Escherichia coli adenylate kinase dynamics: comparison of elastic network model modes with mode-coupling (15)N-NMR relaxation data

The dynamics of adenylate kinase of Escherichia coli (AKeco) and its complex with the inhibitor AP(5)A, are characterized by correlating the theoretical results obtained with the Gaussian Network Model (GNM) and the anisotropic network model (ANM) with the order parameters and correlation times obtained with Slowly Relaxing Local Structure (SRLS) analysis of (15)N-NMR relaxation data. The AMPbd and LID domains of AKeco execute in solution large amplitude motions associated with the catalytic reaction Mg(+2)*ATP + AMP --> Mg(+2)*ADP + ADP. Two sets of correlation times and order parameters were determined by NMR/SRLS for AKeco, attributed to slow (nanoseconds) motions with correlation time tau( perpendicular) and low order parameters, and fast (picoseconds) motions with correlation time tau( parallel) and high order parameters. The structural connotation of these patterns is examined herein by subjecting AKeco and AKeco*AP(5)A to GNM analysis, which yields the dynamic spectrum in terms of slow and fast modes. The low/high NMR order parameters correlate with the slow/fast modes of the backbone elucidated with GNM. Likewise, tau( parallel) and tau( perpendicular) are associated with fast and slow GNM modes, respectively. Catalysis-related domain motion of AMPbd and LID in AKeco, occurring per NMR with correlation time tau( perpendicular), is associated with the first and second collective slow (global) GNM modes. The ANM-predicted deformations of the unliganded enzyme conform to the functional reconfiguration induced by ligand-binding, indicating the structural disposition (or potential) of the enzyme to bind its substrates. It is shown that NMR/SRLS and GNM/ANM analyses can be advantageously synthesized to provide insights into the molecular mechanisms that control biological function.     

Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information

In this paper a systematic attempt has been made to develop a better method for predicting alpha-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting alpha-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach.     

Novel Techniques for Weak Alignment of Proteins in Solution Using Chemical Tags Coordinating Lanthanide Ions

A molecule with an anisotropic magnetic susceptibility is spontaneously aligned in a static magnetic field. Alignment of such a molecule yields residual dipolar couplings and pseudocontact shifts. Lanthanide ions have recently been successfully used to provide an anisotropic magnetic susceptibility in target molecules either by replacing a calcium ion with a lanthanide ion in calcium-binding proteins or by attaching an EDTA derivative to a cysteine residue via a disulfide bond. Here we describe a novel enantiomerically pure EDTA derived tag that aligns stronger due to its shorter linker and does not suffer from stereochemical diversity upon lanthanide complexation. We observed residual (15)N,(1)H-dipolar couplings of up to 8 Hz at 800 MHz induced by a single alignment tensor from this tag.     

Synaptic interactions between nonspiking local interneurones in the terminal abdominal ganglion of the crayfish

Nonspiking local interneurones are the important premotor elements in arthropod motor control systems. We have analyzed the synaptic interactions between nonspiking interneurones in the crayfish terminal (6th) abdominal ganglion using simultaneous intracellular recordings. Only 15% of nonspiking interneurones formed bi-directional excitatory connections. In 77% of connections, however, the nonspiking interneurones showed a one-way inhibitory interaction. In these cases, the presynaptic nonspiking interneurones received excitatory synaptic inputs from the sensory afferents innervating hairs on the surface of the uropods and the postsynaptic nonspiking interneurones received inhibitory synaptic inputs that were partly mediated by the inputs to the presynaptic nonspiking interneurones. The membrane hyperpolarization of the postsynaptic nonspiking interneurones mediated by the presynaptic nonspiking interneurones was reduced in amplitude when the hyperpolarizing current was injected into the postsynaptic interneurones, or when the external bathing solution was replaced with one containing low calcium and high magnesium concentrations. The role of these interactions in the circuits controlling the movements of the terminal appendages is discussed.Abbreviations AL antero-lateral - epsp excitatory postsynaptic potential - ipsp inhibitory postsynaptic potential - PL postero-lateral     

Recent applications of Hidden Markov Models in computational biology

This paper examines recent developments and applications of Hidden Markov Models (HMMs) to various problems in computational biology, including multiple sequence alignment, homology detection, protein sequences classification, and genomic annotation.     

Untangling ecological complexity on different scales of space and time

Ecological systems are complex and essentially unpredictable, because of the multitude of interactions among their constituents. However, there are general statistical patterns emerging on particular spatial and temporal scales, which indicate the existence of some universal principles behind many ecological phenomena, and which can even be used for the prediction of phenomena occurring on finer scales of resolution. These generalities comprise regular frequency distributions of particular macroscopic variables within higher taxa (body size, abundance, range size), relationships between such variables, and general patterns in species richness. All the patterns are closely related to each other and although there are only a few major explanatory principles, there are plenty of alternative explanations. Reconciliation of different approaches cannot be obtained without careful formulation of testable hypotheses and rigorous quantitative empirical research. Two especially promising ways of untangling ecological complexity comprise: (1) analysis of invariances, i.e. universal quantitative relationships observed within many different systems, and (2) detailed analysis of the anatomy of macroecological phenomena, i.e. explorations of how emergent multispecies patterns are related to regular patterns concerning individual species.

Zusammenfassung

Ökologische Systeme sind komplex und im Wesentlichen aufgrund der Vielzahl von Interaktionen zwischen ihren Bestandteilen nicht vorhersagbar. Dennoch gibt es allgemeine statistische Muster, die in bestimmten räumlichen und zeitlichen Skalen auftreten. Dies weist auf die Existenz von einigen universellen Prinzipien hinter diesen ökologischen Phänomenen hin, die sogar für die Vorhersage von Phänomenen genutzt werden können, die auf kleineren Skalen auftreten. Diese Allgemeingültigkeiten bestehen aus Häufigkeitsverteilungen von bestimmten makroskopischen Variablen innerhalb höherer Taxa (Körpergröße, Abundanz, Arealgröße), den Beziehungen zwischen diesen Variablen und allgemeinen Mustern des Artenreichtums. Alle Muster stehen in enger Beziehung zueinander und obwohl es nur wenige bedeutende Erklärungsprinzipien gibt, existieren viele alternative Erklärungen. Die Abstimmung zwischen verschiedenen Ansätzen kann ohne eine sorgfältige Formulierung von testbaren Hypothesen und rigorose quantitative empirische Forschung nicht erreicht werden. Zwei besonders vielversprechende Wege ökologische Komplexität zu entwirren beinhalten (1) die Analyse von Invarianten, d.h. universellen quantitativen Beziehungen, die innerhalb verschiedener Systeme beobachtet werden, und (2) detaillierte Analysen der Anatomie von makroökologischen Phänomenen, d.h. Untersuchungen darüber, in welcher Beziehung die auftauchenden Muster von Multi-Arten-Systemen zu regulären Mustern individueller Arten stehen.     

Dynamics of EF-G interaction with the ribosome explored by classification of a heterogeneous cryo-EM dataset

A method of supervised classification using two available structure templates was applied to investigate the possible heterogeneity existing in a large cryo-EM dataset of an Escherichia coli 70S ribosome-EF-G complex. Two subpopulations showing the ribosome in distinct conformational states, related by a ratchet-like rotation of the 30S subunit with respect to the 50S subunit, were extracted from the original dataset. The possible presence of additional intermediate states is discussed.     

Sequence alignments and pair hidden Markov models using evolutionary history

This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.     

Predicting reliable regions in protein alignments from sequence profiles

For applications such as comparative modelling one major issue is the reliability of sequence alignments. Reliable regions in alignments can be predicted using sub-optimal alignments of the same pair of sequences. Here we show that reliable regions in alignments can also be predicted from multiple sequence profile information alone.Alignments were created for a set of remotely related pairs of proteins using five different test methods. Structural alignments were used to assess the quality of the alignments and the aligned positions were scored using information from the observed frequencies of amino acid residues in sequence profiles pre-generated for each template structure. High-scoring regions of these profile-derived alignment scores were a good predictor of reliably aligned regions.These profile-derived alignment scores are easy to obtain and are applicable to any alignment method. They can be used to detect those regions of alignments that are reliably aligned and to help predict the quality of an alignment. For those residues within secondary structure elements, the regions predicted as reliably aligned agreed with the structural alignments for between 92% and 97.4% of the residues. In loop regions just under 92% of the residues predicted to be reliable agreed with the structural alignments. The percentage of residues predicted as reliable ranged from 32.1% for helix residues to 52.8% for strand residues.This information could also be used to help predict conserved binding sites from sequence alignments. Residues in the template that were identified as binding sites, that aligned to an identical amino acid residue and where the sequence alignment agreed with the structural alignment were in highly conserved, high scoring regions over 80% of the time. This suggests that many binding sites that are present in both target and template sequences are in sequence-conserved regions and that there is the possibility of translating reliability to binding site prediction.     

Predicted structures of two proteins involved in human diseases

Structures of 79 proteins involved in human diseases were predicted by sequence alignments with structural templates. The predicted structures for ALDP and CSA, proteins responsible for adrenoleukodystrophy and the Cockayne syndrome, respectively, were analyzed to elucidate the molecular basis of disease mutations. In particular we positioned residue P484 of ALDP in the homodimer interface. This positioning is consistent with a recent experimental finding that the mutation P484R significantly decreases the self-interaction of ALDP and suggests that the disease mechanism of this mutation lies in the impaired ALDP dimerization. We identified two new WD repeats in CSA and suggest that one of these forms part of the interaction surface with other proteins.