scholarly journals Analysis of Sequence Divergence in Mammalian ABCGs Predicts a Structural Network of Residues That Underlies Functional Divergence

2021 ◽  
Vol 22 (6) ◽  
pp. 3012
Author(s):  
James I. Mitchell-White ◽  
Thomas Stockner ◽  
Nicholas Holliday ◽  
Stephen J. Briddon ◽  
Ian D. Kerr
Keyword(s):  
Substrate Specificity ◽  
Functional Divergence ◽  
3D Structure ◽  
Sequence Divergence ◽  
Conformational Flexibility ◽  
Substrate Selectivity ◽  
Multiple Sequence ◽  
Atp Binding Cassette Transporters ◽  
Structural Network ◽  
Wide Substrate Specificity

The five members of the mammalian G subfamily of ATP-binding cassette transporters differ greatly in their substrate specificity. Four members of the subfamily are important in lipid transport and the wide substrate specificity of one of the members, ABCG2, is of significance due to its role in multidrug resistance. To explore the origin of substrate selectivity in members 1, 2, 4, 5 and 8 of this subfamily, we have analysed the differences in conservation between members in a multiple sequence alignment of ABCG sequences from mammals. Mapping sets of residues with similar patterns of conservation onto the resolved 3D structure of ABCG2 reveals possible explanations for differences in function, via a connected network of residues from the cytoplasmic to transmembrane domains. In ABCG2, this network of residues may confer extra conformational flexibility, enabling it to transport a wider array of substrates.

scholarly journals S-Acylation of Proteins of Coronavirus and Influenza Virus: Conservation of Acylation Sites in Animal Viruses and DHHC Acyltransferases in Their Animal Reservoirs

Pathogens ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 669
Author(s):  
Dina A. Abdulrahman ◽  
Xiaorong Meng ◽  
Michael Veit
Keyword(s):  
Influenza Virus ◽  
Amino Acid ◽  
Ion Channels ◽  
Substrate Specificity ◽  
Influenza A ◽  
3D Structure ◽  
Viral Fitness ◽  
Amino Acid Substitutions ◽  
Essential Function ◽  
Animal Reservoirs

Recent pandemics of zoonotic origin were caused by members of coronavirus (CoV) and influenza A (Flu A) viruses. Their glycoproteins (S in CoV, HA in Flu A) and ion channels (E in CoV, M2 in Flu A) are S-acylated. We show that viruses of all genera and from all hosts contain clusters of acylated cysteines in HA, S and E, consistent with the essential function of the modification. In contrast, some Flu viruses lost the acylated cysteine in M2 during evolution, suggesting that it does not affect viral fitness. Members of the DHHC family catalyze palmitoylation. Twenty-three DHHCs exist in humans, but the number varies between vertebrates. SARS-CoV-2 and Flu A proteins are acylated by an overlapping set of DHHCs in human cells. We show that these DHHC genes also exist in other virus hosts. Localization of amino acid substitutions in the 3D structure of DHHCs provided no evidence that their activity or substrate specificity is disturbed. We speculate that newly emerged CoVs or Flu viruses also depend on S-acylation for replication and will use the human DHHCs for that purpose. This feature makes these DHHCs attractive targets for pan-antiviral drugs.

Structural insights into the substrate specificity of a glycoside hydrolase family 5 lichenase from Caldicellulosiruptor sp. F32

2017 ◽  
Vol 474 (20) ◽  
pp. 3373-3389 ◽  
Author(s):  
Dong-Dong Meng ◽  
Xi Liu ◽  
Sheng Dong ◽  
Ye-Fei Wang ◽  
Xiao-Qing Ma ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Substrate Specificity ◽  
Glycoside Hydrolase ◽  
Complex Structure ◽  
Dynamics Simulation ◽  
Structural Basis ◽  
Glycoside Hydrolase Family ◽  
Substrate Selectivity ◽  
Glucose Residue ◽  
Structural Insights

Glycoside hydrolase (GH) family 5 is one of the largest GH families with various GH activities including lichenase, but the structural basis of the GH5 lichenase activity is still unknown. A novel thermostable lichenase F32EG5 belonging to GH5 was identified from an extremely thermophilic bacterium Caldicellulosiruptor sp. F32. F32EG5 is a bi-functional cellulose and a lichenan-degrading enzyme, and exhibited a high activity on β-1,3-1,4-glucan but side activity on cellulose. Thin-layer chromatography and NMR analyses indicated that F32EG5 cleaved the β-1,4 linkage or the β-1,3 linkage while a 4-O-substitued glucose residue linked to a glucose residue through a β-1,3 linkage, which is completely different from extensively studied GH16 lichenase that catalyses strict endo-hydrolysis of the β-1,4-glycosidic linkage adjacent to a 3-O-substitued glucose residue in the mixed-linked β-glucans. The crystal structure of F32EG5 was determined to 2.8 Å resolution, and the crystal structure of the complex of F32EG5 E193Q mutant and cellotetraose was determined to 1.7 Å resolution, which revealed that the exit subsites of substrate-binding sites contribute to both thermostability and substrate specificity of F32EG5. The sugar chain showed a sharp bend in the complex structure, suggesting that a substrate cleft fitting to the bent sugar chains in lichenan is a common feature of GH5 lichenases. The mechanism of thermostability and substrate selectivity of F32EG5 was further demonstrated by molecular dynamics simulation and site-directed mutagenesis. These results provide biochemical and structural insights into thermostability and substrate selectivity of GH5 lichenases, which have potential in industrial processes.

Reshaping the Active Pocket of Esterase Est816 for Resolution of Economically Important Racemates

2021 ◽  
Author(s):  
Xiaolong Liu ◽  
Meng Zhao ◽  
Xinjiong Fan ◽  
Yao Fu
Keyword(s):  
Substrate Specificity ◽  
Industrial Applications ◽  
Pure Compounds ◽  
Optically Pure ◽  
Wide Substrate Specificity

One of the most important industrial applications of bacterial esterases is the production of optically pure compounds. However, the contradiction between the wide substrate specificity and high enantioselectivity of natural...

scholarly journals Organization of the multigene families of African Swine Fever Virus

Fine Focus ◽  
2017 ◽  
Vol 3 (2) ◽  
pp. 155-170 ◽  
Author(s):  
Jacob Imbery ◽  
Chris Upton
Keyword(s):  
African Swine Fever Virus ◽  
Sequence Divergence ◽  
African Swine Fever ◽  
Fever Virus ◽  
Multigene Families ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Viral Genomes ◽  
Gene Maps

African swine fever virus is a complex DNA virus that infects swine and is spread by ticks. Mortality rates in domestic pigs are very high and the virus is a significant threat to pork farming. The genomes of 16 viruses have been sequenced completely, but these represent only a few of the 23 genotypes. The viral genome is unusual in that it contains 5 multigene families, each of which contain 3-19 duplicated copies (paralogs). There is significant sequence divergence between the paralogs in a single virus and between the orthologs in the different viral genomes. This, together with the fact that in most of the multigene families there are numerous gene indels that create truncations and fusions, makes annotation of these regions very difficult; it has led to inconsistent annotation of the 16 viral genomes. In this project, we have created multiple sequence alignments for each of the multigene families and have produced gene maps to help researchers more easily understand the organization of the multigene families among the different viruses. These gene maps will help researchers ascertain which members of the multigene families are present in each of the viruses. This is critical because some of the multigene families are known to be associated with virus virulence.

scholarly journals Structural analysis of rice Os4BGlu18 monolignol β-glucosidase

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0241325
Author(s):  
Supaporn Baiya ◽  
Salila Pengthaisong ◽  
Sunan Kitjaruwankul ◽  
James R. Ketudat Cairns
Keyword(s):  
Molecular Docking ◽  
Substrate Specificity ◽  
Disulfide Bonds ◽  
Complex Structure ◽  
Docking Studies ◽  
Structural Basis ◽  
Glycoside Hydrolase Family ◽  
Multiple Sequence ◽  
X Ray Crystallography ◽  
Tim Barrel

Monolignol glucosides are storage forms of monolignols, which are polymerized to lignin to strengthen plant cell walls. The conversion of monolignol glucosides to monolignols is catalyzed by monolignol β-glucosidases. Rice Os4BGlu18 β-glucosidase catalyzes hydrolysis of the monolignol glucosides, coniferin, syringin, and p-coumaryl alcohol glucoside more efficiently than other natural substrates. To understand more clearly the basis for substrate specificity of a monolignol β-glucosidase, the structure of Os4BGlu18 was determined by X-ray crystallography. Crystals of Os4BGlu18 and its complex with δ-gluconolactone diffracted to 1.7 and 2.1 Å resolution, respectively. Two protein molecules were found in the asymmetric unit of the P212121 space group of their isomorphous crystals. The Os4BGlu18 structure exhibited the typical (β/α)8 TIM barrel of glycoside hydrolase family 1 (GH1), but the four variable loops and two disulfide bonds appeared significantly different from other known structures of GH1 β-glucosidases. Molecular docking studies of the Os4BGlu18 structure with monolignol substrate ligands placed the glycone in a similar position to the δ-gluconolactone in the complex structure and revealed the interactions between protein and ligands. Molecular docking, multiple sequence alignment, and homology modeling identified amino acid residues at the aglycone-binding site involved in substrate specificity for monolignol β-glucosides. Thus, the structural basis of substrate recognition and hydrolysis by monolignol β-glucosidases was elucidated.

scholarly journals AttentiveDist: Protein Inter-Residue Distance Prediction Using Deep Learning with Attention on Quadruple Multiple Sequence Alignments

2020 ◽  
Author(s):  
Aashish Jain ◽  
Genki Terashi ◽  
Yuki Kagaya ◽  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Charles Christoffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Prediction Models ◽  
3D Structure ◽  
Evolutionary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Distance Prediction

ABSTRACTProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. The model is trained in a multi-task fashion to also predict backbone and orientation angles further improving the inter-residue distance prediction. We show that AttentiveDist outperforms the top methods for contact prediction in the CASP13 structure prediction competition. To aid in structure modeling we also developed two new deep learning-based sidechain center distance and peptide-bond nitrogen-oxygen distance prediction models. Together these led to a 12% increase in TM-score from the best server method in CASP13 for structure prediction.

scholarly journals Remote homology search with hidden Potts models

2020 ◽  
Vol 16 (11) ◽  
pp. e1008085
Author(s):  
Grey W. Wilburn ◽  
Sean R. Eddy
Keyword(s):  
Structure Prediction ◽  
Probability Model ◽  
3D Structure ◽  
Approximate Algorithm ◽  
Homology Search ◽  
Biological Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Potts Models ◽  
Alignment Algorithms

Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic context-free grammars). HPMs perform promisingly in these proof of principle experiments.

scholarly journals Binding site analysis of potential protease inhibitors of COVID-19 using AutoDock

2020 ◽  
Author(s):  
DIPTI MOTHAY ◽  
K.V. RAMESH
Keyword(s):  
Protease Inhibitor ◽  
Protease Inhibitors ◽  
Active Site ◽  
Molecular Interaction ◽  
3D Structure ◽  
Active Site Residues ◽  
Multiple Sequence ◽  
Site Analysis ◽  
Interaction Studies ◽  
Structural Insights

Abstract Recent outbreak of COVID-19 caused by SARS-CoV-2 in December 2019 raised global health concerns. Re-purposing the available protease inhibitor drugs for immediate use in treatment in SARS‐CoV‐2 infections could improve the currently available clinical management. The current study, aims to predict theoretical structure for protease of COVID-19 and to explore further whether this protein can serve as a target for protease inhibitor drugs such as remdesivir, nelfinavir, lopinavir, ritonavir and α –ketoamide. While the 3D structure of protease was predicted using SWISS MODEL server, molecular interaction studies between protein and ligands were performed using AutoDock software. The predicted protease model was reasonably good based on reports generated by different validation servers. The study further revealed that all the protease inhibitor drugs got docked with negative dock energy onto the target protein. Molecular interaction studies showed that protease structure had multiple active site residues for remdesivir, while for remaining ligands the structure had only one active site residue each. From the output of multiple sequence alignment, it is evident that ligand binding sites were conserved. The current in-silico study thus, provides structural insights about the protease of COVID-19 and also its molecular interactions with some of the known protease inhibitors.

scholarly journals Kinetic, Structural, and Mutational Analysis of Acyl-CoA Carboxylase From Thermobifida fusca YX

2021 ◽  
Vol 7 ◽  
Author(s):  
Kiran-Kumar Shivaiah ◽  
Bryon Upton ◽  
Basil J. Nikolau
Keyword(s):  
Substrate Specificity ◽  
Quaternary Structure ◽  
Mutational Analysis ◽  
Streptomyces Coelicolor ◽  
Thermobifida Fusca ◽  
Substrate Selectivity ◽  
Acetyl Coa ◽  
Catalytic Subunits ◽  
Chain Acyl

Acyl-CoA carboxylases (AcCCase) are biotin-dependent enzymes that are capable of carboxylating more than one short chain acyl-CoA substrate. We have conducted structural and kinetic analyses of such an AcCCase from Thermobifida fusca YX, which exhibits promiscuity in carboxylating acetyl-CoA, propionyl-CoA, and butyryl-CoA. The enzyme consists of two catalytic subunits (TfAcCCA and TfAcCCB) and a non-catalytic subunit, TfAcCCE, and is organized in quaternary structure with a A6B6E6 stoichiometry. Moreover, this holoenzyme structure appears to be primarily assembled from two A3 and a B6E6 subcomplexes. The role of the TfAcCCE subunit is to facilitate the assembly of the holoenzyme complex, and thereby activate catalysis. Based on prior studies of an AcCCase from Streptomyces coelicolor, we explored whether a conserved Asp residue in the TfAcCCB subunit may have a role in determining the substrate selectivity of these types of enzymes. Mutating this D427 residue resulted in alterations in the substrate specificity of the TfAcCCase, increasing proficiency for carboxylating acetyl-CoA, while decreasing carboxylation proficiency with propionyl-CoA and butyryl-CoA. Collectively these results suggest that residue D427 of AcCCB subunits is an important, but not sole determinant of the substrate specificity of AcCCase enzymes.

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