{BLR 2799} Amino Acid Sequences – Compugen – Nucleic Acid Sequences – Prior Art – PTO

1998 ◽  
Vol 17 (6) ◽  
pp. 783-783
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Amino Acid Sequences ◽  
Prior Art ◽  
Nucleic Acid Sequences

scholarly journals An alignment method for nucleic acid sequences against annotated genomes

2017 ◽  
Author(s):  
Koen Deforche
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Reference Genome ◽  
Query Sequence ◽  
Amino Acid Sequences ◽  
Alignment Score ◽  
Nucleic Acid Sequence ◽  
Coding Sequences ◽  
Divergent Sequences ◽  
Nucleic Acid Sequences

AbstractMotivationBiological sequence alignment is fundamental to their further interpretation. Current alignment algorithms typically align either nucleic acid or amino acid sequences. Using only nucleic acid sequence similarity, divergent sequences cannot be aligned reliably because of the limited alphabet and genetic saturation. To align divergent coding nucleic acid sequences, one can align using the translated amino acid sequences. This requires the detection of the correct open reading frame, is prone to eventual frame shift errors, and typically requires the treatment of genes separately. It was our motivation to design a nucleic acid sequence alignment algorithm to align a nucleic acid sequence against a (reference) genome sequence, that works equally well for similar and divergent sequences, and produces an optimal alignment considering simultaneously the alignment of all annotated coding sequences.ResultsWe define a genome alignment score for evaluating the quality of an alignment of a nucleic acid query sequence against a reference genome sequence, for which coding sequence features have been annotated (for example in a GenBank record). The genome alignment score combines the a ne gap score for the nucleic acid sequence with an a ne gap score for all amino acid alignments resulting from coding sequences in open reading frames contained within the query sequence. We present a Dynamic Programming algorithm to compute the optimal global or local alignment using this genomic alignment score and provide a formal proof of correctness. This algorithm allows the alignment of nucleic acid sequences from closely related and highly divergent sequences within the same software and using the same parameters, automatically correcting any eventual frame shift errors and produces at the same time the aligned translated amino acid sequences of all relevant coding sequence features.AvailabilityThe software is available as a web application at http://www.genomedetective.com/app/aga and as command-line application at https://github.com/emweb/aga

scholarly journals Algorithms for the search of amino acid patterns in nucleic acid sequences

1986 ◽  
Vol 14 (1) ◽  
pp. 99-107 ◽  
Author(s):  
Hannu Peltola ◽  
Hans Söderlund ◽  
Esko Ukkonen
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Nucleic Acid Sequences

scholarly journals Nucleic acid (cDNA) and amino acid sequences of the maize endosperm protein glutelin-2

1985 ◽  
Vol 13 (5) ◽  
pp. 1493-1504 ◽  
Author(s):  
Salomé Prat ◽  
Jordi Cortadas ◽  
Pere Puigdomènech ◽  
Jaume Palau
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Amino Acid Sequences ◽  
Maize Endosperm ◽  
Endosperm Protein

scholarly journals Diversity of the 47-kD HtrA Nucleic Acid and Translated Amino Acid Sequences from 17 Recent Human Isolates ofOrientia

2013 ◽  
Vol 13 (6) ◽  
pp. 367-375 ◽  
Author(s):  
Ju Jiang ◽  
Daniel H. Paris ◽  
Stuart D. Blacksell ◽  
Nuntipa Aukkanit ◽  
Paul N. Newton ◽  
...  
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Amino Acid Sequences

scholarly journals Single-Particle Characterization of SARS-CoV-2 Isoelectric Point and Comparison to Variants of Interest

2021 ◽  
Vol 9 (8) ◽  
pp. 1606
Author(s):  
Oluwatoyin Areo ◽  
Pratik U. Joshi ◽  
Mark Obrenovich ◽  
Moncef Tayahi ◽  
Caryn L. Heldt
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Amino Acid Sequence ◽  
Isoelectric Point ◽  
Amino Acid Sequences ◽  
Qualitative Comparison ◽  
Highly Pathogenic ◽  
Sequence Modeling ◽  
Global Pandemic ◽  
Structural Surface

SARS-CoV-2, the cause of COVID-19, is a new, highly pathogenic coronavirus, which is the third coronavirus to emerge in the past 2 decades and the first to become a global pandemic. The virus has demonstrated itself to be extremely transmissible and deadly. Recent data suggest that a targeted approach is key to mitigating infectivity. Due to the proliferation of cataloged protein and nucleic acid sequences in databases, the function of the nucleic acid, and genetic encoded proteins, we make predictions by simply aligning sequences and exploring their homology. Thus, similar amino acid sequences in a protein usually confer similar biochemical function, even from distal or unrelated organisms. To understand viral transmission and adhesion, it is key to elucidate the structural, surface, and functional properties of each viral protein. This is typically first modeled in highly pathogenic species by exploring folding, hydrophobicity, and isoelectric point (IEP). Recent evidence from viral RNA sequence modeling and protein crystals have been inadequate, which prevent full understanding of the IEP and other viral properties of SARS-CoV-2. We have thus experimentally determined the IEP of SARS-CoV-2. Our findings suggest that for enveloped viruses, such as SARS-CoV-2, estimates of IEP by the amino acid sequence alone may be unreliable. We compared the experimental IEP of SARS-CoV-2 to variants of interest (VOIs) using their amino acid sequence, thus providing a qualitative comparison of the IEP of VOIs.

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