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Llopiz A, Ramírez-Martínez MA, Olvera L, Xolalpa-Villanueva W, Pastor N, Saab-Rincon G. The Role of a Loop in the Non-catalytic Domain B on the Hydrolysis/Transglycosylation Specificity of the 4-α-Glucanotransferase from Thermotoga maritima. Protein J 2023; 42:502-518. [PMID: 37464145 PMCID: PMC10480278 DOI: 10.1007/s10930-023-10136-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/27/2023] [Indexed: 07/20/2023]
Abstract
The mechanism by which glycoside hydrolases control the reaction specificity through hydrolysis or transglycosylation is a key element embedded in their chemical structures. The determinants of reaction specificity seem to be complex. We looked for structural differences in domain B between the 4-α-glucanotransferase from Thermotoga maritima (TmGTase) and the α-amylase from Thermotoga petrophila (TpAmylase) and found a longer loop in the former that extends towards the active site carrying a W residue at its tip. Based on these differences we constructed the variants W131G and the partial deletion of the loop at residues 120-124/128-131, which showed a 11.6 and 11.4-fold increased hydrolysis/transglycosylation (H/T) ratio relative to WT protein, respectively. These variants had a reduction in the maximum velocity of the transglycosylation reaction, while their affinity for maltose as the acceptor was not substantially affected. Molecular dynamics simulations allow us to rationalize the increase in H/T ratio in terms of the flexibility near the active site and the conformations of the catalytic acid residues and their associated pKas.
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Affiliation(s)
- Alexey Llopiz
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, 62209, Cuernavaca, Morelos, Mexico
| | - Marco A Ramírez-Martínez
- Centro de Investigación en Dinámica Celular, IICBA, Universidad Autónoma del Estado de Morelos, 62209, Cuernavaca, Morelos, Mexico
| | - Leticia Olvera
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, 62209, Cuernavaca, Morelos, Mexico
| | - Wendy Xolalpa-Villanueva
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, 62209, Cuernavaca, Morelos, Mexico
| | - Nina Pastor
- Centro de Investigación en Dinámica Celular, IICBA, Universidad Autónoma del Estado de Morelos, 62209, Cuernavaca, Morelos, Mexico
| | - Gloria Saab-Rincon
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, 62209, Cuernavaca, Morelos, Mexico.
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2
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Durairaj J, de Ridder D, van Dijk AD. Beyond sequence: Structure-based machine learning. Comput Struct Biotechnol J 2022; 21:630-643. [PMID: 36659927 PMCID: PMC9826903 DOI: 10.1016/j.csbj.2022.12.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Aalt D.J. van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
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Timonina DS, Suplatov DA. Analysis of Multiple Protein Alignments Using 3D-Structural Information on the Orientation of Amino Acid Side-Chains. Mol Biol 2022. [DOI: 10.1134/s0026893322040136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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4
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Loop 422–437 in NanA from Streptococcus pneumoniae plays the role of an active site lid and is associated with allosteric regulation. Comput Biol Med 2022; 144:105290. [DOI: 10.1016/j.compbiomed.2022.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/20/2022] [Accepted: 02/01/2022] [Indexed: 11/03/2022]
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Shegay MV, Švedas VK, Voevodin VV, Suplatov DA, Popova NN. Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment. Bioinformatics 2022; 38:985-989. [PMID: 34849594 DOI: 10.1093/bioinformatics/btab798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/23/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. RESULTS We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. AVAILABILITY AND IMPLEMENTATION The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maksim V Shegay
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vytas K Švedas
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vladimir V Voevodin
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Research Computing Center, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Dmitry A Suplatov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Nina N Popova
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
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Zlotnikov ID, Kudryashova EV. Computer simulation of the Receptor-Ligand Interactions of Mannose Receptor CD206 in Comparison with the Lectin Concanavalin A Model. BIOCHEMISTRY. BIOKHIMIIA 2022; 87:54-69. [PMID: 35491020 PMCID: PMC8769089 DOI: 10.1134/s0006297922010059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Computer modeling of complexation of mono- and oligosaccharide ligands with the main (fourth) carbohydrate-binding domain of the mannose receptor CD206 (CRD4), as well as with the model receptor concanavalin A (ConA), was carried out for the first time, using methods of molecular dynamics and neural network analysis. ConA was shown to be a relevant model of CD206 (CRD4) due to similarity of the structural organization of the binding sites and high correlation of the values of free energies of complexation between the literature data and computer modeling (r > 0.9). Role of the main factors affecting affinity of the ligand–receptor interactions is discussed: the number and nature of carbohydrate residues, presence of Me-group in the O1 position, type of the glycoside bond in dimannose. Complexation of ConA and CD206 with ligands is shown to be energetically caused by electrostatic interactions (E) of the charged residues (Asn, Asp, Arg) with oxygen and hydrogen atoms in carbohydrates; contributions of hydrophobic and van der Waals components is lower. Possibility of the additional stabilization of complexes due to the CH–π stacking interactions of Tyr with the Man plane is discussed. The role of calcium and manganese ions in binding ligands has been studied. The values of free energies of complexation calculated in the course of molecular dynamics simulation correlate with experimental data (published for the model ConA): correlation coefficient r = 0.68. The Pafnucy neural network was trained based on the set of PDBbind2020 ligand–receptor complexes, which significantly increased accuracy of the energy predictions to r = 0.8 and 0.82 for CD206 and ConA receptors, respectively. A model of normalization of the complexation energy values for calculating the relevant values of ΔGbind, Kd is proposed. Based on the developed technique, values of the dissociation constants of a series of CD206 complexes with nine carbohydrate ligands of different structures were determined, which were not previously known. The obtained data open up possibilities for using computer modeling for the development of optimal drug carriers capable of active macrophage targeting, and also determine the limits of applicability of using ConA as a relevant model for studying parameters of the CD206 binding to various carbohydrate ligands.
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Affiliation(s)
- Igor D Zlotnikov
- Faculty of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Elena V Kudryashova
- Faculty of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.
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Franco FP, Xu P, Harris BJ, Yarov-Yarovoy V, Leal WS. Single amino acid residue mediates reciprocal specificity in two mosquito odorant receptors. eLife 2022; 11:82922. [PMID: 36511779 PMCID: PMC9799979 DOI: 10.7554/elife.82922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The southern house mosquito, Culex quinquefasciatus, utilizes two odorant receptors, CquiOR10 and CquiOR2, narrowly tuned to oviposition attractants and well conserved among mosquito species. They detect skatole and indole, respectively, with reciprocal specificity. We swapped the transmembrane (TM) domains of CquiOR10 and CquiOR2 and identified TM2 as a specificity determinant. With additional mutations, we showed that CquiOR10A73L behaved like CquiOR2. Conversely, CquiOR2L74A recapitulated CquiOR10 specificity. Next, we generated structural models of CquiOR10 and CquiOR10A73L using RoseTTAFold and AlphaFold and docked skatole and indole using RosettaLigand. These modeling studies suggested space-filling constraints around A73. Consistent with this hypothesis, CquiOR10 mutants with a bulkier residue (Ile, Val) were insensitive to skatole and indole, whereas CquiOR10A73G retained the specificity to skatole and showed a more robust response than the wildtype receptor CquiOR10. On the other hand, Leu to Gly mutation of the indole receptor CquiOR2 reverted the specificity to skatole. Lastly, CquiOR10A73L, CquiOR2, and CquiOR2L74I were insensitive to 3-ethylindole, whereas CquiOR2L74A and CquiOR2L74G gained activity. Additionally, CquiOR10A73G gave more robust responses to 3-ethylindole than CquiOR10. Thus, we suggest the specificity of these receptors is mediated by a single amino acid substitution, leading to finely tuned volumetric space to accommodate specific oviposition attractants.
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Affiliation(s)
- Flavia P Franco
- Department of Molecular and Cellular Biology, University of California, DavisDavisUnited States
| | - Pingxi Xu
- Department of Molecular and Cellular Biology, University of California, DavisDavisUnited States
| | - Brandon J Harris
- Department of Physiology and Membrane Biology, University of California, DavisDavisUnited States
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, DavisDavisUnited States,Department of Anesthesiology and Pain Medicine, University of California, DavisDavisUnited States
| | - Walter S Leal
- Department of Molecular and Cellular Biology, University of California, DavisDavisUnited States
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Modulating Glycoside Hydrolase Activity between Hydrolysis and Transfer Reactions Using an Evolutionary Approach. Molecules 2021; 26:molecules26216586. [PMID: 34770995 PMCID: PMC8587830 DOI: 10.3390/molecules26216586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 01/02/2023] Open
Abstract
The proteins within the CAZy glycoside hydrolase family GH13 catalyze the hydrolysis of polysaccharides such as glycogen and starch. Many of these enzymes also perform transglycosylation in various degrees, ranging from secondary to predominant reactions. Identifying structural determinants associated with GH13 family reaction specificity is key to modifying and designing enzymes with increased specificity towards individual reactions for further applications in industrial, chemical, or biomedical fields. This work proposes a computational approach for decoding the determinant structural composition defining the reaction specificity. This method is based on the conservation of coevolving residues in spatial contacts associated with reaction specificity. To evaluate the algorithm, mutants of α-amylase (TmAmyA) and glucanotransferase (TmGTase) from Thermotoga maritima were constructed to modify the reaction specificity. The K98P/D99A/H222Q variant from TmAmyA doubled the transglycosydation/hydrolysis (T/H) ratio while the M279N variant from TmGTase increased the hydrolysis/transglycosidation ratio five-fold. Molecular dynamic simulations of the variants indicated changes in flexibility that can account for the modified T/H ratio. An essential contribution of the presented computational approach is its capacity to identify residues outside of the active center that affect the reaction specificity.
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9
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Rauer C, Sen N, Waman VP, Abbasian M, Orengo CA. Computational approaches to predict protein functional families and functional sites. Curr Opin Struct Biol 2021; 70:108-122. [PMID: 34225010 DOI: 10.1016/j.sbi.2021.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/13/2021] [Accepted: 05/25/2021] [Indexed: 01/06/2023]
Abstract
Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.
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Affiliation(s)
- Clemens Rauer
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Mahnaz Abbasian
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
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