Time- and ensemble-averaged direct NOE restraints

Summary NMR data are collected as time- and ensemble-averaged quantities. Yet, in commonly used methods for structure determination of biomolecules, structures are required to satisfy simultaneously a large number of constrainsts. Recently, however, methods have been developed that allow a better fit of the experimental data by the use of time- or ensemble-averaged restraints. Thus far, these methods have been applied to structure refinement using distance and J-coupling restraints. In this paper, time and ensemble averaging is extended to the direct refinement with experimental NOE data. The implementation of time- and ensemble-averaged NOE restraints in DINOSAUR is described and illustrated with experimental NMR data for crambin, a 46-residue protein. Structure refinement with both time- and ensemble-averaged NOE restraints results in lower R-factors, indicating a better fit of the experimental NOE data.     

Determination of protein global folds using backbone residual dipolar coupling and long-range NOE restraints

We report the determination of the global fold of human ubiquitin using protein backbone NMR residual dipolar coupling and long-range nuclear Overhauser effect (NOE) data as conformational restraints. Specifically, by use of a maximum of three backbone residual dipolar couplings per residue (N_i-H^N _i, N_i-C_i–1, H^N _i - C_i–1) in two tensor frames and only backbone H^N-H^N NOEs, a global fold of ubiquitin can be derived with a backbone root-mean-square deviation of 1.4 Å with respect to the crystal structure. This degree of accuracy is more than adequate for use in databases of structural motifs, and suggests a general approach for the determination of protein global folds using conformational restraints derived only from backbone atoms.     

Automated detection of problem restraints in NMR data sets using the FINGAR genetic algorithm method

The recently described FINGAR genetic algorithm method for NMR refinement [D.A. Pearlman (1996) J. Biomol. NMR, 8, 67–76] has been extended so that it can be used to detect problem restraints in an NMR-derived set of data. A problem restraint is defined as a restraint in a generally well-behaved set where the associated target value is in error, due to inaccuracies in the data, misassignment, etc. The method described here, FINGAR.RWF, locates problem restraints by finding those restraints that, if removed from the data set, result in a disproportionate improvement in the scoring function. The method is applied to several test cases of simulated data, as well as to real data for the FK506 macrocycle, with excellent results.     

Simultaneous determination of disulphide bridge topology and three-dimensional structure using ambiguous intersulphur distance restraints: Possibilities and limitations

Knowledge of the native disulphide bridge topology allows the introduction of conformational restraints between remote parts of the peptide chain. This information is therefore of great importance for the successful determination of the three-dimensional structure of cysteine-rich proteins by NMR spectroscopy. In this paper we investigate the limitations of using ambiguous intersulphur restraints [Nilges, M. (1995) J. Mol. Biol., 245, 645–660] associated with NMR experimental information to determine the native disulphide bridge pattern. Using these restraints in a simulated annealing protocol we have determined the correct topology of numerous examples, including a protein with seven disulphide bridges (phospholipase A₂) and a protein in which 25% of the total number of residues are cysteines (-conotoxin GIIIB). We have also characterised the behaviour of the method when only limited experimental data is available, and find that the proposed protocol permits disulphide bridge determination even with a small number of restraints (around 5 NOEs – including a long-range restraint – per residue). In addition, we have shown that under these conditions the use of a reduced penalty function allows the identification of misassigned NOE restraints. These results indicate that the use of ambiguous intersulphur distances with the proposed simulated annealing protocol is a general method for the determination of disulphide bridge topology, particularly interesting in the first steps of NMR study of cysteine-rich proteins. Comparison with previously proposed protocols indicates that the presented method is more reliable and the interpretation of results is straightforward.     

Dynamic modeling of genetic networks using genetic algorithm and S-system

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.     

Modified full-sib selection and estimation of genetic parameters

Summary A cycle of full-sib selection is completed in three seasons while that of a modified method is completed in two seasons. In modified full-sib selection, selected families can be recombined and new families generated following a partial-diallel cross. The components of genetic variance can be estimated from the partial-diallel analysis of such families. Thus, in addition to performing selection, genetic parameters can be estimated.     

Predicting in situ soil N2O emission using NOE algorithm and soil database

This paper presents a new algorithm, Nitrous Oxide Emission (NOE) for simulating the emission of the greenhouse gas N₂O from agricultural soils. N₂O fluxes are calculated as the result of production through denitrification and nitrification and reduction through the last step of denitrification. Actual denitrification and nitrification rates are calculated from biological parameters and soil water‐filled pore space, temperature and mineral nitrogen contents. New suggestions in NOE consisted in introducing (1) biological site‐specific parameters of soil N₂O reduction and (2) reduction of the N₂O produced through nitrification to N₂ through denitrification. This paper includes a database of 64 N₂O fluxes measured on the field scale with corresponding environmental parameters collected from five agricultural situations in France. This database was used to test the validity of this algorithm. Site per site comparison of simulated N₂O fluxes against observed data leads to mixed results. For 80% of the tested points, measured and simulated fluxes are in accordance whereas the others resulted in an important discrepancy. The origin of this discrepancy is discussed. On the other hand, mean annual fluxes measured on each site were strongly correlated to mean simulated annual fluxes. The biological site‐specific parameter of soil N₂O reduction introduced into NOE appeared particularly useful to discriminate the general level of N₂O emissions from site to site. Furthermore, the relevance of NOE was confirmed by comparing measured and simulated N₂O fluxes using some data from the US TRAGNET database. We suggest the use of NOE on a regional scale in order to predict mean annual N₂O emissions.     

Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA

Combined automated NOE assignment and structure determination module (CANDID) is a new software for efficient NMR structure determination of proteins by automated assignment of the NOESY spectra. CANDID uses an iterative approach with multiple cycles of NOE cross-peak assignment and protein structure calculation using the fast DYANA torsion angle dynamics algorithm, so that the result from each CANDID cycle consists of exhaustive, possibly ambiguous NOE cross-peak assignments in all available spectra and a three-dimensional protein structure represented by a bundle of conformers. The input for the first CANDID cycle consists of the amino acid sequence, the chemical shift list from the sequence-specific resonance assignment, and listings of the cross-peak positions and volumes in one or several two, three or four-dimensional NOESY spectra. The input for the second and subsequent CANDID cycles contains the three-dimensional protein structure from the previous cycle, in addition to the complete input used for the first cycle. CANDID includes two new elements that make it robust with respect to the presence of artifacts in the input data, i.e. network-anchoring and constraint-combination, which have a key role in de novo protein structure determinations for the successful generation of the correct polypeptide fold by the first CANDID cycle. Network-anchoring makes use of the fact that any network of correct NOE cross-peak assignments forms a self-consistent set; the initial, chemical shift-based assignments for each individual NOE cross-peak are therefore weighted by the extent to which they can be embedded into the network formed by all other NOE cross-peak assignments. Constraint-combination reduces the deleterious impact of artifact NOE upper distance constraints in the input for a protein structure calculation by combining the assignments for two or several peaks into a single upper limit distance constraint, which lowers the probability that the presence of an artifact peak will influence the outcome of the structure calculation. CANDID test calculations were performed with NMR data sets of four proteins for which high-quality structures had previously been solved by interactive protocols, and they yielded comparable results to these reference structure determinations with regard to both the residual constraint violations, and the precision and accuracy of the atomic coordinates. The CANDID approach has further been validated by de novo NMR structure determinations of four additional proteins. The experience gained in these calculations shows that once nearly complete sequence-specific resonance assignments are available, the automated CANDID approach results in greatly enhanced efficiency of the NOESY spectral analysis. The fact that the correct fold is obtained in cycle 1 of a de novo structure calculation is the single most important advance achieved with CANDID, when compared with previously proposed automated NOESY assignment methods that do not use network-anchoring and constraint-combination.     

Docking ligands to vasopressin and oxytocin receptors via genetic algorithm

The aim of the study was to computer-dock selected ligands to neurophyseal receptors in order to identify amino acid residues responsible for ligand-receptor interactions. To this aim, reliable oxytocin receptor (OTR) and arginine-vasopressin receptor (V1aR/V2R) models were built. The OTR-selective agonist [Thr4,Gly7]OT, the OTR-selective cyclohexapeptide antagonist L-366,948 and OT itself were docked via genetic algorithm to OTR, V1aR, and V2R and relaxed using a constrained simulated annealing protocol. For the analysis of receptor/ligand interactions a subset of initial conformations was chosen using energetic and steric criteria. All three ligands seem to prefer similar modes of binding to the receptors, manifested by repetitive residues of the receptors which directly interact with the ligands. Taking into account that many aspects of mechanisms of G protein-coupled receptor (GPCR) action are still unsolved, the results obtained with the docking simulations may propose future experimental research, especially in site-directed mutagenesis analysis and searching for key amino acid residues responsible for drug activities.     

Motif identification method based on Gibbs sampling and genetic algorithm

The regulation of gene expression is the key of organism genetic mechanism. Motif identification is an important step in constructing expression regulatory network. Based on Gibbs sampling method, this work constructed position weight matrix, thereby proposing motif recognition method based on genetic algorithm. Scoring function is defined to update the population and obtain the convergence matrix of position weight, achieving the identification of motifs with different length. Simulation and experimental data sets were utilized to verify the accuracy and execution time of the algorithm.     

Moderate multiple parentage and low genetic variation reduces the potential for genetic incompatibility avoidance despite high risk of inbreeding

Background

Polyandry is widespread throughout the animal kingdom. In the absence of direct benefits of mating with different males, the underlying basis for polyandry is enigmatic because it can carry considerable costs such as elevated exposure to sexual diseases, physical injury or other direct fitness costs. Such costs may be balanced by indirect genetic benefits to the offspring of polyandrous females. We investigated polyandry and patterns of parentage in the spider Stegodyphus lineatus. This species experiences relatively high levels of inbreeding as a result of its spatial population structure, philopatry and limited male mating dispersal. Polyandry may provide an opportunity for post mating inbreeding avoidance that reduces the risk of genetic incompatibilities arising from incestuous matings. However, multiple mating carries direct fitness costs to females suggesting that genetic benefits must be substantial to counter direct costs.

Methodology/Principal Findings

Genetic parentage analyses in two populations from Israel and a Greek island, showed mixed-brood parentage in approximately 50% of the broods. The number of fathers ranged from 1–2 indicating low levels of multiple parentage and there was no evidence for paternity bias in mixed-broods from both populations. Microsatellite loci variation suggested limited genetic variation within populations, especially in the Greek island population. Relatedness estimates among females in the maternal generation and potentially interacting individuals were substantial indicating full-sib and half-sib relationships.

Conclusions/Significance

Three lines of evidence indicate limited potential to obtain substantial genetic benefits in the form of reduced inbreeding. The relatively low frequency of multiple parentage together with low genetic variation among potential mates and the elevated risk of mating among related individuals as corroborated by our genetic data suggest that there are limited actual outbreeding opportunities for polyandrous females. Polyandry in S. lineatus is thus unlikely to be maintained through adaptive female choice.     

Modified citrus pectin reduces galectin-3 expression and disease severity in experimental acute kidney injury

Galectin-3 is a β-galactoside binding lectin with roles in diverse processes including proliferation, apoptosis, inflammation and fibrosis which are dependent on different domains of the molecule and subcellular distribution. Although galectin-3 is known to be upregulated in acute kidney injury, the relative importance of its different domains and functions are poorly understood in the underlying pathogenesis. Therefore we experimentally modulated galectin-3 in folic acid (FA)-induced acute kidney injury utilising modified citrus pectin (MCP), a derivative of pectin which can bind to the galectin-3 carbohydrate recognition domain thereby predominantly antagonising functions linked to this role. Mice were pre-treated with normal or 1% MCP-supplemented drinking water one week before FA injection. During the initial injury phase, all FA-treated mice lost weight whilst their kidneys enlarged secondary to the renal insult; these gross changes were significantly lessened in the MCP group but this was not associated with significant changes in galectin-3 expression. At a histological level, MCP clearly reduced renal cell proliferation but did not affect apoptosis. Later, during the recovery phase at two weeks, MCP-treated mice demonstrated reduced galectin-3 in association with decreased renal fibrosis, macrophages, pro-inflammatory cytokine expression and apoptosis. Other renal galectins, galectin-1 and -9, were unchanged. Our data indicates that MCP is protective in experimental nephropathy with modulation of early proliferation and later galectin-3 expression, apoptosis and fibrosis. This raises the possibility that MCP may be a novel strategy to reduce renal injury in the long term, perhaps via carbohydrate binding-related functions of galectin-3.     

A genetic algorithm that seeks native states of peptides and proteins.

We describe a computer algorithm to predict native structures of proteins and peptides from their primary sequences, their known native radii of gyration, and their known disulfide bonding patterns, starting from random conformations. Proteins are represented as simplified real-space main chains with single-bead side chains. Nonlocal interactions are taken from structural database-derived statistical potentials, as in an earlier treatment. Local interactions are taken from simulations of (phi, psi) energy surfaces for each amino acid generated using the Biosym Discover program. Conformational searching is done by a genetic algorithm-based method. Reasonable structures are obtained for melittin (a 26-mer), avian pancreatic polypeptide inhibitor (a 36-mer), crambin (a 46-mer), apamin (an 18-mer), tachyplesin (a 17-mer), C-peptide of ribonuclease A (a 13-mer), and four different designed helical peptides. A hydrogen bond interaction was tested and found to be generally unnecessary for helical peptides, but it helps fold some sheet regions in these structures. For the few longer chains we tested, the method appears not to converge. In those cases, it appears to recover native-like secondary structures, but gets incorrect tertiary folds.     

Protein structure prediction using mutually orthogonal Latin squares and a genetic algorithm

We combine a new, extremely fast technique to generate a library of low energy structures of an oligopeptide (by using mutually orthogonal Latin squares to sample its conformational space) with a genetic algorithm to predict protein structures. The protein sequence is divided into oligopeptides, and a structure library is generated for each. These libraries are used in a newly defined mutation operator that, together with variation, crossover, and diversity operators, is used in a modified genetic algorithm to make the prediction. Application to five small proteins has yielded near native structures.     

Hierarchical genetic algorithm versus static optimization-investigation of elbow flexion and extension movements

The applicability of static optimization (and, respectively, frequently used objective functions) for prediction of individual muscle forces for dynamic conditions has often been discussed. Some of the problems are whether time-independent objective functions are suitable, and how to incorporate muscle physiology in models. The present paper deals with a twofold task: (1) implementation of hierarchical genetic algorithm (HGA) based on the properties of the motor units (MUs) twitches, and using multi-objective, time-dependent optimization functions; and (2) comparison of the results of the HGA application with those obtained through static optimization with a criterion "minimum of a weighted sum of the muscle forces raised to the power of n". HGA and its software implementation are presented. The moments of neural stimulation of all MUs are design variables coding the problem in the terms of HGA. The main idea is in using genetic operations to find these moments, so that the sum of MUs twitches satisfies the imposed goals (required joint moments, minimal sum of muscle forces, etc.). Elbow flexion and extension movements with different velocities are considered as proper illustration. It is supposed that they are performed by two extensor muscles and three flexor muscles. The results show that HGA is a suitable means for precise investigation of motor control. Many experimentally observed phenomena (such as antagonistic co-contraction, three-phasic behavior of the muscles during fast movements) can find their explanation by the properties of the MUs twitches. Static optimization is also able to predict three-phasic behavior and could be used as practicable and computationally inexpensive method for total estimation of the muscle forces.     

Incremental genetic K-means algorithm and its application in gene expression data analysis

Background  

In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.     

Reconstruction of protein form with X-ray solution scattering and a genetic algorithm

We have reconstructed, from experimental approximately 2 nm resolution X-ray solution scattering profiles, the corresponding shapes and sizes of myoglobin, troponin C, spermadhesin PSP-I/PSP-II, chymotrypsinogen A, superoxide dismutase, ovalbumin, tubulin, nitrite reductase, catalase, the structural change of troponin C upon dissociation of the two high affinity Ca(2+), and the solution model structure of a tandem pair of fibronectin type III cytoplasmic domains of integrin alpha6beta4 before determination of its crystal structure. To this purpose we have designed a new genetic algorithm which gradually explores a discrete search space and evolves convergent models made of several hundred beads (down to 0.3 nm radius) best fitting the scattering profile upon Debye calculation, without geometrical constraints or penalty for loose beads. This is a procedure of effective numerical transformation of the one-dimensional scattering profiles into three-dimensional model structures. The number of beads in models is correlated with the protein molecular mass (with one exception). The shape and approximate dimensions of each protein have been retrieved by a set of ten solution models, essentially superimposable with the available crystal structures.     

Inference of biological S-system using the separable estimation method and the genetic algorithm

Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an l? regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method.