scholarly journals A broad comparative genomics approach to understanding the pathogenicity of Complex I mutations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Galya V. Klink ◽  
Hannah O’Keefe ◽  
Amrita Gogna ◽  
Georgii A. Bazykin ◽  
Joanna L. Elson
Keyword(s):  
Amino Acids ◽  
Complex I ◽  
Animal Species ◽  
Point Mutations ◽  
Acid Sites ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Mtdna Mutations ◽  
Pathogenic Variants

AbstractDisease caused by mutations of mitochondrial DNA (mtDNA) are highly variable in both presentation and penetrance. Over the last 30 years, clinical recognition of this group of diseases has increased. It has been suggested that haplogroup background could influence the penetrance and presentation of disease-causing mutations; however, to date there is only one well-established example of such an effect: the increased penetrance of two Complex I Leber's hereditary optic neuropathy mutations on a haplogroup J background. This paper conducts the most extensive investigation to date into the importance of haplogroup context in the pathogenicity of mtDNA mutations in Complex I. We searched for proven human point mutations across more than 900 metazoans finding human disease-causing mutations and potential masking variants. We found more than a half of human pathogenic variants as compensated pathogenic deviations (CPD) in at least in one animal species from our multiple sequence alignments. Some variants were found in many species, and some were even the most prevalent amino acids across our dataset. Variants were also found in other primates, and in such cases, we looked for non-human amino acids in sites with high probability to interact with the CPD in folded protein. Using this “local interactions” approach allowed us to find potential masking substitutions in other amino acid sites. We suggest that the masking variants might arise in humans, resulting in variability of mutation effect in our species.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Author(s):  
Thomas KF Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

ABSTRACTMultiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely-specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Thomas K F Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

Abstract Multiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals Molecular Characterization of a Novel Fusarivirus Infecting the Plant-pathogenic Fungus Alternaria Solani

2021 ◽  
Author(s):  
Jie Wei Gong ◽  
Hong Liu ◽  
Fei Xiao Zhu ◽  
Yun Shi Zhao ◽  
Le Jia Cheng ◽  
...  
Keyword(s):  
Amino Acids ◽  
Pathogenic Fungus ◽  
Alternaria Solani ◽  
Open Reading Frames ◽  
Phytopathogenic Fungus ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Putative Protein

Abstract A novel mycovirus belonging to the proposed family "Fusariviridae" was discovered in Alternaria Solani by sequencing a double-stranded RNA extracted from this phytopathogenic fungus. The virus was tentatively named “Alternaria solani fusarivirus 1” (AsFV1). AsFV1 has a single-stranded positive-sense (+ssRNA) genome of 6,845 nucleotides containing three open reading frames (ORFs) and a poly(A) tail. The largest ORF, ORF1 encodes a large polypeptide of 1,556 amino acids (aa) with conserved RNA-dependent RNA polymerase and helicase domains. The ORF2 and ORF3 have overlapping regions, encoding a putative protein of 522 amino acids (aa) and a putative protein of 105 amino acids (aa) respectively, for which function is unknown now. Multiple sequence alignments and phylogenetic analysis revealed AsFV1 belonging to Fusariviridae. This is the first report of the full-length nucleotide sequence of a fusarivirus infected with Alternaria solani.

scholarly journals Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Patrice Koehl ◽  
Henri Orland ◽  
Marc Delarue
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Principal Components ◽  
Gaussian Model ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Substitution Matrices ◽  
Multivariate Gaussian ◽  
Multivariate Gaussian Model

Residues in proteins that are in close spatial proximity are more prone to covariate as their interactions are likely to be preserved due to structural and evolutionary constraints. If we can detect and quantify such covariation, physical contacts may then be predicted in the structure of a protein solely from the sequences that decorate it. To carry out such predictions, and following the work of others, we have implemented a multivariate Gaussian model to analyze correlation in multiple sequence alignments. We have explored and tested several numerical encodings of amino acids within this model. We have shown that 1D encodings based on amino acid biochemical and biophysical properties, as well as higher dimensional encodings computed from the principal components of experimentally derived mutation/substitution matrices, do not perform as well as a simple twenty dimensional encoding with each amino acid represented with a vector of one along its own dimension and zero elsewhere. The optimum obtained from representations based on substitution matrices is reached by using 10 to 12 principal components; the corresponding performance is less than the performance obtained with the 20-dimensional binary encoding. We highlight also the importance of the prior when constructing the multivariate Gaussian model of a multiple sequence alignment.

scholarly journals Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 249 ◽  
Author(s):  
Andrew C. R. Martin
Keyword(s):  
Amino Acids ◽  
Sequence Alignment ◽  
Web Site ◽  
Web Pages ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is documented using JSDOC and is available from http://www.bioinf.org.uk/software/jsav/.

scholarly journals The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing

2019 ◽  
Author(s):  
Mark Chonofsky ◽  
Saulo H. P. de Oliveira ◽  
Konrad Krawczyk ◽  
Charlotte M. Deane
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Amino Acid ◽  
Structure Prediction ◽  
Prediction Methods ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Physico Chemical

AbstractOver the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others.Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning, respectively. To further investigate what sets these predicted contacts apart, we considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted.We found that predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts.These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology.Author summaryAccurate contact prediction has allowed scientists to predict protein structures with unprecedented levels of accuracy. The success of contact prediction methods, which are based on inferring correlations between amino acids in protein multiple sequence alignments, has prompted a great deal of work to improve the quality of contact prediction, leading to the development of several different methods for detecting amino acids in proximity.In this paper, we investigate the properties of these contact prediction methods. We find that contacts which are predicted differ from the other contacts in the protein, in particular they have more physico-chemical bonds, and the predicted contacts are more strongly conserved than other contacts across protein families. We also compared the properties of different contact prediction methods and found that the characteristics of the predicted sets depend on the prediction method used.Our results point to a link between physico-chemical bonding interactions and the evolutionary history of proteins, a connection which is reflected in their amino acid sequences.

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.

Sign in / Sign up

Export Citation Format

Share Document