MetaLogo: a generator and aligner for multiple sequence logos

Sequence logos are used to visually display sequence conservations and variations. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators can only draw logos for sequences of the same length, let alone for groups of sequences with different characteristics besides lengths. To solve these problems, we developed MetaLogo, which can draw sequence logos for sequences of different lengths or from different groups in one single plot and align multiple logos to highlight the sequence pattern dynamics across groups, thus allowing users to investigate functional motifs in a more delicate and dynamic perspective. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized, aesthetic, and publishable sequence logos without any programming experience.

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Logomaker: beautiful sequence logos in Python

Bioinformatics ◽

10.1093/bioinformatics/btz921 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2272-2274 ◽

Cited By ~ 23

Author(s):

Ammar Tareen ◽

Justin B Kinney

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Source Code ◽

Protein Sequences ◽

Biological Properties ◽

Programming Environment ◽

Multiple Sequence ◽

Sequence Logos ◽

Python Programming ◽

Publication Quality

Abstract Summary Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures. Availability and implementation Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

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Computational Analysis of Therapeutic Enzyme Uricase from Different Source Organisms

Current Proteomics ◽

10.2174/1570164616666190617165107 ◽

2020 ◽

Vol 17 (1) ◽

pp. 59-77

Author(s):

Anand Kumar Nelapati ◽

JagadeeshBabu PonnanEttiyappan

Keyword(s):

Uric Acid ◽

Amino Acid ◽

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Protein Sequences ◽

Amino Acid Sequences ◽

Amino Acid Residues ◽

Multiple Sequence ◽

Physiochemical Properties ◽

Pharmaceutical Industries

Background:Hyperuricemia and gout are the conditions, which is a response of accumulation of uric acid in the blood and urine. Uric acid is the product of purine metabolic pathway in humans. Uricase is a therapeutic enzyme that can enzymatically reduces the concentration of uric acid in serum and urine into more a soluble allantoin. Uricases are widely available in several sources like bacteria, fungi, yeast, plants and animals.Objective:The present study is aimed at elucidating the structure and physiochemical properties of uricase by insilico analysis.Methods:A total number of sixty amino acid sequences of uricase belongs to different sources were obtained from NCBI and different analysis like Multiple Sequence Alignment (MSA), homology search, phylogenetic relation, motif search, domain architecture and physiochemical properties including pI, EC, Ai, Ii, and were performed.Results:Multiple sequence alignment of all the selected protein sequences has exhibited distinct difference between bacterial, fungal, plant and animal sources based on the position-specific existence of conserved amino acid residues. The maximum homology of all the selected protein sequences is between 51-388. In singular category, homology is between 16-337 for bacterial uricase, 14-339 for fungal uricase, 12-317 for plants uricase, and 37-361 for animals uricase. The phylogenetic tree constructed based on the amino acid sequences disclosed clusters indicating that uricase is from different source. The physiochemical features revealed that the uricase amino acid residues are in between 300- 338 with a molecular weight as 33-39kDa and theoretical pI ranging from 4.95-8.88. The amino acid composition results showed that valine amino acid has a high average frequency of 8.79 percentage compared to different amino acids in all analyzed species.Conclusion:In the area of bioinformatics field, this work might be informative and a stepping-stone to other researchers to get an idea about the physicochemical features, evolutionary history and structural motifs of uricase that can be widely used in biotechnological and pharmaceutical industries. Therefore, the proposed in silico analysis can be considered for protein engineering work, as well as for gout therapy.

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Fe(2)OG: an integrated HMM profile-based web server to predict and analyze putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenase function in protein sequences

BMC Research Notes ◽

10.1186/s13104-021-05477-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Siddhartha Kundu

Keyword(s):

Amino Acid ◽

Water Molecule ◽

Active Site ◽

Ferrous Iron ◽

Web Server ◽

Protein Sequences ◽

Diverse Group ◽

And Function ◽

Functionally Diverse ◽

Haem Iron

Abstract Objective Non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenases (i2OGdd), are a taxonomically and functionally diverse group of enzymes. The active site comprises ferrous iron in a hexa-coordinated distorted octahedron with the apoenzyme, 2-oxoglutarate and a displaceable water molecule. Current information on novel i2OGdd members is sparse and relies on computationally-derived annotation schema. The dissimilar amino acid composition and variable active site geometry thereof, results in differing reaction chemistries amongst i2OGdd members. An additional need of researchers is a curated list of sequences with putative i2OGdd function which can be probed further for empirical data. Results This work reports the implementation of $$Fe\left(2\right)OG$$ F e 2 O G , a web server with dual functionality and an extension of previous work on i2OGdd enzymes $$\left(Fe\left(2\right)OG\equiv \{H2OGpred,DB2OG\}\right)$$ F e 2 O G ≡ { H 2 O G p r e d , D B 2 O G } . $$Fe\left(2\right)OG$$ F e 2 O G , in this form is completely revised, updated (URL, scripts, repository) and will strengthen the knowledge base of investigators on i2OGdd biochemistry and function. $$Fe\left(2\right)OG$$ F e 2 O G , utilizes the superior predictive propensity of HMM-profiles of laboratory validated i2OGdd members to predict probable active site geometries in user-defined protein sequences. $$Fe\left(2\right)OG$$ F e 2 O G , also provides researchers with a pre-compiled list of analyzed and searchable i2OGdd-like sequences, many of which may be clinically relevant. $$Fe(2)OG$$ F e ( 2 ) O G , is freely available (http://204.152.217.16/Fe2OG.html) and supersedes all previous versions, i.e., H2OGpred, DB2OG.

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Benchmarking Statistical Multiple Sequence Alignment

10.1101/304659 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael Nute ◽

Ehsan Saleh ◽

Tandy Warnow

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Structural Alignment ◽

Estimation Method ◽

Simulated Data ◽

Protein Sequences ◽

Data Sets ◽

Sequence Alignments ◽

Multiple Sequence ◽

Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

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Analyses of Homing Endonucleases and Mechanism of Action of CRISPR-Cas9 HNH Endonucleases

International Journal of Biochemistry Research & Review ◽

10.9734/ijbcrr/2020/v29i630192 ◽

2020 ◽

pp. 1-25

Author(s):

Peramachi Palanivelu

Keyword(s):

Amino Acids ◽

Mechanism Of Action ◽

Metal Binding ◽

Binding Sites ◽

Proton Acceptor ◽

Homing Endonucleases ◽

Multiple Sequence ◽

Conserved Motifs ◽

X Ray ◽

Metal Binding Sites

Aim: To analyze different HNH endonucleases from various sources including the HNH endonuclease regions of CRISPR-Cas9 proteins for their conserved motifs, metal-binding sites and catalytic amino acids and propose a plausible mechanism of action for HNH endonucleases, using CRISPR-Cas9 as the model enzyme. Study Design: Multiple sequence analysis (MSA) of homing endonucleases including the CRISPR-Cas9 using Clustal Omega was studied. Other biochemical, Site-directed mutagenesis (SDM) and X-ray crystallographic data were also analyzed. Place and Duration of Study: School of Biotechnology, Madurai Kamaraj University, Madurai, India, between 2007 and 2013. Methodology: Bioinformatics, Biochemical, SDM and X-ray crystallographic data of the HNH endonucleases from different organisms including CRISPR-Cas9 enzymes were analyzed. The advanced version of Clustal Omega was used for protein sequence analysis of different HNH endonucleases from various sources. The conserved motifs identified by the bioinformatics analysis were analyzed further with the data already available from biochemical and SDM and X-ray crystallographic analyses of this group of enzymes and to confirm the possible amino acids involved in the active sites and catalysis. Results: Different types of homing endonucleases from various sources including the HNH endonuclease regions of CRISPR-Cas9 enzymes exhibit different catalytic regions and metal-binding sites. However, the catalytic amino acid, i.e., the proton acceptor histidine (His), is completely conserved in all homing endonucleases analyzed. From these data, a plausible mechanism of action for HNH endonucleases, using CRISPR-Cas9 from Streptococcus pyogenes, as the model enzyme is proposed. Furthermore, multiple sequence alignment (MSA) of various homing endonucleases from different organisms showed many highly conserved motifs also among them. However, some of the HNH endonucleases showed consensus only around the active site regions. Possible catalytic amino acids identified among them belong to either -DH---N or -HH--N types. There are at least two types of metal-binding sites and bind Mg2+ or Zn2+ or both. The CRISPR-Cas9 enzyme from S. pyogenes belongs to the -DH- based HNH endonucleases and possesses –DxD- type metal-binding site where it possibly binds to a Mg2+ ion. The other HNH enzymes possess one or two invariant Zn binding CxxC/ CxxxC motifs. Conclusions: The CRISPR-Cas9 enzymes are found to be -DH- type where the first D is likely to involve in metal-binding and the second invariant H acts as the proton acceptor and the N in –HNH- Cas9 confers specificity by interacting with the nucleotide near the catalytic region. In this communication, a metal-bound water molecule is shown as the nucleophile initiating catalysis. Homing endonucleases may be used as novel DNA binding and cleaving reagents for a variety of genome editing applications and Zinc finger nucleases have already found applications in genome editing.

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