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Protein Engineering vol.14 no.3 pp.141–148, 2001 Structural adaptation of enzymes to low temperatures Giulio Gianese1, Patrick Argos1 and Stefano Pascarella1,2,3 1Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Centro di Biologia Molecolare del CNR and 2Centro Interdipartimentale di Ricerca per l’Analisi dei Modelli e dell’Informazione nei Sistemi Biomedici (CISB), Università ‘La Sapienza’, P.le A. Moro 5, 00185 Rome, Italy 3To whom correspondence should be addressed, at the Dipartimento di Scienze Biochimiche. E-mail: [email protected] A systematic comparative analysis of 21 psychrophilic enzymes belonging to different structural families from prokaryotic and eukaryotic organisms is reported. The sequences of these enzymes were multiply aligned to 427 homologous proteins from mesophiles and thermophiles. The net flux of amino acid exchanges from meso/thermophilic to psychrophilic enzymes was measured. To assign the observed preferred exchanges to different structural environments, such as secondary structure, solvent accessibility and subunit interfaces, homology modeling was utilized to predict the secondary structure and accessibility of amino acid residues for the psychrophilic enzymes for which no experimental three-dimensional structure is available. Our results show a clear tendency for the charged residues Arg and Glu to be replaced at exposed sites on α-helices by Lys and Ala, respectively, in the direction from ‘hot’ to ‘cold’ enzymes. Val is replaced by Ala at buried regions in α-helices. Compositional analysis of psychrophilic enzymes shows a significant increase in Ala and Asn and a decrease in Arg at exposed sites. Buried sites in β-strands tend to be depleted of Val. Possible implications of the observed structural variations for protein stability and engineering are discussed. Keywords: cold adaptation/protein engineering/protein stability/psychrophilic enzymes/residue substitution Introduction It is estimated that around 90% of the biosphere exists at temperatures below 10°C. Indeed, the earth’s surface is dominated by low-temperature environments such as the Arctic and Antarctic continents, mountains regions and the marine waters which cover 70% of its area and display, below 1000 m under the sea level, temperatures not exceeding 5°C. Psychrophilic organisms live at such low temperatures, where most other species cannot grow and to survive they need to produce enzymes able to perform efficiently their catalysis under these extreme environmental conditions. At the same temperatures, enzymes from mesophilic or thermophilic organisms are generally unable to sustain a viable metabolism (Feller and Gerday, 1997; Gerday et al., 2000). For these reasons, enzymes synthesized by psychrophilic organisms have considerable biotechnological potential: their ability to work efficiently as catalysts at low temperatures offers advantageous environmental applications and energy savings when used in industrial processes (Marshall, 1997). Whereas important © Oxford University Press progress has been made in elucidating the molecular adaptation mechanism of enzymes produced by extremophiles such as hyperthermophiles (e.g. Jaenicke and Böhm, 1998), the molecular basis of the cold adaptation is still relatively poorly understood. Recent accumulation of structural data on psychrophilic enzymes is beginning to shed light on their functional and structural characteristics (e.g. Feller and Gerday, 1997). The commonly observed features of these cold active enzymes are their increased catalytic efficiency at low temperatures measured as kcat/KM and a significantly increased thermolability which is believed to be a consequence of enhanced peptide chain flexibility. The amount of available structural data, primary and tertiary, on psychrophilic enzymes is now sufficient to undertake a comprehensive and significant comparative analysis. Studies on the structural adaptation of thermophilic enzymes (e.g. Menéndez-Arias and Argos, 1989; Vogt et al., 1997; Szilágyi and Závodszky, 2000) utilized comparative analysis. This approach gave valuable indications on possible adaptive strategies utilized by evolution to stabilize enzymes at high temperatures, thus suggesting rules to be followed by protein engineers to produce modified enzymes with characteristics suitable for biotechnological applications (Fontana, 1991). For example, the neutral protease from Bacillus subtilis was successfully stabilized by two mutations Gly → Ala in loop and helical regions (Margarit et al., 1992). These exchanges were indicated by Menéndez-Arias and Argos (1989) to be involved in the enzyme adaptation to hot environments. This paper describes an analysis of preferred amino acid residue substitutions in a dataset of 21 psychrophilic enzymes extracted from various prokaryotic and eukaryotic species and multiply aligned to homologous mesophilic and thermophilic sequences. To assign the observed preferred exchanges to different structural environments (secondary structure, solvent accessibility and subunit interfaces), homology modeling was utilized to predict the secondary structure and accessibility of amino acid residues in those psychrophilic enzymes for which no experimental three-dimensional structure was available. The results suggest possible general rules for protein engineering experiments aimed at producing enzymes catalytically effective at low temperatures. It should be emphasized that the statistical analysis described in this work can only detect general features of enzyme cold adaptation and overlooks the subtle structural modifications that can be reliably identified by detailed intra-family structural comparisons (e.g. Maes et al., 1999). Materials and methods Data collection Protein sequences were retrieved from the SWISS-PROT, PIR, EMBL and NRL3D databanks using the Sequence Retrieval System (SRS) (Etzold and Argos, 1993). An initial search was carried out with the keywords: ‘psychro’, ‘cold’, ‘arctic’, ‘antarctic’ and the like. The names of cold-adapted species, from which the selected protein sequences were extracted, 141 G.Gianese, P.Argos and S.Pascarella were used in turn as keywords to check further for the presence of other proteins from the same source in the databanks. A literature scrutiny was undertaken to ensure that only proteins with proved ‘cold-adapted’ features and clear enzymatic activity were considered among those previously retrieved. Subsequently, each of the selected cold-adapted proteins was used as query sequences in the program FASTA (Pearson and Lipman, 1988) to collect homologous mesophilic and thermophilic counterparts from the databanks. Within each protein family, the sequences were then multiply aligned using the programs CLUSTAL W (Thompson et al., 1994) or PILEUP in the Genetics Computer Group suite (Deveraux et al., 1984). Sequences sharing less than 35% residue identity to the psychrophilic protein were removed and the remainder were realigned. Such an identity threshold guarantees a sufficiently accurate alignment and structural homology (Vogt et al., 1995). For the same reason, we also excluded incomplete and ambivalently homologous sequences. Proteins from plants were not taken into consideration owing to the ambiguous definition of ‘optimum temperature’ for such organisms. To limit the comparisons to functional enzymatic units, only sequences of mature protein were considered (e.g. signal sequences were removed). Only psychrophilic proteins for which at least one homologous sequence had a known threedimensional structure were used in the analysis. Structural data were taken from the Brookhaven Protein Data Bank (PDB) (Sussman et al., 1998). Multiple sequence alignments were manually refined to optimize the localization of insertion/ deletions. For each protein family, only one cold-adapted representative was chosen to avoid oversampling of the same amino acid exchanges. Among similar psychrophilic enzymes belonging to the same family, the protein with known crystallographic structure or with the lowest optimum growth temperature was selected. The optimum growth temperature assigned to each protein corresponds to the normal living environmental temperature (or to the average of a range of temperatures of the normal habitat) for monocellular and ectothermic organisms and to body temperature for homeothermic organisms. Bacterial optimum temperatures were taken from Bergey’s Manual of Systematic Bacteriology (Krieg and Holt, 1984; Sneath et al., 1986; Staley et al., 1989; Williams et al., 1989), whereas yeasts and filamentous fungi optimum temperatures were taken from the Web site of the Deutsche Sammlung von Mikroorganismen und Zelikulturen GmbH (DSMZ) (URL: http://www.gbf-braunschweig.de/dsmz/dsmzhome.htm). The use of host environmental temperatures has several precedents in the literature (e.g. Querol et al., 1996; Menéndez-Arias and Argos, 1989). Only homologous proteins from organisms with growth temperatures 艌22°C were considered. Preferred amino acid substitutions Favored amino acid substitutions were calculated from the multiple alignments using the method of Argos et al. (1979), adopting the modifications introduced by Menéndez-Arias and Argos (1989) to remedy the excess of data due to very similar sequences from related species with the same growth temperature. Therefore, to cope with statistical overestimation of residue exchanges, all sequences having an identity of 85% or more to each other and the same growth temperature were merged into one. In the amino acid substitution evaluation, this approach counts exchanges in every alignment position only once for each different residue type found, irrespective 142 of its occurrences. For example, if only a Val was observed at an alignment position in four closely related sequences and the psychrophilic one had Ala, only one exchange Val → Ala was counted. In the presence of different amino acids, each exchange type counted once. The substitution matrix was calculated by comparing each protein sequence in a multiple alignment with the psychrophilic counterpart. If the possible pairwise sequence comparisons were n, the cij elements of a temperature-weighted average exchange matrix can be calculated according to Σ (∆T )a n cij ⫽ ij n Σ (1) (∆Tn)2 n where aij is the number of times an amino acid of type i in a mesophilic or thermophilic protein has changed to one of type j in the homologous psychrophilic protein in a pairwise comparison. ∆T is the absolute difference between the optimum growth temperatures of the two species in degrees Celsius. Equation 1 shows that the weight (∆Tn)2 of identified substitutions is variable according to temperature differences. The favored residue substitutions in a single protein family can be calculated by Equation 1. The overall exchange matrix for k protein families was calculated according to Cij ⫽ Σ (c ) (2) ij k k which defines elements of a matrix C giving equal weight to all the families, irrespective the number of sequences or the following equation (Menéndez-Arias and Argos, 1989): [ Σ [Σ Σ Σ (∆T )a k C⬘ij ⫽ n n k ] ] ij k (3) (∆Tn)2 n k which defines elements of a matrix C⬘ assigning larger weight to families with more members. Then, the cross-elements in the C or C⬘ matrices were subtracted from each other to obtain a new matrix Dij (equal to: Cij – Cji) or D⬘ij (equal to: C⬘ij – C⬘ji), representing the net residue substitution flow in the direction from non-cold to cold-adapted species. The standard deviations for the non-zero elements of matrices D and D⬘ were determined. The significance R of each exchange was calculated by dividing each of the D or D⬘ elements by the standard deviation of the respective matrix. Equation 3 was also used in the analysis of the amino acid composition of the sequences (Böhm and Jaenicke, 1994; Vogt et al., 1997). In this case, the aij coefficient was substituted by the difference in amino acid composition between the coldadapted enzyme and thermo/mesophilic counterparts in each comparison. For each protein family of k members, where the kth is the psychrophilic protein, the average composition difference ci for each of the 20 amino acids ai was calculated as k–1 n ci ⫽ [ Σ ∆T N Σ (a ) –N Σ (a ) n⫽1 –1 k –1 n i l l k–1 Σ (∆T n⫽1 n )2 ] i m m (4) Cold adaptation of enzymes where N is the size of the sequence considered and l and m are the number of times the amino acid ai is present in the psychrophilic and in the related protein, respectively. The significance of the observed differences in amino acid composition (temperature weighted and extended to all families) was measured by transforming its value into a Zscore (difference between each value and the overall mean divided by the standard deviation). Model building Secondary structures, solvent accessibility and subunit interface residues were assigned to the psychrophilic proteins with unknown three-dimensional structure by comparative modeling. Homology models were based on the available crystal structures in the selected families. Template and target sequences were multiply aligned and the resulting alignments were checked by visual inspection of superposition of template three-dimensional structures. The program MODELLER version 4.0 (Šali and Blundell, 1993) was used to build the models. Four models at the highest optimization level were built for each target protein. The model displaying the lowest ‘objective function’ value was selected among the four. Model quality was assessed with the program ProsaII (Sippl, 1993). Whenever applicable, multimeric biological units were recreated from the monomers using the symmetry information in the template structures. Secondary structures were determined with the program DSSP (Kabsch and Sander, 1983) and were assigned to α-helix (DSSP symbols H, G, I), β-strand (B and E) or coil (the rest). Solvent accessibility was calculated from atom coordinates with the program NACCESS (Hubbard and Thornton, 1993). Structure sites displaying not more than 0.05 and not less 0.25 fractional accessibility were considered buried and exposed, respectively (Pascarella et al., 1998). During accessibility calculations, only physiological ligands (cofactors, ions, etc.) were mantained. In allowance for residues located at subunit interfaces, they were considered to be located at the interface if they lost at least 15% of the accessible surface area of all their atoms upon subunit association (Menéndez-Arias and Argos, 1989). Water molecules were always excluded from the structures in all calculations. Amino acid exchanges and composition in different structural environments Structural environments taken into consideration were (i) secondary structure (α-helix, β-strand or random coil); (ii) accessibility state (buried or exposed) and (iii) subunit interface. Propensities Pij for a residue exchange from type i to type j were calculated according to Pij ⫽ (C⬘ij)env /(C⬘ij)tot (Na)env /(Na)tot (5) The terms (C⬘ij)env and (C⬘ij)tot represent elements of the exchange matrices calculated for residues observed in the structural environments (env) (i), (ii) or (iii) and for the whole sequences (tot). (Na)env and (Na)tot are the number of amino acids counted in the structural environments and in all sequences, respectively. Residue compositional differences at the structural environments (i) and (ii) were calculated with Equation 4. Results The data selection procedure yielded 21 psychrophilic enzymes from different families and 427 homologous sequences (Table I) accounting for 112 117 amino acid comparisons. Fifty homo- logs, distributed among 15 protein families (Table I), are from thermophilic bacteria able to grow above 60°C and 27 of these belong to hyperthermophilic organisms adapted at temperatures above 80°C (Jaenicke and Böhm, 1998). Six psychrophilic enzymes have known three-dimensional structure while the remaining 15 families include at least one homologous member with solved spatial structure (Table II). Entire D⬘ matrix and counts of amino acid exchanges observed in our dataset are shown in Figure 1. While matrix D is computed without any correction for family size, D⬘ relies on a weighting scheme that gives more emphasis to families bearing a higher number of sequences. Comparison of the two matrices should indicate the residue exchanges biased by statistical noise. Therefore, only the residue exchanges scored by both matrices with a significance higher than 2.0 were considered. Seven exchanges fulfill such a condition and, among these, three have a significance above 3.0 (Table III). To emphasize the residue exchanges affecting protein stability at extreme temperatures, we compared only the thermophilic and psychrophilic enzymes and recalculated the two matrices D and D⬘. This reduced data set contains 65 sequences, 15 from psychrophilic and 50 from thermophilic organisms, accounting for 18 338 residue exchanges. The results confirmed the top four exchanges reported in Table III at a significance level higher than 2.0. Preferred structural environments for the exchanges reported in Table III were analyzed. Secondary structure and solvent accessibility assignments to 15 psychrophilic proteins with no available spatial structure required the application of homology modeling. The percentage of residue identity between the structural templates and the target sequences ranged from 34 to 73% and eight cold enzymes bore more than one structural homolog (Table II). This assured a sufficient degree of modeling accuracy (Šali et al., 1995). Template–target alignments were taken from the literature. In all cases, however, they were checked and, when appropriate, alignments were modified at insertion/deletion regions to optimize the target model or to include more templates (an example is shown in Figure 2). Table III reports the propensities of the most significant exchanges for different structural environments. Fractions of α-helix, β-strand and coil observed in our sample are 33, 23 and 44%, respectively. Sites displaying not more than 0.05 and not less than 0.25 fractional accessibility were considered buried and exposed, respectively (Pascarella et al., 1998). Propensities at subunit interfaces were calculated in the databank subset represented by the 12 enzymes displaying a quaternary structure (Table II) with 263 homologous sequences. Discussion We have described a systematic comparative analysis of 21 psychrophilic enzymes from different families and 427 homologous sequences and structures. This is the largest dataset of psychrophilic enzymes analyzed to date. Our analysis is aimed at detecting general features of structural adaptation of enzymes to low temperatures. Enzyme-specific strategies can only be identified by a detailed intra-family structural comparison. The results indicate three residue exchanges (namely Glu → Ala, Val → Ala, Arg → Lys) scored at a significance level higher than 3.0 by both matrices D and D⬘. Statistical tendencies are also observed for four other residue substitutions involving charged residues (Lys → Ser, Lys → Asn, Arg → Ser) and hydrophobic residues (Val → Ile). These observations strongly suggest that charged residues Glu, Arg and Lys (Table III) 143 G.Gianese, P.Argos and S.Pascarella Table I. List of psychrophilic enzymes used in this work No. Family name Species Growth temperature (°C) Databank code or referencea Number of homologsb 1 2 3 Bacillus psychrosaccharolyticus Alteromonas haloplanctis A23 Vibrio sp. 2693 15 4 6 embl:ab021683 sw:amy_altha sw:pyrb_vibs2 8 (1) 39 13 (5) 383 453 310 6 sw:pyri_vibs2 4 (1) 153 5 6 7 8 9 10 11 12 13 14 15 16 17 Alanine racemase α-Amylase Aspartate carbamoyltransferase (catalytic chain) Aspartate carbamoyltransferase (regulatory chain) β-Galactosidase β-Lactamase Chymotrypsin A Citrate synthase DNA ligase Elastase Isocitrate dehydrogenase 1 3-Isopropylmalate dehydrogenase L-Lactate dehydrogenase P Malate dehydrogenase Ornithine carbamoyltransferase Pyruvate kinase Serralysin (alkaline protease) 18 19 20 21 Subtilisin Triosephosphate isomerase Trypsin I Xylanase 4 aNotation bNumbers Vibrio sp. 2693 Arthrobacter sp. B7 Psychrobacter immobilis A5 Gadus morhua Antarctic bacterium DS2-3R Pseudoalteromonas haloplanktis Salmo salar Vibrio sp. ABE-1 Vibrio sp. I5 Bacillus psychrosaccharolyticus Aquaspirillium arcticum Vibrio sp. 2693 Bacillus psychrophilus Pseudomonas aeruginosa TACII18 Bacillus sp. TA39 Vibrio marinus Salmo salar Cryptococcus adeliae TAE85 15 4 4 5 4 4 4 15 15 4 6 15 4 sw:bgal_artsp sw:ampc_psyim sw:ctra_gadmo sw:cisy_abds2 embl:af126866 nrl3d:1elt sw:idh1_viba1 Wallon et al. (1997) sw:ldhp_bacps sw:mdh_aquar sw:otca_vibs2 sw:kpyk_bacpy embl:psy17314 2 (1) 11 6 13 (2) 22 (8) 15 10 (4) 45 (6) 42 (6) 9 (1) 25 (7) 51 (3) 9 1015 362 245 379 672 236 414 360 318 329 301 586 463 4 15 4 4 sw:subt_bacs9 sw:tpis_vibma sw:try1_salsa embl:cay15434 19 (1) 41 (3) 35 8 (1) 309 256 222 338 is ‘databank:protein code’. in parentheses denote the number of homologous sequences from thermo/hyperthermophilic organisms. tend to be replaced in psychrophilic enzymes. These substitutions occur mainly at exposed sites within α-helices or coil regions. Only the substitution Lys → Asn is favoured at the subunit interface with propensity 1.38. Compositional analysis indicates that Asn is more frequent in psychrophilic than in meso/thermophilic enzymes (Z-score 2.0). Asn is a thermolabile residue and its increased frequency in psychrophiles was already noted in aspartate carbamoyltransferase (Xu et al., 1998). The substitution Arg → Lys was already observed to occur in α-helices in the thermophilic → mesophilic direction (Menéndez-Arias and Argos, 1989). It has been suggested by several authors (e.g. Vogt et al., 1997; Xiao and Honig, 1999) that ion pairs and H-bonds and more generally electrostatic interactions in which charged residues are involved play an important role in protein stabilization, particularly at high temperature, also because of the influence on polypeptide chain flexibility. Likewise, other authors (Feller et al., 1996; Feller and Gerday, 1997; Marshall, 1997) state, on the basis of comparative single-family analysis, that decreased Arg content or decreased Arg/(Lys ⫹ Arg) molar ratio is a feature of cold adaptation. Our results support the view that substitution of charged residues Glu, Arg and Lys be one of the mechanisms of low-temperature adaptation shared by most of the families included in our databank and possibly by most of the coldadapted proteins. The two substitutions involving exclusively hydrophobic residues, namely Val → Ala and Val → Ile, prefer buried sites in α-helices and β-strands, respectively. The replacement Val → Ile was detected in the direction thermophile → mesophile by Menéndez-Arias and Argos (1989) in α-helices. Vogt et al. (1997) assigned it to the opposite direction mesophile → thermophile in β-strands using a different dataset smaller than that used in the present work. Both residues are highly preferred in β-strands (Levitt, 1978). Ile can establish larger Van der Waals contacts with the surrounding residues. It should be 144 Sequence length (monomer) mentioned that a mutation Ile → Val in a short β-strand of chymotrypsin inhibitor 2 (Jackson et al., 1993) stabilized the structure. It is reasonable to expect that the inverse mutation may destabilize some proteins. Val → Ala was also detected by Vogt et al. (1997) in α-helices, although in the opposite direction mesophile → thermophile. Ala has a clear preference for α-helical conformations while Val displays a propensity for β-strands. Psychrophiles show avoidance for Val in buried regions of β-sheets. Compositional analysis indicates that Val is less frequent in buried regions (Z-score –3.0) and in βstrand conformations (Z-score –2.0) of psychrophilic enzymes than in the corresponding regions of thermo/mesophiles. The overall effect of the two mutations considered, Val → Ala, Val → Ile, is the decrease in the number of carbon atoms in the hydrophobic core. It can be calculated from Table III that six side-chain carbon atoms are replaced by five carbons. Structural comparison between salmon and bovine trypsins (Smålas et al., 1994) revealed that the combined effect of three of the six residue exchanges located in the interior of salmon trypsin is the reduction of the side-chain volume. Compositional analysis indicates increased content of Ala particularly at exposed sites in coil regions (Z-score 2.0). An increased presence of Ala at exposed sites in place of hydrophilic residues (see the most significant Glu → Ala substitution in Table III) may enhance surface hydrophobicity. Favorable solvation of the non-polar surface at low temperatures should destabilize the entire structure (Creighton, 1991). This destabilization may increase flexibility. A significant increase in the apolar surface exposed to solvent was observed in citrate synthase from Antarctic bacterium (Russell et al., 1998), in trypsin from Atlantic salmon (Smalås et al., 1994) and in αamylase from Alteromonas haloplanctis (Aghajari et al., 1998). These enzymes display a reduced number of charged residues on the surface compared with their mesophilic and thermophilic counterparts. It is a general assumption that thermophilicity is Cold adaptation of enzymes Table II. (A) List of structurally solved homologs used as templates to build homology models of psychrophilic proteins and (B) list of psychrophilic enzymes with known structure (A) Protein name Species PDB code Structure resolution (Å) Identitya (%) Number of subunits Alanine racemase Aspartate carbamoyltransferase (catalytic chain) Aspartate carbamoyltransferase (regulatory chain) β-Galactosidase β-Lactamase Bacillus stearothermophilus Escherichia coli Escherichia coli Escherichia coli Enterobacter cloacae Escherichia coli Bos taurus Bacillus stearothermophilus Escherichia coli Thiobacillus ferrooxidans Bacillus coagulans Salmonella typhymurium Escherichia coli Thermus thermophilus Bacillus stearothermophilus Lactobacillus casei Thermotoga maritima Pyrococcus furiosus Escherichia coli Pseudomonas aeruginosa Escherichia coli Oryctolagus cunuculus Saccaromyces cerevisiae Leishmania mexicana Pseudomonas aeruginosa Serratia marcescens Serratia sp. E-15 Bacillus sp. KSM-K16 Bacillus lentus (subtilisin Savinase) Bacillus lentus (subtilisin BL) Bacillus subtilis (subtilisin DY) Penicillium simplicissimum Thermoascus aurantiacus 1bd0 8atc 8atc 1bgl 1bls 2bls 1ab9 1b04 1ai3 1a05 2ayq 1cnz 1cm7 1xaa 1ldn 1llc 1a5z 1a1s 2otc 1ort 1pky 1a5u 1a3x 1pkl 1kap 1sat 1srp 1mpt 1gci 1st3 1bh6 1b31 1tax 1.60 2.50 2.50 2.50 2.30 2.00 1.60 2.80 1.90 2.00 3.00 1.76 2.06 2.10 2.50 3.00 2.10 2.70 2.80 3.00 2.50 2.35 3.00 2.35 1.64 1.75 2.00 2.40 0.70 1.40 1.75 1.75 1.14 57 73 54 34 39 37 67 48 73 62 55 53 53 51 70 56 42 43 36 36 48 43 41 41 67 56 55 41 41 41 38 40 38 2 6 6 4 1 1 1 1 2 2 2 2 2 2 4 4 4 12 3 12 4 4 4 4 1 1 1 1 1 1 1 1 1 Protein name Species PDB code Structure resolution (Å) Number of subunits α-Amylase Citrate synthase Elastase Malate dehydrogenase Triosephosphate isomerase Trypsin I Alteromonas haloplanktis A23 Antarctic bacterium DS2-3R Salmo salar Aquaspirillium arcticum Vibrio marinus Salmo salar 1aqm 1a59 1elt 1b8p 1aw2 2tbs 1.85 2.09 1.61 1.90 2.65 1.80 1 2 1 2 2 1 Chymotrypsin A DNA ligase (N-terminal domain) Isocitrate dehydrogenase 1 3-Isopropylmalate dehydrogenase L-Lactate dehydrogenase P Ornithine carbamoyltransferase Pyruvate kinase Serralysin (alkaline protease) Subtilisin Xylanase (B) aPercentage of residue identity shared with the sequence of the psychrophilic homolog. correlated with rigidity of the protein and that psychrophilicity should be reflected by a more flexible protein structure, the consequence of which is considered by many authors as thermolability. However, the question remains as to whether the thermal instability is a real consequence of the structure flexibility or is correlated with the lack of selective pressure related to the stability. A more flexible structure, in fact, reduces the energetic cost of the conformational changes required to interact with the substrate. The higher specific activity can be explained by a lower activation energy resulting from an easier accommodation of the substrate at low and moderate temperatures. However, theoretical analyses were unable to detect an overall increase in flexibility in salmon trypsin compared with bovine trypsin (Heimstad et al., 1995) but suggested that there may be significant differences on a more detailed level. It has been suggested that enhancement of flexibility to achieve optimum enzymatic activity at low temperatures implies a decreased Pro content, especially at loop regions (Aghajari et al., 1998). No significant exchange involves Pro residues in our data set, although the exchange Pro → Ala has significance equal to 2.0 only in matrix D⬘. Compositional analysis suggests a decreased Pro content only with a marginal statistical significance (Z-score –0.7). It cannot be excluded, however, that the lack of significant residue exchanges involving Pro is a result of scant statistics. Indeed, net residue substitution flux calculated over single families indicates that Pro → Ala exchange is significant only in the α-amylase family. Family specific adaptations are also evident in the psychrophilic triose-phosphate isomerase (Alvarez et al., 1998) that displays a Ser → Ala substitution. Site-directed mutagenesis experiments proved that this substitution is relevant for 145 G.Gianese, P.Argos and S.Pascarella Fig. 1. (a) Significance of the D⬘ matrix exchanges and (b) counts of residue substitutions observed from thermo/mesophilic to psychrophilic enzymes. Amino acid residues are indicated with the one-letter code. 146 Cold adaptation of enzymes Table III. Most significant amino acid substitutions From meso/thermophile to psychrophile Glu → Ala Val → Ala Arg → Lys Lys → Ser Lys → Asn Arg → Ser Val → Ile Significancea Propensityb D⬘ D Helix Sheet Coil 5.7 3.9 3.4 2.6 2.4 2.2 2.1 5.0 4.1 3.0 3.4 2.5 2.6 2.2 2.20 1.39 1.48 1.09 1.45 1.23 0.92 0.10 0.97 0.88 0.71 0.51 0.49 2.38 0.55 0.72 0.70 1.08 0.91 1.09 0.35 Favored regions Helix Helix Helix Helix/coil Helix Helix/coil Sheet Propensityc Buried Exposed 0.20 1.79 0.16 0.11 0.43 0.16 2.05 1.87 0.46 1.75 1.84 1.58 1.78 0.22 Favored accessibility state Exposed Buried Exposed Exposed Exposed Exposed Buried aThe significance of residue substitutions is reported as the ratio between the value and the standard deviation of the non-zero elements of the matrices D and D⬘. Only substitutions with a significance ⬎2.0 in both matrices D and D⬘ are reported. bPropensity is defined as the ratio between the fraction of an amino acid exchange at each type of secondary structure and the fraction of secondary structural content in all the three-dimensional structures. cPropensity is defined as the ratio between the fraction of an amino acid exchange at a buried or exposed position and the fraction of residues found at the respective position in all the three-dimensional structures. Fig. 2. Comparison between (a) part of the multiple amino acid sequence alignment utilized in the present work for model building and (b) the corresponding portion reported by Wallon et al. (1997). Target sequence is 3-isopropylmalate dehydrogenase from the psychrophilic Vibrio sp. I5 (code VIBI5) and it is aligned to the structural templates from Thermus thermophilus (THETH), Bacillus coagulans (BACCO), Thiobacillus ferrooxidans (THIFE), Salmonella typhimurium (SALTY) and Escherichia coli (ECOLI). The sequence segment encompassed by positions 35–40 is folded as a β-strand. Residues are indicated in one-letter code and numbering system refers to the Vibrio sp. I5 sequence. low-temperature adaptation. Indeed, it is clear that adaptation to extreme environments can be achieved with different strategies in different enzyme families (Argos et al., 1979; Jaenicke and Böhm, 1998). For example, the exchange Ala → Gly was detected in an α-helix of malate dehydrogenase (MDH) from Aquaspirillium arcticum (Kim et al., 1999). Compared with the Thermus flavus MDH, this Gly represents the only mutation among the residues interacting with the oxaloacetate substrate. The authors interpreted this observation in terms of increased local flexibility that should contribute to the catalytic efficiency. The evolutionary strategy of adaptation to low temperatures does not seem to be merely the inverse of adaptation to high temperature. Psychrophilic enzymes evolved at the low boundaries of the biological temperature range and had to face peculiar thermodynamic challenges (Gerday et al., 1997). While thermophilic proteins need to optimize thermostability to prevent hot denaturation, psychrophiles need to compensate for the reduction in chemical reaction rate inherent to low temperatures and to resist cold denaturation. Two of our seven significant residue exchanges (Table III), namely Arg → Lys and Val → Ile, were detected by Menéndez-Arias and Argos (1989) and Vogt et al. (1997) in the equivalent direction thermophile → mesophile. In particular, Arg → Lys was observed by Menéndez-Arias and Argos (1989) in α-helices, as in our case, while Vogt et al. (1997) assigned it to both α-helices and β-strands. Val → Ile was observed by MenéndezArias and Argos (1989) in α-helices while in our sample it occurs in β-strands. It can be speculated that the psychrophilicspecific substitutions meet the peculiar requirements of enzyme function at low temperatures such as resistance to cold denaturation, local flexibility, balancing of excess flexibility and the like. Our research suggests that a common adaptive mechanism of enzymes to low temperature consists of reduction of charged residues (mainly Arg, Glu and Lys) at exposed sites in α-helix or coil regions. Vogt et al. (1997) stress that the strategy to gain stability in thermophilic enzymes exploits the increase of the number of H-bonds. The preferred amino acid exchanges observed from thermo/mesophiles to psychrophiles and compositional analysis indicate also a decreased number of side-chain potential H-bonds and salt bridges in cold-adapted enzymes. Indeed, it can be calculated from Table III that 10 side-chain N ⫹ O atoms in meso/thermophiles are replaced by five N ⫹ O side-chain atoms in psychrophiles. In this respect, the strategy of cold adaptation seems to use the same principle as the hot adaptation, namely an increase in the number of H-bonds/ion pairs on going from low to high temperatures. Indeed, a smaller number of electrostatic interdomain interactions was found in α-amylase from Alteromonas haloplanctis (Aghajari et al., 1998), whereas a significant decreased number of intersubunit ion pairs and ion pair networks was observed in malate dehydrogenase from Aquaspirillium arcticum (Kim et al., 1999) and in citrate syntase from Antarctic bacterium (Russell et al., 1998). It is interesting to note that the recalculation of amino acid exchanges using only the comparison among thermophilic and psychrophilic sequences confirmed the top four exchanges reported in Table III. The data set now contains only 15 families with a total of 65 sequences, yet the statistical trend is mantained. This strengthens the view that the transition from thermo- to psychrophilic enzymes is achieved mainly by a decrease in electrostatic interactions and possibly by alteration of the tight packing of side-chains in the hydrophobic core. It is suggested that protein engineering aimed at producing a cold-adapted enzyme should plan at first the replacement of one or more of the charged residues Arg, Glu, Lys at exposed sites on α-helices with one of the amino acids indicated in 147 G.Gianese, P.Argos and S.Pascarella Table III. Simultaneously or alternatively, replacement of Val at buried sites on α-helices with Ala can also be tested. It should be mentioned that it has been demonstrated by ‘evolutionary engineering’ (Taguchi et al., 1999) that several alternative strategies are practicable to achieve psychrophiliclike enzymes. Indeed, a psychrophilic-like subtilisin ‘evolved’ from a mesophilic one upon incorporation of three mutations (two Ala → Thr and Ala → Val), none of which is observed in our sample (Table III). However, this cold-adapted enzyme displayed only a 70% increase in kcat/KM at 10°C over the starting mesophilic enzyme, while normally psychrophilic enzymes display a several-fold increase compared with the mesophilic counterpart at the same temperature. Perhaps Nature adopted the most expedient mutations amongst different alternatives. Acknowledgements This work was partially supported by grants from the Consiglio Nazionale delle Ricerche (grant No. 97.02338.12) and from the Ministero dell’Università e della Ricerca Scientifica e Tecnologica. We are indebted to Professor G.Marino and Dr L.Birolo for introducing us to the problem of psychrophilic enzyme adaptation during a scientific collaboration. We are grateful to Professor F.Bossa for support and critically reviewing the manuscript. 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