scholarly journals Plastics degradation by hydrolytic enzymes: the Plastics-Active Enzymes Database - PAZy

2021 ◽  
Author(s):  
Patrick Buchholz ◽  
Hongli Zhang ◽  
Pablo Perez-Garcia ◽  
Lena-Luisa Nover ◽  
Jennifer Chow ◽  
...  
Keyword(s):  
Markov Models ◽  
Hydrolytic Enzymes ◽  
Ecological Niches ◽  
Structural Description ◽  
Sequence Motifs ◽  
Sequence Alignments ◽  
Activity Data ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Open Access Database

Petroleum based plastics are durable and accumulate in all ecological niches. Knowledge on enzymatic degradation is sparse. Today, less than 50 verified plastics-active enzymes are known. First examples of enzymes acting on the polymers polyethylene terephthalate (PET) and polyurethane (PUR) have been reported together with a detailed biochemical and structural description. Further, very few polyamide (PA) oligomer active enzymes are known. In this paper, the current known enzymes acting on the synthetic polymers PET and PUR are briefly summarized, their published activity data were collected and integrated into a comprehensive open access database. The Plastics-Active Enzymes Database (PAZy) represents an inventory of known and experimentally verified plastics-active enzymes. Almost 3000 homologues of PET-active enzymes were identified by profile hidden Markov models. Over 2000 homologues of PUR-active enzymes were identified by BLAST. Based on multiple sequence alignments, conservation analysis identified the most conserved amino acids, and sequence motifs for PET- and PUR-active enzymes were derived.

scholarly journals Evolutionary Relationships and Sequence-Structure Determinants in Human SARS Coronavirus-2 Spike Proteins for Host Receptor Recognition

2020 ◽  
Author(s):  
Lalitha Guruprasad
Keyword(s):  
Phylogenetic Trees ◽  
Disulfide Bridge ◽  
Sequence Motifs ◽  
Structural Determinants ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Angiotensin Converting Enzyme 2 ◽  
Multiple Sequence Alignments ◽  
Loop 2 ◽  
Spike Proteins

<div>Coronavirus disease 2019 (COVID-19) is a pandemic infectious disease caused by novel Severe Acute Respiratory Syndrome coronavirus-2 (SARS CoV-2). The SARS CoV-2 is transmitted more rapidly and readily than SARS CoV. Both, SARS CoV and SARS CoV-2 via their glycosylated spike proteins recognize the human angiotensin converting enzyme-2 (ACE-2) receptor. We generated multiple sequence alignments and phylogenetic trees for representative spike proteins of CoV and CoV-2 from various host sources in order to analyze the specificity in SARS CoV-2 spike proteins required for causing infection in humans. Our results show that two sequence motifs in the N-terminal domain; "MESEFR" and "SYLTPG" are specific to human SARS CoV-2 and pangolin SARS CoV. In the receptor binding domain (RBD), three sequence loops; VGGNY (loop 1), YQAGSTPC (loop 2), EGFNCY (loop 3) and a tethered disulfide bridge Cys480-Cys488 connecting loops 2 and 3 are structural determinants for the recognition of human ACE-2 receptor. The complete genome analysis of representative SARS CoVs from bat, civet, pangolin, human host sources and human SARS CoV-2 identified the bat genome (GenBank code: MN996532.1) and the pangolin SARS CoV genomes as closest to the recent novel human SARS CoV-2 genomes. The bat CoV genomes (GenBank codes: MG772933 and MG772934) are evolutionary intermediates in the mutagenesis progression towards becoming human SARS CoV-2. </div>

INFLUENZA A SUBTYPING AND HOST ORIGIN CLASSIFICATION USING PROFILE HIDDEN MARKOV MODELS

2012 ◽  
Vol 12 (02) ◽  
pp. 1240009 ◽  
Author(s):  
FAYROZ F. SHERIF ◽  
YASSER M. KADAH ◽  
MAHMOUD EL-HEFNAWI
Keyword(s):  
Hidden Markov Models ◽  
Influenza A ◽  
Markov Models ◽  
Hidden Markov ◽  
High Morbidity ◽  
Human Influenza ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Profile Hidden Markov Models

Influenza is one of the most important emerging and reemerging infectious diseases, causing high morbidity and mortality in communities (epidemic) and worldwide (pandemic). Here, classification of human vs. non-human influenza, and subtyping of human influenza viral strains virus is done based on profile hidden Markov models (HMM). The classical ways of determining influenza viral subtypes depend mainly on antigenic assays, which is time-consuming and not fully accurate. The introduced technique is much cheaper and faster, yet usually can still yield high accuracy. Multiple sequence alignments were done for the 16 HA subtypes and 9 NA subtypes, followed by profile-HMMs models generation, calibration and evaluation using the HMMER suite for each group. Subtyping accuracy of all HA and NA models achieved 100%, while host classification achieved accuracies around 53% and 95.1% according to HA subtype.

scholarly journals Evolutionary Relationships and Sequence-Structure Determinants in Human SARS Coronavirus-2 Spike Proteins for Host Receptor Recognition

2020 ◽  
Author(s):  
Lalitha Guruprasad
Keyword(s):  
Phylogenetic Trees ◽  
Disulfide Bridge ◽  
Sequence Motifs ◽  
Structural Determinants ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Angiotensin Converting Enzyme 2 ◽  
Multiple Sequence Alignments ◽  
Loop 2 ◽  
Spike Proteins

Coronavirus disease 2019 (COVID-19) is a pandemic infectious disease caused by novel Severe Acute Respiratory Syndrome coronavirus-2 (SARS CoV-2). The SARS CoV-2 is transmitted more rapidly and readily than SARS CoV. Both, SARS CoV and SARS CoV-2 via their glycosylated spike proteins recognize the human angiotensin converting enzyme-2 (ACE-2) receptor. We generated multiple sequence alignments and phylogenetic trees for representative spike proteins of CoV and CoV-2 from various host sources in order to analyze the specificity in SARS CoV-2 spike proteins required for causing infection in humans. Our results show that two sequence motifs in the N-terminal domain; "MESEFR" and "SYLTPG" are specific to human SARS CoV-2 and pangolin SARS CoV. In the receptor binding domain (RBD), three sequence loops; VGGNY (loop 1), YQAGSTPC (loop 2), EGFNCY (loop 3) and a tethered disulfide bridge Cys480-Cys488 connecting loops 2 and 3 are structural determinants for the recognition of human ACE-2 receptor. The complete genome analysis of representative SARS CoVs from bat, civet, pangolin, human host sources and human SARS CoV-2 identified the bat genome (GenBank code: MN996532.1) and the pangolin SARS CoV genomes as closest to the recent novel human SARS CoV-2 genomes. The bat CoV genomes (GenBank codes: MG772933 and MG772934) are evolutionary intermediates in the mutagenesis progression towards becoming human SARS CoV-2.

scholarly journals Evolutionary Relationships and Sequence-Structure Determinants in Human SARS Coronavirus-2 Spike Proteins for Host Receptor Recognition

2020 ◽  
Author(s):  
Lalitha Guruprasad
Keyword(s):  
Phylogenetic Trees ◽  
Disulfide Bridge ◽  
Sequence Motifs ◽  
Structural Determinants ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Angiotensin Converting Enzyme 2 ◽  
Multiple Sequence Alignments ◽  
Complete Genome Analysis ◽  
Spike Proteins

<div>Coronavirus disease 2019 (COVID-19) is a pandemic infectious disease caused by novel Severe Acute Respiratory Syndrome coronavirus-2 (SARS CoV-2). The SARS CoV-2 is transmitted more rapidly and readily than SARS CoV. Both, SARS CoV and SARS CoV-2 via their glycosylated spike proteins recognize the human angiotensin converting enzyme-2 (ACE-2) receptor. We generated multiple sequence alignments and phylogenetic trees for representative spike proteins of CoV and CoV-2 from various host sources in order to analyze the specificity in SARS CoV-2 spike proteins required for causing infection in humans. Our results show that two sequence motifs in the N-terminal domain; "MESEFR" and "SYLTPG" are specific to human SARS CoV-2. In the receptor binding domain (RBD), two sequence motifs; "VGGNY" and "EIYQAGSTPCNGV" and a disulfide bridge connecting 480C and 488C in the extended loop are structural determinants for the recognition of human ACE-2 receptor. The complete genome analysis of representative SARS CoVs from bat, civet, human host sources and human SARS CoV-2 identified the bat genome (GenBank code: MN996532.1) as closest to the recent novel human SARS CoV-2 genomes. The bat CoV genomes (GenBank codes: MG772933 and MG772934) are evolutionary intermediates in the mutagenesis progression towards becoming human SARS CoV-2. <br></div>

scholarly journals Positive natural selection in primate genes of the type I interferon response

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elena N. Judd ◽  
Alison R. Gilchrist ◽  
Nicholas R. Meyerson ◽  
Sara L. Sawyer
Keyword(s):  
Natural Selection ◽  
Positive Selection ◽  
Type I Interferon ◽  
Interferon Response ◽  
Type I ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Interferon Stimulated Genes ◽  
Interferon Induction

Abstract Background The Type I interferon response is an important first-line defense against viruses. In turn, viruses antagonize (i.e., degrade, mis-localize, etc.) many proteins in interferon pathways. Thus, hosts and viruses are locked in an evolutionary arms race for dominance of the Type I interferon pathway. As a result, many genes in interferon pathways have experienced positive natural selection in favor of new allelic forms that can better recognize viruses or escape viral antagonists. Here, we performed a holistic analysis of selective pressures acting on genes in the Type I interferon family. We initially hypothesized that the genes responsible for inducing the production of interferon would be antagonized more heavily by viruses than genes that are turned on as a result of interferon. Our logic was that viruses would have greater effect if they worked upstream of the production of interferon molecules because, once interferon is produced, hundreds of interferon-stimulated proteins would activate and the virus would need to counteract them one-by-one. Results We curated multiple sequence alignments of primate orthologs for 131 genes active in interferon production and signaling (herein, “induction” genes), 100 interferon-stimulated genes, and 100 randomly chosen genes. We analyzed each multiple sequence alignment for the signatures of recurrent positive selection. Counter to our hypothesis, we found the interferon-stimulated genes, and not interferon induction genes, are evolving significantly more rapidly than a random set of genes. Interferon induction genes evolve in a way that is indistinguishable from a matched set of random genes (22% and 18% of genes bear signatures of positive selection, respectively). In contrast, interferon-stimulated genes evolve differently, with 33% of genes evolving under positive selection and containing a significantly higher fraction of codons that have experienced selection for recurrent replacement of the encoded amino acid. Conclusion Viruses may antagonize individual products of the interferon response more often than trying to neutralize the system altogether.

scholarly journals DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Farhan Quadir ◽  
Raj S. Roy ◽  
Randal Halfmann ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Tertiary Structure ◽  
Protein Complexes ◽  
Complex Structure ◽  
Great Success ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Residue Contacts ◽  
Evolutionary Features

AbstractDeep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

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