A robust method for the joint estimation of yield coefficients and kinetic parameters in bioprocess models.

Bioprocess model structures that require nonlinear parameter estimation, thus initialization values, are often subject to poor identification performances because of the uncertainty on those initialization values. Under some conditions on the model structure, it is possible to partially circumvent this problem by an appropriate decoupling of the linear part of the model from the nonlinear part of it. This article provides a procedure to be followed when these structural conditions are not satisfied. An original method for decoupling two sets of parameters, namely, kinetic parameters from maximum growth, production, decay rates, and yield coefficients, is presented. It exhibits the advantage of requiring only initialization of the first subset of parameters. In comparison with a classical nonlinear estimation procedure, in which all the parameters are freed, results show enhanced robustness of model identification with regard to parameter initialization errors. This is illustrated by means of three simulation case studies: a fed-batch Human Embryo Kidney cell cultivation process using a macroscopic reaction scheme description, a process of cyclodextrin-glucanotransferase production by Bacillus circulans, and a process of simultaneous starch saccharification and glucose fermentation to lactic acid by Lactobacillus delbrückii, both based on a Luedeking-Piret model structure. Additionally, perspectives of the presented procedure in the context of systematic bioprocess modeling are promising.

A new generation of statistical potentials for proteins.

We propose a novel and flexible derivation scheme of statistical, database-derived, potentials, which allows one to take simultaneously into account specific correlations between several sequence and structure descriptors. This scheme leads to the decomposition of the total folding free energy of a protein into a sum of lower order terms, thereby giving the possibility to analyze independently each contribution and clarify its significance and importance, to avoid overcounting certain contributions, and to deal more efficiently with the limited size of the database. In addition, this derivation scheme appears as quite general, for many previously developed potentials can be expressed as particular cases of our formalism. We use this formalism as a framework to generate different residue-based energy functions, whose performances are assessed on the basis of their ability to discriminate genuine proteins from decoy models. The optimal potential is generated as a combination of several coupling terms, measuring correlations between residue types, backbone torsion angles, solvent accessibilities, relative positions along the sequence, and interresidue distances. This potential outperforms all tested residue-based potentials, and even several atom-based potentials. Its incorporation in algorithms aiming at predicting protein structure and stability should therefore substantially improve their performances.

PoPMuSiC, rationally designing point mutations in protein structures.

PoPMuSiC is an efficient tool for rational computer-aided design of single-site mutations in proteins and peptides. Two types of queries can be submitted. The first option allows to estimate the changes in folding free energy for specific point mutations given by the user. In the second option, all possible point mutations in a given protein or protein region are performed and the most stabilizing or destabilizing mutations, or the neutral mutations with respect to thermodynamic stability, are selected. For each sequence position or secondary structure the deviation from the most stable sequence is moreover evaluated, which helps to identify the most suitable sites for the introduction of mutations.

Optimality of the genetic code with respect to protein stability and amino-acid frequencies.

BACKGROUND: The genetic code is known to be efficient in limiting the effect of mistranslation errors. A misread codon often codes for the same amino acid or one with similar biochemical properties, so the structure and function of the coded protein remain relatively unaltered. Previous studies have attempted to address this question quantitatively, by estimating the fraction of randomly generated codes that do better than the genetic code in respect of overall robustness. We extended these results by investigating the role of amino-acid frequencies in the optimality of the genetic code. RESULTS: We found that taking the amino-acid frequency into account decreases the fraction of random codes that beat the natural code. This effect is particularly pronounced when more refined measures of the amino-acid substitution cost are used than hydrophobicity. To show this, we devised a new cost function by evaluating in silico the change in folding free energy caused by all possible point mutations in a set of protein structures. With this function, which measures protein stability while being unrelated to the code's structure, we estimated that around two random codes in a billion (109) are fitter than the natural code. When alternative codes are restricted to those that interchange biosynthetically related amino acids, the genetic code appears even more optimal. CONCLUSIONS: These results lead us to discuss the role of amino-acid frequencies and other parameters in the genetic code's evolution, in an attempt to propose a tentative picture of primitive life.

Role of salt bridges in homeodomains investigated by structural analyses and molecular dynamics simulations.

Homeodomains are a class of helix-turn-helix DNA-binding protein motifs that play an important role in the control of cellular development in eukaryotes. They fold in a three alpha-helix structural module, where the third helix is the recognition helix that fits into the major groove of DNA. Structural analysis of the members of the homeodomain family led to the identification of interactions likely to stabilize the protein domains. Linking the helices pairwise, three salt bridges were found to be well preserved within the family. Also well conserved were two cation-pi interactions between aromatic and positively charged side chains. To analyze the structural role of the salt bridges, molecular dynamics simulations (MD) were carried out on the wild-type homeodomain from the Drosophila paired protein (1fjl) and on three mutants, which lack one or two salt bridges and mimic natural mutations in other homeodomains. Analysis of the trajectories revealed only small structural rearrangements of the three helices in all MD simulations, thereby suggesting that the salt bridges have no essential stabilizing role at room temperature, but rather might be important for improving thermostability. The latter hypothesis is supported by a good correlation between the melting midpoint temperatures of several homeodomains and the number of salt bridges and cation-pi interactions that connect secondary structures.

Identification and ab initio simulations of early folding units in proteins.

The location of protein subunits that form early during folding, constituted of consecutive secondary structure elements with some intrinsic stability and favorable tertiary interactions, is predicted using a combination of threading algorithms and local structure prediction methods. Two folding units are selected among the candidates identified in a database of known protein structures: the fragment 15-55 of 434 cro, an all-alpha protein, and the fragment 1-35 of ubiquitin, an alpha/beta protein. These units are further analyzed by means of Monte Carlo simulated annealing using several database-derived potentials describing different types of interactions. Our results suggest that the local interactions along the chain dominate in the first folding steps of both fragments, and that the formation of some of the secondary structures necessarily occurs before structure compaction. These findings led us to define a prediction protocol, which is efficient to improve the accuracy of the predicted structures. It involves a first simulation with a local interaction potential only, whose final conformation is used as a starting structure of a second simulation that uses a combination of local interaction and distance potentials. The root mean square deviations between the coordinates of predicted and native structures are as low as 2-4 A in most trials. The possibility of extending this protocol to the prediction of full proteins is discussed. Proteins 2001;42:164-176.

Contribution of cation-pi interactions to the stability of protein-DNA complexes.

Cation-pi interactions between an aromatic ring and a positive charge located above it have proven to be important in protein structures and biomolecule associations. Here, the role of these interactions at the interface of protein-DNA complexes is investigated, by means of ab initio quantum mechanics energy calculations and X-ray structure analyses. Ab initio energy calculations indicate that Na ions and DNA bases can form stable cation-pi complexes, whose binding strength strongly depends on the type of base, on the position of the Na ion, and whether the base is isolated or included in a double-stranded B-DNA. A survey of protein-DNA complex structures using appropriate geometrical criteria revealed cation-pi interactions in 71% of the complexes. More than half of the cation-pi pairs involve arginine residues, about one-third asparagine or glutamine residues that only carry a partial charge, and one-seventh lysine residues. The most frequently observed pair, which is also the most stable as monitored by ab initio energy calculations, is arginine- guanine. Arginine-adenine interactions are also favorable in general, although to a lesser extent, whereas those with thymine and cytosine are not. Our calculations show that the major contribution to cation-pi interactions with DNA bases is of electrostatic nature. These interactions often occur concomitantly with hydrogen bonds with adjacent bases; their strength is estimated to be from three to four times lower than that of hydrogen bonds. Finally, the role of cation-pi interactions in the stability and specificity of protein-DNA complexes is discussed.

PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins.

A novel tool for computer-aided design of single-site mutations in proteins and peptides is presented. It proceeds by performing in silico all possible point mutations in a given protein or protein region and estimating the stability changes with linear combinations of database-derived potentials, whose coefficients depend on the solvent accessibility of the mutated residues. Upon completion, it yields a list of the most stabilizing, destabilizing or neutral mutations. This tool is applied to mouse, hamster and human prion proteins to identify the point mutations that are the most likely to stabilize their cellular form. The selected mutations are essentially located in the second helix, which presents an intrinsic preference to form beta-structures, with the best mutations being T183-->F, T192-->A and Q186-->A. The T183 mutation is predicted to be by far the most stabilizing one, but should be considered with care as it blocks the glycosylation of N181 and this blockade is known to favor the cellular to scrapie conversion. Furthermore, following the hypothesis that the first helix might induce the formation of hydrophilic beta-aggregates, several mutations that are neutral with respect to the structure's stability but improve the helix hydrophobicity are selected, among which is E146-->L. These mutations are intended as good candidates to undergo experimental tests.

Expression of a novel factor in human breast cancer cells with metastatic potential.

Clinical and experimental evidence suggests that tumor cells shed into the circulation from solid cancers are ineffective in forming distant metastasis unless the cells are able to respond to growth conditions offered by the secondary organs. To identify the phenotypic properties that are specific for such growth response, we injected carcinoma cells, which had been recovered from bone marrow micrometastases in a breast cancer patient who was clinically devoid of overt metastatic disease and established in culture, into the systemic circulation of immunodeficient rats. The animals developed metastases in the central nervous system, and metastatic tumor cells were isolated with immunomagnetic beads coated with an antibody that was reactive with human cells. The segregated cell population was compared with the injected cells by means of differential display analysis, and two candidate fragments were identified as up-regulated in the fully metastatic cells. The first was an intracellular effector molecule involved in tyrosine kinase signaling, known to mediate nerve growth factor-dependent promotion of cell survival. The second was a novel gene product (termed candidate of metastasis-1), presumably encoding a DNA-binding protein of helix-turn-helix type. Constitutive expression of candidate of metastasis-1 seemed to distinguish breast cancer cells with metastatic potential from cells without metastatic potential. Hence, our experimental approach identified factors that may mediate the growth response of tumor cells upon establishment in a secondary organ and, thereby, contribute to the metastatic phenotype.

Typical interaction patterns in alphabeta and betaalpha turn motifs.

A fully automatic classification procedure of short protein fragments is applied to identify connections between alpha-helices and beta-strands in a dataset of 141 protein chains. It yields 15 structural families of alphabeta turns and 15 families of betaalpha turns with at least five members. The sequence and structural features of these turn motifs are analysed with the focus on the local interactions located at alpha-helix and beta-strand ends. This analysis reveals specific interaction patterns that occur frequently among the members of many of the identified turn motifs. For the beta-strands, novel patterns are identified at the strands' entry and exit; they involve side chain/side chain contacts and beta-turns, generally of type I or II. For the alpha-helices, the interaction patterns consist of several backbone/backbone or backbone/side chain hydrogen bonds and of hydrophobic contacts; they generalize the well known N-terminal capping and C-terminal Schellman motifs. The interaction patterns at both ends of alpha-helices and beta-strands are found to constitute favourable structure motifs with low amino acid sequence specificity; their possible stabilizing role is discussed. Finally, the robustness of our classification procedure and of the description of N- and C-cap interaction patterns is validated by repeating our analysis on a larger dataset of 381 protein chains and showing that the results are maintained.