scholarly journals CAUSA 2.0: accurate and consistent evolutionary analysis of proteins using codon and amino acid unified sequence alignments

2015 ◽  
Author(s):  
Xiaolong Wang ◽  
Chao Yang
Keyword(s):  
Amino Acid ◽  
Sequence Alignment ◽  
Novel Mutation ◽  
Genetic Diseases ◽  
Amino Acid Level ◽  
Evolutionary Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Coding Sequences ◽  
Functional Changes

Multiple sequence alignment (MSA) is widely used to reveal structural and functional changes leading to genetic differences among species, and to reconstruct evolutionary histories of related genes, proteins and genomes. Traditionally, proteins and their coding sequences (CDSs) are aligned and analyzed separately, but often drastically different conclusions were drawn on a same set of data. Here we present a new alignment strategy, Codon and Amino Acid Unified Sequence Alignment (CAUSA) 2.0, which aligns proteins and their coding sequences simultaneously. CAUSA 2.0 optimizes the alignment of CDSs at both codon and amino acid level efficiently. Theoretical analysis showed that CAUSA 2.0 enhances the entropy information content of MSA. Empirical data analysis demonstrated that CAUSA 2.0 is more accurate and consistent than nucleotide, protein or codon level alignments. CAUSA 2.0 locates in-frame indels more accurately, makes the alignment of coding sequences biologically more significant, and reveals several novel mutation mechanisms that relate to some genetic diseases. CAUSA 2.0 is available in website www.DNAPlusPro.com .

scholarly journals CAUSA 2.0: accurate and consistent evolutionary analysis of proteins using codon and amino acid unified sequence alignments

2015 ◽  
Author(s):  
Xiaolong Wang ◽  
Chao Yang
Keyword(s):  
Amino Acid ◽  
Sequence Alignment ◽  
Novel Mutation ◽  
Genetic Diseases ◽  
Amino Acid Level ◽  
Evolutionary Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Coding Sequences ◽  
Functional Changes

Multiple sequence alignment (MSA) is widely used to reveal structural and functional changes leading to genetic differences among species, and to reconstruct evolutionary histories of related genes, proteins and genomes. Traditionally, proteins and their coding sequences (CDSs) are aligned and analyzed separately, but often drastically different conclusions were drawn on a same set of data. Here we present a new alignment strategy, Codon and Amino Acid Unified Sequence Alignment (CAUSA) 2.0, which aligns proteins and their coding sequences simultaneously. CAUSA 2.0 optimizes the alignment of CDSs at both codon and amino acid level efficiently. Theoretical analysis showed that CAUSA 2.0 enhances the entropy information content of MSA. Empirical data analysis demonstrated that CAUSA 2.0 is more accurate and consistent than nucleotide, protein or codon level alignments. CAUSA 2.0 locates in-frame indels more accurately, makes the alignment of coding sequences biologically more significant, and reveals several novel mutation mechanisms that relate to some genetic diseases. CAUSA 2.0 is available in website www.DNAPlusPro.com .

scholarly journals Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Etai Jacob ◽  
Ron Unger ◽  
Amnon Horovitz
Keyword(s):  
Amino Acid ◽  
Amino Acid Level ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
New Approach ◽  
Multiple Sequence Alignments ◽  
Residue Contacts ◽  
Level Information ◽  
Correlated Mutations ◽  
Correlated Mutation

Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

Computational Analysis of Therapeutic Enzyme Uricase from Different Source Organisms

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.

Amino Acids Sequence-based Analysis of Arginine Deiminase from Different Prokaryotic Organisms: An In Silico Approach

2020 ◽  
Vol 14 (3) ◽  
pp. 235-246
Author(s):  
Sara Abdollahi ◽  
Mohammad H. Morowvat ◽  
Amir Savardashtaki ◽  
Cambyz Irajie ◽  
Sohrab Najafipour ◽  
...  
Keyword(s):  
Amino Acid ◽  
Physicochemical Properties ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
In Silico ◽  
Bacterial Species ◽  
Arginine Deiminase ◽  
Antitumor Effects ◽  
Multiple Sequence ◽  
In Silico Studies

Background: Arginine deiminase is a bacterial enzyme, which degrades L-arginine. Some human cancers such as hepatocellular carcinoma (HCC) and melanoma are auxotrophic for arginine. Therefore, PEGylated arginine deiminase (ADI-PEG20) is a good anticancer candidate with antitumor effects. It causes local depletion of L-arginine and growth inhibition in arginineauxotrophic tumor cells. The FDA and EMA have granted orphan status to this drug. Some recently published patents have dealt with this enzyme or its PEGylated form. Objective: Due to increasing attention to it, we aimed to evaluate and compare 30 arginine deiminase proteins from different bacterial species through in silico analysis. Methods: The exploited analyses included the investigation of physicochemical properties, multiple sequence alignment (MSA), motif, superfamily, phylogenetic and 3D comparative analyses of arginine deiminase proteins thorough various bioinformatics tools. Results: The most abundant amino acid in the arginine deiminase proteins is leucine (10.13%) while the least amino acid ratio is cysteine (0.98%). Multiple sequence alignment showed 47 conserved patterns between 30 arginine deiminase amino acid sequences. The results of sequence homology among 30 different groups of arginine deiminase enzymes revealed that all the studied sequences located in amidinotransferase superfamily. Based on the phylogenetic analysis, two major clusters were identified. Considering the results of various in silico studies; we selected the five best candidates for further investigations. The 3D structures of the best five arginine deiminase proteins were generated by the I-TASSER server and PyMOL. The RAMPAGE analysis revealed that 81.4%-91.4%, of the selected sequences, were located in the favored region of arginine deiminase proteins. Conclusion: The results of this study shed light on the basic physicochemical properties of thirty major arginine deiminase sequences. The obtained data could be employed for further in vivo and clinical studies and also for developing the related therapeutic enzymes.

scholarly journals Molecular homology and multiple-sequence alignment: an analysis of concepts and practice

2015 ◽  
Vol 28 (1) ◽  
pp. 46 ◽  
Author(s):  
David A. Morrison ◽  
Matthew J. Morgan ◽  
Scot A. Kelchner
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Molecular Data ◽  
Simple Relationship ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Molecular Change ◽  
Nucleotide Homology ◽  
Tree Building ◽  
Molecular Homology

Sequence alignment is just as much a part of phylogenetics as is tree building, although it is often viewed solely as a necessary tool to construct trees. However, alignment for the purpose of phylogenetic inference is primarily about homology, as it is the procedure that expresses homology relationships among the characters, rather than the historical relationships of the taxa. Molecular homology is rather vaguely defined and understood, despite its importance in the molecular age. Indeed, homology has rarely been evaluated with respect to nucleotide sequence alignments, in spite of the fact that nucleotides are the only data that directly represent genotype. All other molecular data represent phenotype, just as do morphology and anatomy. Thus, efforts to improve sequence alignment for phylogenetic purposes should involve a more refined use of the homology concept at a molecular level. To this end, we present examples of molecular-data levels at which homology might be considered, and arrange them in a hierarchy. The concept that we propose has many levels, which link directly to the developmental and morphological components of homology. Of note, there is no simple relationship between gene homology and nucleotide homology. We also propose terminology with which to better describe and discuss molecular homology at these levels. Our over-arching conceptual framework is then used to shed light on the multitude of automated procedures that have been created for multiple-sequence alignment. Sequence alignment needs to be based on aligning homologous nucleotides, without necessary reference to homology at any other level of the hierarchy. In particular, inference of nucleotide homology involves deriving a plausible scenario for molecular change among the set of sequences. Our clarifications should allow the development of a procedure that specifically addresses homology, which is required when performing alignment for phylogenetic purposes, but which does not yet exist.

scholarly journals Benchmarking Statistical Multiple Sequence Alignment

2018 ◽  
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

scholarly journals MSAC: Compression of multiple sequence alignment files

2017 ◽  
Author(s):  
Sebastian Deorowicz ◽  
Joanna Walczyszyn ◽  
Agnieszka Debudaj-Grabysz
Keyword(s):  
Sequence Alignment ◽  
Compression Ratio ◽  
Multiple Sequence Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Link Type ◽  
Bioinformatics Databases ◽  
Supplementary Material ◽  
Burrows Wheeler Transform

AbstractMotivationBioinformatics databases grow rapidly and achieve values hardly to imagine a decade ago. Among numerous bioinformatics processes generating hundreds of GB is multiple sequence alignments of protein families. Its largest database, i.e., Pfam, consumes 40–230 GB, depending of the variant. Storage and transfer of such massive data has become a challenge.ResultsWe propose a novel compression algorithm, MSAC (Multiple Sequence Alignment Compressor), designed especially for aligned data. It is based on a generalisation of the positional Burrows–Wheeler transform for non-binary alphabets. MSAC handles FASTA, as well as Stockholm files. It offers up to six times better compression ratio than other commonly used compressors, i.e., gzip. Performed experiments resulted in an analysis of the influence of a protein family size on the compression ratio.AvailabilityMSAC is available for free at https://github.com/refresh-bio/msac and http://sun.aei.polsl.pl/REFRESH/[email protected] materialSupplementary data are available at the publisher Web site.

scholarly journals Structures of core eukaryotic protein complexes

2021 ◽  
Author(s):  
Ian R. Humphreys ◽  
Jimin Pei ◽  
Minkyung Baek ◽  
Aditya Krishnakumar ◽  
Ivan Anishchenko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Amino Acid ◽  
Protein Complexes ◽  
Structure Modeling ◽  
Protein Assemblies ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Eukaryotic Protein ◽  
Recent Advances ◽  
Coevolution Analysis

AbstractProtein-protein interactions play critical roles in biology, but despite decades of effort, the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions that have not yet been identified. Here, we take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes, as represented within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of S. cerevisiae proteins and build models for strongly predicted protein assemblies with two to five components. Comparison to existing interaction and structural data suggests that these predictions are likely to be quite accurate. We provide structure models spanning almost all key processes in Eukaryotic cells for 104 protein assemblies which have not been previously identified, and 608 which have not been structurally characterized.One-sentence summaryWe take advantage of recent advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes.

scholarly journals SequenceBouncer: A method to remove outlier entries from a multiple sequence alignment

2020 ◽  
Author(s):  
Cory D. Dunn
Keyword(s):  
Nucleic Acid ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Analyses ◽  
Protein Sequences ◽  
Mitochondrial Genomes ◽  
Dna Barcodes ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

AbstractPhylogenetic analyses can take advantage of multiple sequence alignments as input. These alignments typically consist of homologous nucleic acid or protein sequences, and the inclusion of outlier or aberrant sequences can compromise downstream analyses. Here, I describe a program, SequenceBouncer, that uses the Shannon entropy values of alignment columns to identify outlier alignment sequences in a manner responsive to overall alignment context. I demonstrate the utility of this software using alignments of available mammalian mitochondrial genomes, bird cytochrome c oxidase-derived DNA barcodes, and COVID-19 sequences.

RELATIVE VON NEUMANN ENTROPY FOR EVALUATING AMINO ACID CONSERVATION

2010 ◽  
Vol 08 (05) ◽  
pp. 809-823 ◽  
Author(s):  
FREDRIK JOHANSSON ◽  
HIROYUKI TOH
Keyword(s):  
Amino Acid ◽  
Sequence Analysis ◽  
Shannon Entropy ◽  
Von Neumann Entropy ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Von Neumann ◽  
Multiple Sequence Alignments ◽  
Previous Definition ◽  
Definition Of

The Shannon entropy is a common way of measuring conservation of sites in multiple sequence alignments, and has also been extended with the relative Shannon entropy to account for background frequencies. The von Neumann entropy is another extension of the Shannon entropy, adapted from quantum mechanics in order to account for amino acid similarities. However, there is yet no relative von Neumann entropy defined for sequence analysis. We introduce a new definition of the von Neumann entropy for use in sequence analysis, which we found to perform better than the previous definition. We also introduce the relative von Neumann entropy and a way of parametrizing this in order to obtain the Shannon entropy, the relative Shannon entropy and the von Neumann entropy at special parameter values. We performed an exhaustive search of this parameter space and found better predictions of catalytic sites compared to any of the previously used entropies.

Sign in / Sign up

Export Citation Format

Share Document