Molecular Cloning and Structural Insights into pectin lyase proteins from different strains of Fusarium

2020 ◽  
Vol 17 ◽  
Author(s):  
Sangeeta Yadav ◽  
Gautam Anand ◽  
Vinay K Singh ◽  
Dinesh Yadav
Keyword(s):  
Structure Prediction ◽  
Disulfide Bonds ◽  
3D Structure ◽  
Chemical Characterization ◽  
Structural Features ◽  
Docking Studies ◽  
Biochemical Properties ◽  
Pectin Lyase ◽  
Homology Search ◽  
Multiple Sequence

: Pectin lyaseis an industrially important enzymeof pectinase group that degrade pectin polymers forming 4,5-unsaturated oligogalacturonides. Several fugal pectin lyase genes predominately from Aspergillus and Penicillium genera have been reported in the literature. Five pectin lyase genes were cloned from FusariumoxysporumMTCC1755, F.monoliforme var. subglutinansMTCC2015, FusariumavneceumMTCC10572, and FusariumsolaniMTCC3004 using PCR approach. Pectin lyase genes and proteins were subjected to homology search, multiple sequence alignment, motif search, physio-chemical characterization, phylogenetic tree construction, 3D structure prediction and molecular docking. Many conserved amino acids were found at several positions in all the pectin lyase proteins. Phylogenetic analysis of these proteins alongwith other pectinases revealed two major clusters representing members of lyases and hydrolases. In-silico characterization revealed pectin lyase proteins to be highly stable owing to the presence of disulfide bonds in their structure. Molecular weight and pI of these proteins were in the range 14.4 to 25.1 kDa and 4.47-9.39 respectively. Pectin lyase proteins from different Fusariumstrains were very much similar in their structural features and biochemical properties which might be due to their similarity on the primary sequence. Docking studies revealed that electrostatic forces, vander Waal and hydrogen bonds are the major interacting forces between the ligands and the enzyme. This might be accountable for comparatively higher and better activity of pectin lyase against galacturonic acid as compared to α-D-galactopyranuronic acid, galactofuranuronicacid and galactopyranuronate. Aspartate, tyrosine and tryptophan residues in the active site of the enzyme are responsible for ligand binding.

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 Remote homology search with hidden Potts models

2020 ◽  
Author(s):  
Grey W. Wilburn ◽  
Sean R. Eddy
Keyword(s):  
Structure Prediction ◽  
Statistical Physics ◽  
Probability Model ◽  
3D Structure ◽  
Homology Search ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Potts Models ◽  
Multiple Sequence Alignments ◽  
Insertion And Deletion

AbstractMost 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.Author summaryComputational homology search and alignment tools are used to infer the functions and evolutionary histories of biological sequences. Most widely used tools for sequence homology searches, such as BLAST and HMMER, rely on primary sequence conservation alone. It should be possible to make more powerful search tools by also considering higher-order covariation patterns induced by 3D structure conservation. Recent advances in 3D protein structure prediction have used a class of statistical physics models called Potts models to infer pairwise correlation structure in multiple sequence alignments. However, Potts models assume alignments are given and cannot build new alignments, limiting their use in homology search. We have extended Potts models to include a probability model of insertion and deletion so they can be applied to sequence alignment and remote homology search using a new model we call a hidden Potts model (HPM). Tests of our prototype HPM software show promising results in initial benchmarking experiments, though more work will be needed to use HPMs in practical tools.

scholarly journals Crystal structure of human interferon-γ receptor 2 reveals the structural basis for receptor specificity

2016 ◽  
Vol 72 (9) ◽  
pp. 1017-1025 ◽  
Author(s):  
Pavel Mikulecký ◽  
Jirí Zahradník ◽  
Petr Kolenko ◽  
Jiří Černý ◽  
Tatsiana Charnavets ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Disulfide Bonds ◽  
Bioinformatic Analysis ◽  
Structural Features ◽  
Interferon Γ ◽  
Cell Surface Receptor ◽  
Structural Basis ◽  
Human Interferon ◽  
Multiple Sequence ◽  
Surface Receptor

Interferon-γ receptor 2 is a cell-surface receptor that is required for interferon-γ signalling and therefore plays a critical immunoregulatory role in innate and adaptive immunity against viral and also bacterial and protozoal infections. A crystal structure of the extracellular part of human interferon-γ receptor 2 (IFNγR2) was solved by molecular replacement at 1.8 Å resolution. Similar to other class 2 receptors, IFNγR2 has two fibronectin type III domains. The characteristic structural features of IFNγR2 are concentrated in its N-terminal domain: an extensive π–cation motif of stacked residues KWRWRH, a NAG–W–NAG sandwich (where NAG stands forN-acetyl-D-glucosamine) and finally a helix formed by residues 78–85, which is unique among class 2 receptors. Mass spectrometry and mutational analyses showed the importance of N-linked glycosylation to the stability of the protein and confirmed the presence of two disulfide bonds. Structure-based bioinformatic analysis revealed independent evolutionary behaviour of both receptor domains and, together with multiple sequence alignment, identified putative binding sites for interferon-γ and receptor 1, the ligands of IFNγR2.

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 RNA 3D structure prediction using multiple sequence alignment information

2019 ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz Bujnicki
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Prediction Method ◽  
Target Sequence ◽  
Rna Sequences ◽  
Multiple Sequence ◽  
Original Target ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction method. EvoClustRNA is a multi- step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Conclusion Through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence.

scholarly journals Contrastive learning on protein embeddings enlightens midnight zone at lightning speed

2021 ◽  
Author(s):  
Michael Heinzinger ◽  
Maria Littmann ◽  
Ian Sillitoe ◽  
Nicola Bordin ◽  
Christine Orengo ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Sequence Similarity ◽  
3D Structure ◽  
Three Dimensional ◽  
Hierarchical Classification ◽  
Language Models ◽  
Sequence Alignments ◽  
Sequence Comparisons ◽  
Multiple Sequence ◽  
3D Structures

Thanks to the recent advances in protein three-dimensional (3D) structure prediction, in particular through AlphaFold 2 and RoseTTAFold, the abundance of protein 3D information will explode over the next year(s). Expert resources based on 3D structures such as SCOP and CATH have been organizing the complex sequence-structure-function relations into a hierarchical classification schema. Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI) transferring annotations from a protein with experimentally known annotation to a query without annotation. Here, we presented a novel approach that expands the concept of HBI from a low-dimensional sequence-distance lookup to the level of a high-dimensional embedding-based annotation transfer (EAT). Secondly, we introduced a novel solution using single protein sequence representations from protein Language Models (pLMs), so called embeddings (Prose, ESM-1b, ProtBERT, and ProtT5), as input to contrastive learning, by which a new set of embeddings was created that optimized constraints captured by hierarchical classifications of protein 3D structures. These new embeddings (dubbed ProtTucker) clearly improved what was historically referred to as threading or fold recognition. Thereby, the new embeddings enabled the intrusion into the midnight zone of protein comparisons, i.e., the region in which the level of pairwise sequence similarity is akin of random relations and therefore is hard to navigate by HBI methods. Cautious benchmarking showed that ProtTucker reached much further than advanced sequence comparisons without the need to compute alignments allowing it to be orders of magnitude faster. Code is available at https://github.com/Rostlab/EAT .

scholarly journals Protein language model embeddings for fast, accurate, alignment-free protein structure prediction

2021 ◽  
Author(s):  
Konstantin Weissenow ◽  
Michael Heinzinger ◽  
Burkhard Rost
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Prediction Models ◽  
Language Model ◽  
Structural Features ◽  
Language Models ◽  
Evolutionary Information ◽  
Major Advance ◽  
Sequence Alignments ◽  
Multiple Sequence

All state-of-the-art (SOTA) protein structure predictions rely on evolutionary information captured in multiple sequence alignments (MSAs), primarily on evolutionary couplings (co-evolution). Such information is not available for all proteins and is computationally expensive to generate. Prediction models based on Artificial Intelligence (AI) using only single sequences as input are easier and cheaper but perform so poorly that speed becomes irrelevant. Here, we described the first competitive AI solution exclusively inputting embeddings extracted from pre-trained protein Language Models (pLMs), namely from the transformer pLM ProtT5, from single sequences into a relatively shallow (few free parameters) convolutional neural network (CNN) trained on inter-residue distances, i.e. protein structure in 2D. The major advance originated from processing the attention heads learned by ProtT5. Although these models required at no point any MSA, they matched the performance of methods relying on co-evolution. Although not reaching the very top, our lean approach came close at substantially lower costs thereby speeding up development and each future prediction. By generating protein-specific rather than family-averaged predictions, these new solutions could distinguish between structural features differentiating members of the same family of proteins with similar structure predicted alike by all other top methods.

scholarly journals 2dSS: a web server for protein secondary structure visualization

2019 ◽  
Author(s):  
Diksha Priya Lotun ◽  
Charlotte Cochard ◽  
Fabio R.J Vieira ◽  
Juliana Silva Bernardes
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Web Server ◽  
Homologous Proteins ◽  
Multiple Sequence ◽  
Prediction Tools ◽  
Secondary Structure Predictions

2dSS is a web-server for visualising and comparing secondary structure predictions. It provides two main functionalities: 2D-alignment and compare predictions. The “2D-alignment” has been designed to visualise conserved secondary structure elements in a multiple sequence alignment (MSA). From this we can study the secondary structure content of homologous proteins (a protein family) and highlight its structural patterns. The “compare predictions” has been designed to compare the output of several secondary structure prediction tools, and check their accuracy when compared with real secondary structure elements extracted from 3D-structure. 2dSS provides a comprehensive representation of protein secondary structure elements, and it can be used to visualise and compare secondary structures of any prediction tool.Availabilityhttp://genome.lcqb.upmc.fr/2dss/

scholarly journals Structure Prediction of a Thermostable SR74 α-Amylase from Geobacillus stearothermophilus Expressed in CTG-Clade Yeast Meyerozyma guilliermondii Strain SO

Catalysts ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1059
Author(s):  
Si Jie Lim ◽  
Noor Dina Muhd Noor ◽  
Abu Bakar Salleh ◽  
Siti Nurbaya Oslan
Keyword(s):  
Structure Prediction ◽  
In Silico Analysis ◽  
Biochemical Properties ◽  
Amino Acid Residues ◽  
Geobacillus Stearothermophilus ◽  
Multiple Sequence ◽  
Meyerozyma Guilliermondii ◽  
Glycosidic Bonds ◽  
And Function ◽  
Hydrolysis Of

α-amylase which catalyzes the hydrolysis of α-1,4-glycosidic bonds in starch have frequently been cloned into various microbial workhorses to yield a higher recombinant titer. A thermostable SR74 α-amylase from Geobacillus stearothermophilus was found to have a huge potential in detergent industries due to its thermostability properties. The gene was cloned into a CTG-clade yeast Meyerozyma guilliermondii strain SO. However, the CUG ambiguity present in the strain SO has possibly altered the amino acid residues in SR74 amylase wild type (WT) encoded by CUG the codon from the leucine to serine. From the multiple sequence alignment, six mutations were found in recombinant SR74 α-amylase (rc). Their effects on SR74 α-amylase structure and function remain unknown. Herein, we predicted the structures of the SR74 amylases (WT and rc) using the template 6ag0.1.A (PDB ID: 6ag0). We sought to decipher the possible effects of CUG ambiguity in strain SO via in silico analysis. They are structurally identical, and the metal triad (CaI–CaIII) might contribute to the thermostability while CaIV was attributed to substrate specificity. Since the pairwise root mean square deviation (RMSD) between the WT and rc SR74 α-amylase was lower than the template, we suggest that the biochemical properties of rc SR74 α-amylase were better deduced from its WT, especially its thermostability.

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