Implication of Molecular Conservation on Computational Designing of Haloarchaean Urease with Novel Functional Diversity

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Implication of Molecular Conservation on Computational Designing of Haloarchaean Urease with Novel Functional Diversity
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  http://www.TurkJBiochem.comISSN 1303–829X (electronic) 0250–4685 (printed) 5 Implication of Molecular Conservation on Computational Designing of Haloarchaean Urease with Novel Functional Diversity [Geliştirilmiş fonksiyonel çesitlilik açısından Haloarchaean üreaz’ının hesaplamalı dizaynında moleküler korumanın etkisi*] Research Article [Araştırma Makalesi] Türk Biyokimya Dergisi [Turkish Journal of Biochemistry–Turk J Biochem] 2012; 37 (2) ; 283–289. Yayın tarihi 30 Mart, 2012 © TurkJBiochem.com [Published online 30 March, 2012] Paulchamy Chellapandi 1 , Jayachandrabal Balachandramohan 1 1 Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli-620024, Tamil Nadu, India Yazışma Adresi [Correspondence Address] Paulchamy Chellapandi Department of Bioinformatics, School of Life Sci - ences, Bharathidasan University, Tiruchirappalli-620024, Tamil Nadu, IndiaTel. +91-431-2407071 Fax. +91-431-2407045 Email: pchellapandi@gmail.com *Translated by[Çeviri]  Aylin Sepici Registered: 14 October 2011; Accepted: 3 November 2011[Kayıt Tarihi : 14 Ekim 2011; Kabul Tarihi : 3 Kasım 2011] ABSTRACT Objective:  The objective was to design an enzyme construct with diverse function from urease sequences of haloarchaean,  Haloarcula marismortui  ATCC 43049 based on its conserved domain consisting metal-binding region and active sites. Methods: Complete urease sequences of haloarchaea were retrieved from National Center for Biotechnology Information (NCBI) and then homology models generated, and validated. The best protein models were selected for docking with respective substrates using Ligand Fit program. The lowest energetic conformers were generated from these protein models by molecular dynamics methods. Urease construct-substrate complex was chosen based on the mode of catalysis, types of molecular interactions, and binding energy. Results: The resulted construct has a monomeric structure consisting of 3 helixes and 6 turns with 97 amino acids in length. The side chains of Asp49, Gly50 and Gln51 were predicted as functional residues in this construct. Urease construct was predicted to show catalytic function as similar to aliphatic nitrile hydradase and acrylamide hydro-lyase. Binding affinity of construct was more significant, which was better than to native urease. Urease construct was showed high binding affinity with semicarbazide and acrylamide wherein it has formed favorable hydrogen bonds. Conclusion: Substrate-binding region and active sites in the conserved domain of Haloarchaean ureases are evolutionarily conserved at sequence as well as structural level. Substrate docking study supports the strong molecular interactions between construct and relative substrates. Thus, the present approach provides an insight to design urease construct with diverged catalytic function. Key Words:  Molecular docking, urease, nitrilase, molecular evolution, enzyme design, Haloarchaea Conflict of interest: Authors have no conflict of interest.   ÖZET Amaç: Bu çalışmada,  Haloarcula marismortui  ATCC 43049’in metal bağlayan bölge ve aktif bölge gibi korunmuş domainleri esas alınarak haloarchaeanın üreaz dizilimine ters fonksiyonlu enzim yapısının tasarlanması amaçlandı. Yöntem: Haloarchaea ait üreaz dizisi, Ulusal Biyoteknoloji Bilgi Merkezinden (NCBI) alındı ve homolog modeller geliştirilerek doğrulama yapıldı. Docking için en uygun  protein modelleri Ligand Fit programı kullanılarak ilgili substratlar ile seçildi. Bu protein modellerinden en düşük enerjili olan uyumlu modeller moleküler dinamik metodlar ile oluşturuldu. Üreaz yapı-substrat kompleks modeli enerji bağlama kapasitesi, moleküler etkileşim tipleri ve kataliz tipi esas alınarak seçildi. Bulgular: Meydana getirilen yapı 97 amino asit uzunluğunda 3 heliks ve 6 dönüş içeren monomer özelliğindedir. Bu yapıda Asp49, Gly50 ve Gln51 yan zincirleri fonksiyonel kalıntılar olarak öngörüldü. Üreaz yapısının ise alifatik nitril hidrataz ve akrilamid hidroliyaza benzer katalitik fonksiyonları olduğu düşünüldü. Oluşturulan üreazın bağlanma eğilimi esas üreazdan daha iyi olarak anlamlıydı. Ayrıca üreaz semikarbazid ve akrilamid ile yüksek bağlanma eğilimi gösterdi ve uygun hidrojen bağları oluşturdu. Sonuç:  Haloarchaean üreaza ait korunmuş domainde bulunan substrat bağlayıcı bölge ve aktif bölge yapısal olduğu kadar, dizilim düzeyinde de evrimsel olarak korunmuştur. Substrat docking çalışmaları oluşturulan yapı ve ilgili substrat arasında sağlam moleküler etkileşimlerin olduğunu desteklemektedir. Bu çalışma üreaz yapısının tasarlanmasına farklı katalitik fonksiyonlar üzerinden değişik bakış açıları sağlamaktadır. Anahtar sözcükler: Moleküler docking; üreaz, nitrilaz; moleküler evrim; enzim tasarımı; haloarchaea Çıkar çatışması: Yazarlar herhangi bir çıkar çatışması bildirmemektedirler. doi: 10.5505/tjb.2012.09797  Turk J Biochem, 2012; 37 (2) ; 283–289. Chellapandi and Balachandramohan 6 Introduction The maturation of enzyme technology is shown by the development of the theory concerning how enzymes function and how this is related to their primary structure through the formation and configuration of their three- dimension structure [1,2]. The design of artificial enzyme is based on the knowledge about the structure, architecture and functional properties of biological enzymes. It is well known that the enzymes contain a binding site and a catalytic site consisting of two or more catalytic amino acid groups [3,4]. Exploitation of the diverse reactivities of metal center cofactors presents a profitable strategy to introduce catalytic activity into  proteins. Several different potential reactivities toward a single substrate often exhibit on metal centre [5,6]. Hence, computer-aided enzyme modeling has taken an important effort to design metalloenzymes so as to perform chemical reactivity with good catalytic efficiency in biotransformation processes. Nickel is a key metal involved in many of the biochemical  process in archaea, and urease (urea amidohydrolase; EC 3.5.15) is one of the nickel-dependent metalloenzymes in haloarchaea. Apart from urease, other archaeal nickel-dependent enzymes are more diverse in nature so that urease has taken more advantages for a rational enzyme designing [7,8].   Urease catalyzes the hydrolysis of urea to yield ammonia and carbamate, which spontaneously hydrolyzes to form carbon dioxide and a second molecule of ammonia [9]. It is composed of three subunits, encoded  by the genes ure A, ure B, and ure C. The biosynthesis of a functional urease also requires the presence of four additional genes ( ure DEFG) [10]. The gene ure E encodes a nickel carrier protein [11], while ure DFG encode a chaperone complex that keeps urease in a configuration competent to accept a nickel ion and also requires carbamylation for efficient nickel incorporation [12,13].Unfortunately, naturally available enzymes are usually not optimally suited for industrial applications due to the less stability under process conditions, when applying them in biotransformation reactions in industry [14]. Though protein engineering technologies can be used to alter variety of enzyme properties simultaneously, the appropriate screening parameters such as mutant library construction and variant selection should be employed [2]. Hence, the successful designs of small (less than 75 residues) monomeric proteins [15], protein oligomers [16], and the redesign of natural proteins to confer novel functionalities [17] have been achieved  by the development and use of computational methods for searching the sequence space associated with a  particular target structure.The generation of active biocatalysts from dramatically reduced amino acid alphabets provides strong support for the idea that primordial enzymes are made from only a handful of building blocks [1,14,16]. The binding of a substrate close to functional groups in the enzyme causes catalysis by so-called proximity effects. The success of current protein design methods based largely on optimizing the molecular energy potentials suggested that the proposed natural design properties are not necessary conditions for producing well-folded and perhaps even functional artificial proteins [17, 18]. It is therefore possible to design similar biocatalysts from small molecule mimics of enzyme active sites  by combining in a small molecule and evolutionary conservation of sequences. In this context, we have aimed to use computer-aided modeling of urease constructs with diverse substrate-specificity based on evolutionary conservation of urease sequences at nickel- and substrate-binding regions. Materials and Methods  Evolutionary conservation analysis Complete haloarchaean urease sequences were retrieved from GenPept of National Center for Biotechnology Information (NCBI). Multiple sequence alignment was carried out for selected sequences with complete deletion of gaps and correction in multiple substitutions using ClustalX 2.0 software [19]. The aligned sequences were iterated at each alignment step and manually inspected to delete the low scoring sequences. Homogeneous  patterns among all sequences were searched by Neighbor  joining (NJ) algorithm to construct a phylogenetic tree with 1000 bootstraps values using MEGA 4.0 software [20]. The NJ algorithm calculates distances (percent divergence) between all pairs of sequence from a multiple alignment and applies it to the distance matrix. Because NJ method only gives strictly dichotomous trees (never more than 2 sequences join at one time), a multifurcation (several sequences joining at the same  part of the tree) cannot be exactly represented. Using conserved domain search tool [21], conserved domains architecture as well as metal-binding templates of query sequences was searched from NCBI-CDD (Conserved Domain Database) [22].  Molecular modeling and enzyme designing  PSI-BLAST tool with a default parameter was used to search suitable protein data bank (PDB) templates for structure modeling from the sequences [23]. ModWeb is an automatic comparative protein modeling server which was used to build three dimensional structures from query sequences   [24]. It enables a thorough exploration of fold assignments, sequence–structure alignments and conformations, with the aim of finding the model with the best evaluation score. A representative model for each alignment is chosen by ranking based on the atomic distance-dependent statistical potential Discrete Optimized Protein Energy (DOPE). The fold of each model is evaluated using a composite model quality criterion that includes the coverage of the modeled sequence, sequence identity implied by the sequence–   Turk J Biochem, 2012; 37 (2) ; 283–289. Chellapandi and Balachandramohan 7 structure alignment, the fraction of gaps in the alignment, the compactness of the model and various statistical potential Z-scores. Active site residues of selected models were predicted by ProFunc server, which helps to identify the likely biochemical function of a protein from its three-dimensional structure [25]. Crystallographic protein structures whose catalytic domains are similar to metal- and substrate-binding sites were compared with the models. Amino acid residues exclude metal- and substrate-binding regions and active sites have been removed from modeled proteins through atomic coordinates. Amino acid residues corresponding to the selected atomic coordinates were further used to generate 3D homology modeling structure using Prime  program in Maestro software package (Schrodinger Inc.). The resulting model was evaluated using Structural Analysis and Verification Server (SAVS) (http://nihserver.mbi.ucla.edu/SAVES/), and then superimposed on the corresponding PDB template with Dali pairwise comparison tool in DALITE server (http://ekhidna.biocenter.helsinki.fi/dali_lite/start).  Molecular dynamics simulation   of enzyme constructs Structural conformers of the models were generated by Discovery Studio software using CHARMM force field, and steepest descent as well as adopted basis Newton-Raphson algorithms. Distance constraint was between  N-terminal to C-terminal and dihedral restraint was started from C to Cα (Ф) of first amino acid residue and Cα to N (ψ) of second amino acid residue until the last amino acid residue in a molecular dynamic ensemble. After molecular dynamic simulation, the energy conformer 1 (lowest one) was selected for computing  binding energies of construct-substrate complexes.  Molecular docking studies Urea-related substrate strutures in MOL2 files were retrieved from KEGG database using SIMCOM software (http://www.genome.jp/tools/simcomp) and then converted to PDB format. AutoDock software 4.0 implemented with Genetic algorithm and AMBER force field was used to dock substrate into construct. Genetic Algorithm is adaptive heuristic search premised on the evolutionary ideas of natural selection and its basic concept is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. Binding site (cavity) of each construct was selected within an enegy grid and a flexible substrate prefered to dock into it using Ligand Fit program. Smart energy minimization algorithm was used to refine the orientation of the substrate in the receptor site after finding good docking models. The quality of docking models was evaluated by computing interaction distances, binding energy terms and inhibition constants of each construct-substrate complex. Results  Analysis of molecular conservation Eight nickel-dependent enzymes (coenzyme F 420 reducing hydrogenase, F 420 non-reducing hydrogenase, methyl-coenzyme M reductase, hydrogenase maturation  protease, carbon monoxide dehydrogenase, rubredoxin, urease and acetyl-CoA decarbonylase/synthase) were entries available for archaea in NCBI database. Text mining of this study pointed out many urease sequences including alpha, beta and gamma subunits for haloarchaea among archaeal domain. Protein sequences (NCBI accession YP_134542, BAC84959 and Q75ZQ4) have shown a good structural identity with corresponding crystallographic structures, which was ranged from 58 to 60% (Table 1). Metal-binding domain of these sequences was existed at the position 5-85 amino acids corresponding to the PDB template 2FVH (A). Urease sequence of  Haloarcula marismortui  ATCC 43049 (accession YP_134542) was most likely suited for rational enzyme design because of it has the shortest amino acid length to cover metal-and substrate- binding sites. The actual length of selected region for modeling was 95 amino acid residues. Construct was  predicted to show similarity (e-value 1.27e-38; bit score 153; CD length 96 amino acids) to Uraese_gamma subunit (CD00390), a nickel dependent metalloenzymes (Figure 1). Amino acids Asp49, Gly50 and Gln51 were  predicted as active site (nest) residues that were similar to PDB template 2FVH (A). Conservation score of  predicted functional residues was 2.092 (Table 2). Due to a low identity and modeling score, positions beyond active sites and substrate-binding regions, the rest of the modeled proteins have been neglected from this study.  Phylogenetic analysis In phylogenetic tree, the sequences of urease from haloarchaea were formed three separate clades such as sub units of alpha, beta and gamma. The sequence (construct) of  Haloarcula marismortui  ATCC 43049 was typically clustered within halophilic archaea and then with  Metallosphaera sedula  DSM 5348 (Figure 2). Gamma ureases were shared their phylogenic resemblance with alpha and beta ureases of haloarchaea and showed their functional uniqueness. As the sequences of alpha and beta ureases were distantly related with gamma urease, a clade formed by them was not included in this phylogenetic tree.  Structural quality and accuracy of urease construct  The sequence of urease construct was highly similar to the PDB template 2FVH (A) wherein we calculated sequence identity 60.4%, e-value 7.23e-19 and total energy -4061.003 kJ/mol. When its homology model was superimposed with 2FVH (A) (urease, gamma subunit; 1.80A), it was predicted to show 21.6 Z-score  Turk J Biochem, 2012; 37 (2) ; 283–289. Chellapandi and Balachandramohan 8 Table 1. Homology modeling data for predicting 3D structure from urease sequences NCBIAccessionNo. of Amino acidTemplate IDIdentity (%)Target Posi-tion MPQS*Z-Dope**Urease alpha-subunit YP_001190983 555 4ubpC 56 1-555 1.69 -0.75Q18EB9 570 1a5lC 57 5-570 1.63 -0.5Q75ZQ5 568 1a5lC 57 4-568 1.62 -0.38BAC84958 568 1a5lC 57 4-568 1.62 -0.38YP_134541 568 1a5lC 57 4-568 1.62 -0.38Q3IRZ5 570 1a5lC 57 5-570 1.63 -0.42 Urease beta-subunit CAJ53713 126 1ejxB 57 6-104 1.51 -0.64Q75ZQ6 138 4ubpB 58 5-108 1.49 -0.66BAC84957 138 4ubpB 58 5-108 1.49 -0.66YP_134540 138 4ubpB 58 5-108 1.49 -0.66Q3IRZ6 132 1ejxB 63 9-109 1.52 -0.37ABP95060 215 4ubpA 59 1-99 1.28 -1.36YP_001190984 215 4ubpA 59 1-99 1.27 -1.28 Urease gamma-subunit YP_659289 108 4ubpA 58 1-99 1.77 -1.84Q75ZQ4 128 2fvhA 60 2-99 1.66 -1.97BAC84959 128 2fvhA 60 2-99 1.66 -1.97YP_134542 128 2fvhA 60 2-99 1.66 -1.97YP_326659 109 2fvhA 59 3-99 1.74 -1.64 *  ModPipe Quality Score, * *  a normalized DOPE (Discrete Optimized Protein Energy) score 1EF2_C 4 TPREK DK LLLFTAAL V  AER R LARGLKL NYP ESVALI S  AFIMEGARDG.[2].VASLMEEGRHVLTREQVM E GVPE  M  IPDI 80 Construct 4 TAKEQ ER LTVFTAAE V  ARR R KERGVPL NHP EAVAYI S DWCIERGRDG.[2].VAEIRSGASKLLGREDVM D GVPE  M  IDMI 80 4UBP_A 5 NPAEK EK LQIFLASE L LLR R KARGLKL NYP EAVAIITSFIMEGARDG.[2].VAMLMEEGKHVLTRDDVM E GVPE  M  IDDI 81 gi 17402589 4 EQREA  EK LALHNAGF L  AQK R LARGLRL NY TEAVALIAAQILEFVRDG.[3].VTDLMDLGKQLLGRRQVLPAVPHLLETV 81 gi 418162 4 TPREK DK LLLFTAGL V  AER R LARGLKL NYP EAVALI S CAIMEGARDG.[2].VAQLMSEGRTLLTAEQVM E GVPE  M  IKDI 80 gi 82702368 4 TPREK DK LQIFTAGL L  AER R KARGLRL NYP EAVALITCAILEGARDG.[2].VAELMSEGRKVLTRADVM E GVPE  M  IPDI 80 gi 90591216 4 TPRES EK LLLHLAGE L  AAK R KARGLKL NYP ETIAYI S SHLLEAARDG.[2].VAELMNYGATLLTRDDVM E GIAE  M  IHDV 80 gi 6460755 4 TERER DK LLIFTAAQ L  ARE R RARGLKL NHP EAVALITAEVLEGIRDG.[2].VEDLMSFGAAILTPDDVL D GVPELIHEI 80 gi 2636191 4 TPVEQ EK LLIFAAGE L  AKQ R KARGVLL NYP EAAAYITCFIMEGARDG.[2].VAELMEAGRHVLTEKDVM E GVPE  M  LDSI 80 gi 14024886 4 TPREK DK LLIAMAAI V  ARK R LERGVKL NHP EAIALITDFVVEGARDG.[2].VAELMEAGAHVVTRAQVMQGIAE  M  IHDV 80 1EF2_C 81 Q V E  ATFPDGSKLVTVHNPI 99 Construct 81 Q V E PVFPDGTKLVTVHDPI 99 4UBP_A 82 Q  A  E  ATFPDGTKLVTVHNPI 100 gi 17402589 82 Q V E GTFMDGTKLITVHDPI 100 gi 418162 81 Q V E CTFPDGTKLVSIHDPI 99 gi 82702368 81 Q V E  ATFPDGTKLVTVHNPI 99 gi 90591216 81 Q I E  ATFPDGTKLVTVHSPI 99 gi 6460755 81 Q V E GTFPDGTKLVTVHDPI 99 gi 2636191 81 Q V E  ATFPDGVKLVTVHQPI 99 gi 14024886 81 Q V E  ATFPDGTKLVTVHAPI 99 Figure 1 . Multiple sequence alignment of urease constructs with functionally related domain sequences. (Shaded regions are showing alpha-gamma subunit interface)  Turk J Biochem, 2012; 37 (2) ; 283–289. Chellapandi and Balachandramohan 9 Table 2. Data mining for searching metal-binding and active site similarity regions of urease models NCBI AccessionTemplate IDMetal-binding regionActive site*Conservation ScoreUrease alpha-subunit (PSSM-ID: 30031 )YP_001190983 4ubpC 135-370 Ala150, Gly151, Phe152 4.375Q18EB9 1a5lC 135-370 Gln364, Ala365, Met366 5.291Q75ZQ5 1a5lC 135-370 Lys91, Arg92, Arg93 3.640BAC84958 1a5lC 135-370 Gln362, Ala363, Met364 4.624YP_134541 1a5lC 135-370 Gln362, Ala363, Met364 4.624Q3IRZ5 1a5lC 135-370 Gln364, Ala365, Met366 5.264 Urease beta-subunit (PSSM-ID: 73201 )CAJ53713 1ejxB 4-100 Gly98, Leu99, Val100 3.405Q75ZQ6 4ubpB 4-100 Lys91, Arg92, Arg93 3.640BAC84957 4ubpB 4-100 Lys91, Arg92, Arg93 3.640YP_134540 4ubpB 4-100 Lys91, Arg92, Arg93 3.640Q3IRZ6 1ejxB 8-104 Asp95, Arg96, Ile97 4.625ABP95060 4ubpA 95-195 Asn97, Pro98, Ile99 0.843YP_001190984 4ubpA 95-195 Asn97, Pro98, Ile99 0.843 Urease gamma-subunit (PSSM-ID: 63883 )YP_659289 4ubpA 5-85 Arg26, Gly27, Val28 2.582Q75ZQ4 2fvhA 5-85 Asp49, Gly50, Gln51 2.092BAC84959 2fvhA 5-85 Asp49, Gly50, Gln51 2.092YP_134542 2fvhA 5-85 Asp49, Gly50, Gln51 2.092YP_326659 2fvhA 5-85 Gly72, Val73, Pro74 2.169 *Active sites were predicted by ProFunc server    Haloarcula marismortui Haloquadratum walsbyi DSM 16790 Natronomonas pharaonis DSM2160 Metallosphaera sedula DSM 5348 Haloarcula marismortui ATCC 43049 Haloarcula marismortui Alpha sub unit  Haloquadratum walsbyi DSM 16790 Natronomonas pharaonis DSM 2160 Haloarcula marismortui Haloarcula marismortui Haloarcula marismortui ATCC 43049 Gamma sub unit  Haloarcula marismortui Haloarcula marismortui Haloquadratum walsbyi DSM 16790 Metallosphaera sedula Metallosphaera sedula DSM 5348 Natronomonas pharaonis DSM 2160 Beta sub unit  Haloarcula marismortui ATCC 43049 0.5 Figure 2.  Phylogenetic tree constructed with NJ algorithm based on the urease sequences of archaea
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