.5% / 61.3%), 17(53.7% / 57.2%), 21(53.2% / 57.4%). This study demonstrated the prospect of implementing fast and efficient structure prediction of peptide sequences using neural network.

BackgroundThe actin cytoskeleton is a hallmark of eukaryotic cells. Its regulation as well as its interaction with other proteins is carefully orchestrated by actin interaction domains. One of the key players is the WH2 motif, which enables binding to actin monomers and filaments and is involved in the regulation of actin nucleation. Contrasting conserved domains, the identification of this motif in protein sequences is challenging, as it is short and poorly conserved.FindingsTo identify divergent members, we combined Hidden-Markov-Model (HMM) to HMM alignments with orthology predictions. Thereby, we identified nearly 500 proteins containing so far not annotated WH2 motifs. This included shootin-1, an actin binding protein involved in neuron polarization. Among others, WH2 motifs of ‘proximal to raf’ (ptr)-orthologs, which are described in the literature, but not annotated in genome databases, were identified.ConclusionIn summary, we increased the number of WH2 motif containing proteins substantially. This identification of candidate regions for actin interaction could steer their experimental characterization. Furthermore, the approach outlined here can easily be adapted to the identification of divergent members of further domain families.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-0981-7) contains supplementary material, which is available to authorized users.

Protein structure prediction is often assisted by predicting one-dimensional structural properties including relative solvent accessibility (RSA) surface and backbone torsion angles (BTA) of residues, and these two properties are continuously varying variables because proteins can move freely in a three-dimensional space. Instead of subdividing them into a few arbitrarily defined states that many popular approaches used, this paper proposes an integrated system for realvalue prediction of protein structural motifs’ two local properties, based on the modified Hidden Markov Model that we previously presented. The model was used to capture the relevance of RSA and the dependency of BTA between adjacent residues along the local protein chain in motifs with definite probabilities. These two properties were predicted according to their own probability distribution. The method was applied to a protein fragment library. For nine different classes of motifs, real values of RSA were predicted with mean absolute error (MAE) of 0.122-0.175 and Pearson’s correlation coefficient (PCC) of 0.623-0.714 between predicted and actual RSA. Meanwhile, real values of BTA were obtained with MAE of 8.50-29.40 for Φ angles, 11.20-38.50 for ψ angles and PCC of 0.601-0.716 for Φ, 0.597-0.713 for ψ. The results were compared with well-known Real-SPINE Server, and indicate the proposed method may at least serve as the foundation to obtain better local properties from structural motifs for protein structure prediction. Keywords: Protein structure predication, structural motifs, hidden Markov model, solvent accessibility surface, backbone torsion angles, directional statistics distribution

Protein structure prediction is one of the most important problems in modern computational biology. Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machine (SVM) etc., to focus on the prediction of the secondary structures. In this paper, a new method was proposed based on SVM. Different from the existing methods, this method takes into account of the physical-chemical properties and structure properties of amino acids. When tested on the most popular dataset CB513, it achieved a Q(3) accuracy of 0.7844, which illustrates that it is one of the top range methods for protein of secondary structure prediction.

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried & which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

RNA molecules take advantage of prevalent structural motifs to fold and assemble into well-defined 3D architectures. The A-minor junction is a class of RNA motifs that specifically controls coaxial stacking of helices in natural RNAs. A sensitive self-assembling supra-molecular system was used as an assay to compare several natural and previously unidentified A-minor junctions by native polyacrylamide gel electrophoresis and atomic force microscopy. This class of modular motifs follows a topological rule that can accommodate a variety of interchangeable A-minor interactions with distinct local structural motifs. Overall, two different types of A-minor junctions can be distinguished based on their functional self-assembling behavior: one group makes use of triloops or GNRA and GNRA-like loops assembling with helices, while the other takes advantage of more complex tertiary receptors specific for the loop to gain higher stability. This study demonstrates how different structural motifs of RNA can contribute to the formation of topologically equivalent helical stacks. It also exemplifies the need of classifying RNA motifs based on their tertiary structural features rather than secondary structural features. The A-minor junction rule can be used to facilitate tertiary structure prediction of RNAs and rational design of RNA parts for nanobiotechnology and synthetic biology.

Reentrant loops are an important structural motif in alpha-helical transmembrane proteins. A reentrant loop is a structural motif that goes only halfway through the membrane and then turns back to the side from which it originates. The question of what causes the reentrant loops to form such a unique topology is still unanswered. In this study, we try to answer this question by analyzing the hydrophobicity distribution on the amino acid sequences of the reentrant loops. Our results show that reentrant loops have very low hydrophobicity around the deepest point buried in the membrane and relative high hydrophobicity close to the membrane surfaces. We speculate that this hydrophobicity distribution is a major force that stabilizes the unique reentrant loop structure. Our results also show that this hydrophobicity distribution results in special patterns on protein sequences, which can be captured using profile hidden Markov models (HMMs). The resulting profile HMMs can detect reentrant loops on protein sequences with high sensitivity and perfect specificity.

Important and relevant information is expected to be encoded in local structural elements of proteins. An unsupervised learning algorithm (Kohonen algorithm) was applied to the representation and unbiased classification of local backbone structures contained in a set of proteins. Training yielded a two-dimensional Kohonen feature map with 100 different structural motifs including certain helical and strand structures. All motifs were represented in a phi-psi-plot and some of them as a three-dimensional model. The course of structural motifs along the backbone of four selected proteins (cytochrome b5, cytochrome b562, lysozyme, gamma crystallin) was investigated in detail. Trajectories and histograms visualizing the abundance of characteristic motifs allowed for the distinction between different types of protein overall folds. It is demonstrated how the histograms may be used to construct a structural similarity matrix for proteins. The Kohonen algorithm provides a simple procedure for classification of local protein structures independent of any a priori knowledge of leading structural motifs. Training of the Kohonen network leads to the generation of "consensus structures' serving for the task of classification.

A simple approach is described for the identification of packing motifs in crystal structures of aromatic molecules using Hirshfeld surfaces and fingerprint plot analysis. The method was first validated by investigating previously studied molecules and establishing trends within these data, and then applied to the analysis of crystal structures of heteroaromatic molecules extracted from the Cambridge Structural Database. A series of cocrystal structures containing these same heteroaromatic molecules were also extracted and investigated using the same method. This simple method appears to be both consistent and robust in distinguishing the different types of packing motifs in structures of aromatic molecules.

Protein structure motif detection is one of the fundamental problems in Structural Bioinformatics. Compared with sequence motifs, structural motifs are more sensitive in detecting the evolutionary relationships among proteins. A variety of algorithms have been proposed to attack this problem. However, they are either heuristic without theoretical performance guarantee, or inefficient for employing an exhaustive search strategy. Here, we study a reasonably restricted version of this problem: the compact structural motif problem. In this paper, we prove that this restricted version is still NP-hard, and we present a polynomial-time approximation scheme to solve it. To the best of our knowledge, this is the first approximation algorithm with a guarantee ratio for the protein structural motif problem.

Protein folding is a hierarchical process where structure forms locally first, then globally. Some short sequence segments initiate folding through strong structural preferences that are independent of their three-dimensional context in proteins. We have constructed a knowledge-based force field in which the energy functions are conditional on local sequence patterns, as expressed in the hidden Markov model for local structure (HMMSTR). Carbon-alpha force field (CALF) builds sequence specific statistical potentials based on database frequencies for alpha-carbon virtual bond opening and dihedral angles, pair-wise contacts and hydrogen bond donor-acceptor pairs, and simulates folding via Brownian dynamics. We introduce hydrogen bond donor and acceptor potentials as alpha-carbon probability fields that are conditional on the predicted local sequence. Constant temperature simulations were carried out using 27 peptides selected as putative folding initiation sites, each 12 residues in length, representing several different local structure motifs. Each 0.6 micros trajectory was clustered based on structure. Simulation convergence or representativeness was assessed by subdividing trajectories and comparing clusters. For 21 of the 27 sequences, the largest cluster made up more than half of the total trajectory. Of these 21 sequences, 14 had cluster centers that were at most 2.6 A root mean square deviation (RMSD) from their native structure in the corresponding full-length protein. To assess the adequacy of the energy function on nonlocal interactions, 11 full length native structures were relaxed using Brownian dynamics simulations. Equilibrated structures deviated from their native states but retained their overall topology and compactness. A simple potential that folds proteins locally and stabilizes proteins globally may enable a more realistic understanding of hierarchical folding pathways.

The enzyme 3-hydroxy-3-methylglutaryl CoA reductase (HMG-CoA reductase or HMGR) catalyzes the conversion of HMG-CoA to mevalonate, which is the rate-limiting step in cholesterol and isoprenoid biosynthesis. HMG-CoA reductase (HMGR) is the main target of statins, which are commonly used medicines to lower cholesterol and prevent heart-related diseases. However, the use of statins may lead to harmful side effects, like muscle pain (myopathy) and muscle breakdown (rhabdomyolysis), creating a demand for alternative therapeutic options. This study aims to analyze and predict the 3D structure of HMGR from 12 different species, such as humans, fruit flies, plants, and protists, using a computer-based method to evaluate their potential for future drug design. Protein sequences were retrieved from the NCBI database and analyzed using various bioinformatics tools. The physicochemical properties and amino acid composition were computed by ExPASy's ProtParam tool, and the secondary structural features were determined by SOPMA and PHD servers. ClustalW, CELLO, and MEME, respectively, predicted the phylogenetic tree, subcellular localization, and structural motifs. Homology modeling was performed using the Swiss-Model Workspace with selected templates and visualized with PyMOL software. The predicted models were evaluated with PROCHECK analysis by Ramachandran plot and VERIFY 3D, and ERRAT values using the SAVES server. The aliphatic index and GRAVY values indicated that most HMGRs were thermally stable and hydrophobic. Among the selected HMG-CoA reductase sequences, the alpha helix dominated over the extended strand, beta turn, and random coil, according to the secondary structural properties. The phylogenetic analysis ensured the relationship among experimental species, and the predicted motifs indicated uniformity in the conserved sequences. The result of the predicted model indicated good quality and high reliability of the protein for most species, particularly for H. sapiens and D. melanogaster. This in silico study provides valuable insights into the structural and functional aspects of HMGR across species, laying a strong foundation for the development of safer and more effective cholesterol-lowering medications in the future.