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.

Different derivations of knowledge-based potentials and analysis of their robustness and context-dependent predictive power.

The possibility of defining effective potentials from known protein structures, which are sufficiently accurate to be used for protein-structure-prediction purposes, is investigated. Three types of distance potentials and three types of backbone torsion potentials are defined, based on propensities of amino acid pairs to be separated by a given spatial distance or to be associated to a backbone torsion angle domain. Their differences reside in the way the physical correlations between the amino acids and the conformational states are extracted from the bulk interactions due to the presence of many residues in a protein. For the distance potentials, a physical meaning can be associated to the different definitions, given that some of the potentials favor hydrophobic interactions and others favor interactions between oppositely charged residues. The performance of the different torsion and distance potentials in structure prediction procedures, in particular native-fold recognition and evaluation of protein stability changes upon point mutations, is analyzed. It appears to differ according to the specific proteins and protein environments. In particular, one of the distance potentials performs better than the others for membrane proteins and in protein regions involving charged residues, but less well in other protein regions. Furthermore, the dependence of the potentials on the characteristics of the proteins from which they are derived is analyzed. It is shown that the dependence of the potentials on the length, amino acid composition and secondary-structure content of the proteins from the dataset is either very limited or rather strong, according to the type of potential. The results obtained suggest that the main problem limiting the performance of database-derived potentials is their lack of universality: each potential describes with satisfactory accuracy only the interactions present in certain protein environments.

Structural classification of alphabetabeta and betabetaalpha supersecondary structure units in proteins.

We present a fully automatic structural classification of supersecondary structure units, consisting of two hydrogen-bonded beta strands, preceded or followed by an alpha helix. The classification is performed on the spatial arrangement of the secondary structure elements, irrespective of the length and conformation of the intervening loops. The similarity of the arrangements is estimated by a structure alignment procedure that uses as similarity measure the root mean square deviation of superimposed backbone atoms. Applied to a set of 141 well-resolved nonhomologous protein structures, the classification yields 11 families of recurrent arrangements. In addition, fragments that are structurally intermediate between the families are found; they reveal the continuity of the classification. The analysis of the families shows that the alpha helix and beta hairpin axes can adopt virtually all relative orientations, with, however, some preferable orientations; moreover, according to the orientation, preferences in the left/right handedness of the alpha-beta connection are observed. These preferences can be explained by favorable side by side packing of the alpha helix and the beta hairpin, local interactions in the region of the alpha-beta connection or stabilizing environments in the parent protein. Furthermore, fold recognition procedures and structure prediction algorithms coupled to database-derived potentials suggest that the preferable nature of these arrangements does not imply their intrinsic stability. They usually accommodate a large number of sequences, of which only a subset is predicted to stabilize the motif. The motifs predicted as stable could correspond to nuclei formed at the very beginning of the folding process.

Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence.

For 238 mutations of residues totally or partially buried in the protein core, we estimate the folding free energy changes upon mutation using database-derived potentials and correlate them with the experimentally measured ones. Several potentials are tested, representing different kinds of interactions. Local interactions along the chain are described by torsion potentials, based on propensities of amino acids to be associated with backbone torsion angle domains. Non-local interactions along the sequence are represented by distance potentials, derived from propensities of amino acid pairs or triplets to be at a given spatial distance. We find that for the set of totally buried residues, the best performing potential is a combination of a distance potential and a torsion potential weighted by a factor of 0.4; it yields a correlation coefficient between computed and measured changes in folding free energy of 0.80. For mutations of partially buried residues, the best potential is a combination of a torsion potential and a distance potential weighted by a factor of 0.7, and for the previously analysed mutations of solvent accessible residues, it is a torsion potential taken individually; the respective correlation coefficients reach 0.82 and 0.87. These results show that distance potentials, dominated by hydrophobic interactions, represent best the main interactions stabilizing the protein core, whereas torsion potentials, describing local interactions along the chain, represent best the interactions at the protein surface. The prediction accuracy reached by the distance potentials is, however, lower than that of the torsion potentials. A possible reason for this is that distance potentials would not describe correctly the effect on protein stability due to cavity formation upon mutating a large into a small amino acid. Last but not least, our results indicate that although local interactions, responsible for secondary structure formation, do not dominate in the protein core, they are not negligible for all that. They have a significant weight in the delicate balance between all the interactions that ensure protein stability.

Structural classification of HTH DNA-binding domains and protein-DNA interaction modes.

This paper constitutes an attempt to rationalize the structural similarities and differences that are observed among the HTH DNA-binding domains, and the various modes of protein-DNA interactions. It consists of classifying all the domains of known structure into families on the basis of the spatial arrangement of their helices, irrespective of the type of loops and the presence of beta-strands, and examining the interaction patterns between amino acids and DNA within each family. It is found that the recognition helix and the preceding helix along the chain have always the same relative orientation. Structural differences arise when considering three helices, corresponding usually to the recognition helix and the two preceding ones, but sometimes to the recognition helix and the two flanking helices. Using an automatic classification procedure, seven main families are obtained, whose members have in common the spatial arrangement of their three key helices, but have sometimes different topology and belong to different species. The structural divergence among these families and the existence of structural intermediates are analyzed. Searching these families systematically for recurrent motifs, leads to identify two specific turns, besides the HTH turn. They both link the two helices preceding the recognition helix and are each characteristic of a given family. Furthermore, the conservation of protein-DNA interaction patterns is examined with respect to the structural alignments. These patterns are found to be relatively well conserved within each family and to be different between the different families. The agreement of the structural classification and the patterns of protein-DNA contacts justify our approach, and suggests its applicability, in particular for modelling protein-DNA interactions.

Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials.

The stability changes in peptides and proteins caused by the substitution of a single amino acid, which can be measured experimentally by the change in folding free energy, are evaluated here using effective potentials derived from known protein structures. The analysis is focused on mutations of residues that are accessible to the solvent. These represent in total 106 mutations, introduced at different sites in barnase, bacteriophage T4 lysozyme and chymotrypsin inhibitor 2, and in a synthetic helical peptide. Assuming that the mutations do not modify the backbone structure, the changes in folding free energies are computed using various types of database-derived potentials and are compared with the measured ones. Distance-dependent residue-residue potentials are found to be inadequate for estimating the stability changes caused by these mutations, as they are dominated by hydrophobic interactions, which do not play an essential role at the protein surface. On the contrary, the potentials based on backbone torsion angle propensities yield quite good results. Indeed, for a subset of 96 out of the 106 mutations, the computed and measured changes in folding free energy correlate with a linear correlation coefficient of 0.87. Moreover, the ten mutations that are excluded from the correlation either seem to cause modifications of the backbone structure or to involve strong hydrophobic interactions, which are atypical for solvent-accessible residues. We find furthermore that raising the ionic strength of the solvent used for measuring the changes in folding free energies improves the correlation, as it tends to mask the electrostatic interactions. When adding to these 106 mutations 44 mutations performed in staphylococcal nuclease and chemotactic protein, which were first discarded because some of them were suspected to affect the backbone conformation or the denatured state, the correlation between measured and computed folding free energy changes remains quite good: the correlation coefficient is 0.86 for 135 out of the 150 mutations. The success of the backbone torsion potentials in predicting stability changes indicates that the approximations made for deriving these potentials are adequate. It suggests moreover that the local interactions along the chain dominate at the protein surface.

Automatic classification and analysis of alpha alpha-turn motifs in proteins.

An automatic procedure for the classification of short protein fragments, representing turn motifs between two consecutive secondary structures, is presented. This procedure has two steps. Fragments of given length are first grouped on the basis of their backbone dihedral angle values, and then clustered as a function of the root-mean-square deviation of their superimposed backbone atoms. The classification procedure identifies 63 families of turn motifs with at least five members, in a dataset of 141 proteins. A detailed analysis is presented of the ten identified alpha alpha-turn families, of which four correspond to novel motifs. The sequence and structure features that characterize these families are described. It is found that some features are conserved within the fragments belonging to the same family, but their environment in the parent protein varies considerably. N-capping interactions and helix stop signals are encountered in a number of families, where they seem to stabilize the motif conformation. In two families, one with three residues in the loop, and one with four, an appreciable fraction of the members displays both types of characteristic helix end interactions in the same motif. Interestingly, contrary to most other alpha alpha-turns, the relative frequency of these two motifs is much higher than that of short protein segments with the same loop conformation. Furthermore, the family with three residues in the loop includes the helix-turn-helix motif known to bind DNA. It seems to be the only one among the ten identified families that can be related to biological function.

Protein structure prediction by threading methods: evaluation of current techniques.

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure.

Automatic analysis of protein conformational changes by multiple linkage clustering.

An automatic algorithm is presented for analyzing protein conformational changes such as those occurring upon substrate binding or in different crystal forms of the same protein. Using, as sole information, the atomic coordinates of a pair of protein structures, the procedure first generates structure alignments, which optimize the root-mean-square deviation of the backbone atoms. To this end, equivalent secondary structures and/or loops from both proteins are combined by a multiple linkage hierarchic clustering algorithm, which generates several intertwined clustering trees. Automatic analysis of these clustering trees is used to dissect the mechanism of the conformational change. It allows the identification of the static core, representing the collection of secondary structures which undergo no structural changes, as well as other entities which move like rigid bodies. It also permits the description of the movement of secondary structures or loops relative to this core or entities. USing this information, it can be inferred whether a particular conformational change involves shear or hinge motion, or components of both. The algorithm is applied to the analysis of the conformational changes of citrate synthase, lactate dehydrogenase, lactoferrin and beta-glucosyltransferase, representing typical examples of shear- and hinge-type mechanisms, and a varied range in movement size. The results are shown to be in excellent agreement with previous analyses, and to provide additional information which gives a more complete and objective picture of the conformational change. Using our automatic algorithm, we find that any conformational change may be viewed as having components of both shear- and hinge-type motion. Determining which of these is most appropriate requires the combination of the information provided by our procedure with detailed knowledge of the protein tertiary structures.

Are database-derived potentials valid for scoring both forward and inverted protein folding?

Database-derived potentials, compiled from frequencies of sequence and structure features, are often used for scoring the compatibility of protein sequences and conformations. It is often believed that these scores correspond to differences in free energy with, in addition, a term containing the partition function of the system. Since this function does not depend on the conformation, the potentials are considered to be valid for scoring the compatibility of different conformations with a given sequence ('forward folding'), but not of sequences with a given structure ('inverted folding'). This interpretation is questioned here. It is argued that when many body-effects, which dominate frequencies compiled from the protein database, are corrected for, the potentials approximate a physically meaningful free energy difference from which the partition function term cancels out. It is the difference between the free energy of a given sequence in a specific conformation and that of the same sequence in a denatured-like state. Two examples of denatured-like states are discussed. Depending on the considered state, the free energy difference reduces to the commonly used scoring scheme, or contains additional terms that depend on the sequence. In both cases, all the terms can be derived from sequence-structure frequencies in the database. Such free energy difference, commonly defined as the folding free energy, is a measure of protein stability and can be used for scoring both forward and inverted protein folding. The implications for the use of knowledge-based potentials in protein structure prediction are described. Finally, the difficulty of designing tests that could validate the proposed approach, and the inherent limitations of such tests, are discussed.

Optimal protein structure alignments by multiple linkage clustering: application to distantly related proteins.

A fully automatic procedure for aligning two protein structures is presented. It uses as sole structural similarity measure the root mean square (r.m.s.) deviation of superimposed backbone atoms (N, C alpha, C and O) and is designed to yield optimal solutions with respect to this measure. In a first step, the procedure identifies protein segments with similar conformations in both proteins. In a second step, a novel multiple linkage clustering algorithm is used to identify segment combinations which yield optimal global structure alignments. Several structure alignments can usually be obtained for a given pair of proteins, which are exploited here to define automatically the common structural core of a protein family. Furthermore, an automatic analysis of the clustering trees is described which enables detection of rigid-body movements between structure elements. To illustrate the performance of our procedure, we apply it to families of distantly related proteins. One groups the three alpha + beta proteins ubiquitin, ferredoxin and the B1-domain of protein G. Their common structure motif consists of four beta-strands and the only alpha-helix, with one strand and the helix being displaced as a rigid body relative to the remaining three beta-strands. The other family consists of beta-proteins from the Greek key group, in particular actinoxanthin, the immunoglobulin variable domain and plastocyanin. Their consensus motif, composed of five beta-strands and a turn, is identified, mostly intact, in all Greek key proteins except the trypsins, and interestingly also in three other beta-protein families, the lipocalins, the neuraminidases and the lectins. This result provides new insights into the evolutionary relationships in the very diverse group of all beta-proteins.