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Amino Acid Substitution

Amino Acid Substitution refers to the replacement of one amino acid residue with another in a protein sequence.
This process can occur naturally due to genetic mutations or be engineered in the laboratory to study protein structure, function, and stability.
Understanding the effects of amino acid substitutions is crucial for advancing research in areas such as drug discovery, protein engineering, and evolutionary biology.
Discover how PubCompare.ai's AI-powered tools can help you locate and evaluate the most reliable protocols for analyzing amino acid substitutions, enabling you to make informed decisions and drive your research forward with confidence.
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Most cited protocols related to «Amino Acid Substitution»

1
Extensive Genomic Variant Annotation Pipeline
We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)16 (link) to obtain gene model annotation for single nucleotide and indel variants. For single nucleotide variants within coding sequence, we also obtained SIFT7 (link) and PolyPhen-26 (link) scores from VEP. We combined output lines describing MotifFeatures with the other annotation lines, reformatted it to a pure tabular format and reduced the different Consequence output values to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see Supplementary Note). To the 6 VEP input derived columns (chromosome, start, reference allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output derived columns, we added 56 columns providing diverse annotations (e.g. mapability scores and segmental duplication annotation as distributed by UCSC51 (link),52 (link); PhastCons and phyloP conservation scores53 (link) for three multi-species alignments9 (link) excluding the human reference sequence in score calculation; GERP++ single-nucleotides scores, element scores and p-values54 (link), also defined from alignments with the human reference excluded; background selection score40 (link),55 (link); expression value, H3K27 acetylation, H3K4 methylation, H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for ENCODE cell lines in the UCSC super tracks52 (link); genomic segment type assignment from Segway56 (link); predicted transcription factor binding sites and motifs11 (link); overlapping ENCODE ChIP-seq transcription factors11 (link), 1000 Genome variant14 (link) and Exome Sequencing Project57 (link) variant status and frequencies, Grantham scores20 (link) associated with a reported amino acid substitution). The Supplementary Note provides a full description and Supplementary Table 1 lists all columns of the obtained annotation matrix.
Kircher M., Witten D.M., Jain P., O’Roak B.J., Cooper G.M, & Shendure J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature genetics, 46(3), 310-315.
Publication 2014
Acetylation Alleles Amino Acid Substitution Binding Sites Cell Lines Chromatin Chromatin Immunoprecipitation Sequencing Chromosomes Gene Annotation Genome Homo sapiens INDEL Mutation Methylation Nucleosomes Nucleotides Open Reading Frames Segmental Duplications, Genomic Transcription, Genetic Transcription Factor
2
Phylogenetic Analysis of Bacterial Genomes
Homologs of each of the 31 phylogenetic marker genes were identified from the 578 complete bacterial genomes by BLASTP searches (using marker sequences of Escherichia coli as query sequences and a cut-off E-value of 0.1) followed by HMMer searches (cut-off E-value 1 × e-10). The corresponding protein sequences were retrieved, aligned, and trimmed as described above, and then concatenated by species into a mega-alignment. A maximum likelihood tree was then constructed from the mega-alignment using PHYML [35 (link)]. The model selected based on the likelihood ratio test was the WAG model of amino acid substitution with γ-distributed rate variation (five categories) and a proportion of invariable sites. The shape of the γ-distribution and the proportion of the invariable sites were estimated by the program.
To speed up bootstrapping analyses, very closely related taxa were removed from the original mega-alignment, which left us with 310 taxa. Maximum likelihood trees were made from 100 bootstrapped replicates of this reduced dataset using PHYML with the same parameters described above.
With very few exceptions, the marker genes are single-copy genes in all of the bacterial genomes analyzed. In those rare cases in which two or more homologs were identified within a single species, a tree-guided approach was used to resolve the redundancy. If the redundancy resulted from a species-specific duplication event, then one homolog was randomly chosen as the representative. In all other cases, to avoid potential complications such as lateral gene transfer, we excluded that marker and treated it as 'missing' in that particular genome. It has been shown that as long as there is sufficient data, a few 'holes' in the dataset will not compromise the resulting tree [36 (link)].
Wu M, & Eisen J.A. (2008). A simple, fast, and accurate method of phylogenomic inference. Genome Biology, 9(10), R151.
Publication 2008
Amino Acid Sequence Amino Acid Substitution Escherichia coli Genes Genes, Bacterial Genetic Markers Gene Transfer, Horizontal Genome Genome, Bacterial Trees
3
Plasmid DNA Sequences and sgRNA Targets
DNA sequences of plasmids used in this study can be found in Supplementary Information. sgRNAs target sites are available in Supplementary Table 1, and oligonucleotides used in this study can be found in Supplementary Table 2. SpCas9 expression plasmids containing amino acid substitutions were generated by standard PCR and molecular cloning into JDS2464 (link). sgRNA expression plasmids were constructed by ligating oligonucleotide duplexes into BsmBI cut BPK152015 (link). Unless otherwise indicated, all sgRNAs were designed to target sites containing a 5’-guanine nucleotide.
Kleinstiver B.P., Pattanayak V., Prew M.S., Tsai S.Q., Nguyen N., Zheng Z, & Joung J.K. (2016). High-fidelity CRISPR-Cas9 variants with undetectable genome-wide off-targets. Nature, 529(7587), 490-495.
Publication 2015
Amino Acid Substitution DNA Sequence Guanine Nucleotides Oligonucleotides Plasmids
4
Protein Divergence Time Estimation
For the estimation of divergence time, let s1 and s2 be two aligned protein sequences (gaps are ignored) of identical length l. A similarity score σ is defined as
where S is a log-odds score matrix. Log-odds score matrices are constructed such that substitutions by the same or a similar amino acid receive a positive score, whereas substitutions to dissimilar amino acids are attributed a negative score. The expected value for this kind of matrix is negative. This ensures that the comparison of unrelated sequences returns a negative score. For two random sequences of length l the expected score σr(l) = σ0 * l, where is the expected value of the score matrix. As we strive to measure scores above the scores for the null model of sequence independence, the score σ(s1, s2) is deducted by the expected score σr, giving the normalised score σNσN = σ(s1, s2) - σr (l).     (2)
For two random sequences of length l the expected score σr (l) = σ0 * l, where σ0 is the expected value of the score matrix.
The expected score σr for unrelated sequences can be regarded as lower limit. The upper limit of the score between s1 and any other sequences is given by σ(s1, s1). For two different sequences, the upper limit of the score σU is, for the sake of symmetry, assumed to be
and normalised
σUN = σU (s1, s2) - σr (l).     (4)
Any sound score σN is situated within the interval [0, σUN]. The validity of the upper boundary follows from the score's definition. The lower boundary might, however, get violated if two sequences receive a score σ(s1, s2) <σr (l). As the model assumes independent evolution already for σr (l), a score below σr does not contain any additional information. A lower score is therefore set to σ(s1, s2) = σr (l). We model the raw distance as a modified Poisson process
As seen in Figure 3, dr is linearly related to the true distance, deviating only by a constant factor. The Scoredist evolutionary distance estimate of two sequences is given as the product of the raw distance and a calibration factor
ds = c * dr.     (6)
Evolutionary distances of 250–300 PAM units are commonly considered as the maximum for reasonable distance estimation and, therefore, the Scoredist estimate ds is restricted to the interval [0, 300] PAM.
Calibration factors can be determined for various evolutionary models. We used the ROSE program [16 (link)] to simulate evolution with three different matrix series and generated 2000 sample sequence alignments for distances up to 200 PAM units. The calibration factor c was calculated by least squares fitting on this data, using the BLOSUM62 score matrix for calculating the score σ in the estimator (Table 2, Figure 3). The simulated evolution started with a random sequence of 200 residues. For each integer distance within the interval [1, 200] PAM, we produced 10 alignments, yielding 2000 alignments per dataset. The default gap parameters of ROSE V1.3 were applied. Each dataset was generated with the transition probability matrix and the stationary frequencies of the respective evolutionary model.
Calculation of Maximum Likelihood (ML) and Expected Distances (ED): ML distances were estimated by applying the Newton-Raphson method to the derivative of the likelihood of the evolutionary distance given an alignment. To calculate ED, the same likelihood function was numerically integrated, to get its "center of gravity" [15 (link)]. Both methods are implemented in the program lapd (L. Arvestad, unpublished), which uses Perl and Octave. The Jukes-Cantor and Kimura distance estimators were run as implemented in Belvu. The popular PROTDIST program from the PHYLIP package [19 ] calculates only ML-Dayhoff and Kimura distances. We therefore chose to use lapd in order to assess Scoredist by a broader range of distance estimators.
Sonnhammer E.L, & Hollich V. (2005). Scoredist: A simple and robust protein sequence distance estimator. BMC Bioinformatics, 6, 108.
Publication 2005
Amino Acids Amino Acid Sequence Amino Acid Substitution Biological Evolution Cantor factor A Gravity Limulus clotting factor C Sequence Alignment Sound Vision
5
Identification of TCR Motifs from VDJdb
We have used the TCRNET implementation (14 (link)) in VDJtools (15 (link)) to identify TCR nodes in VDJdb TCR similarity network that have more neighbors than expected by chance, allowing for a single amino acid substitution in the CDR3 region. Only epitopes assigned to at least 30 distinct TCR amino acid sequences were considered. Selected nodes and their first neighbors were left in the TCR similarity network and sets of homologous TCR sequences (motifs) were defined for each epitope as connected components of the resulting graph. Position weight matrices (PWMs) for CDR3 amino acid sequences of inferred motifs were constructed using connected components of the graph. PWM normalization was performed by using the probability in a control set as the information measure, where control set is a set TCR sequences having the same V/J genes and CDR3 length coming from a pool of healthy donor samples. Details of this procedure are summarized in an R markdown notebook available at https://github.com/antigenomics/vdjdb-motifs.
Bagaev D.V., Vroomans R.M., Samir J., Stervbo U., Rius C., Dolton G., Greenshields-Watson A., Attaf M., Egorov E.S., Zvyagin I.V., Babel N., Cole D.K., Godkin A.J., Sewell A.K., Kesmir C., Chudakov D.M., Luciani F, & Shugay M. (2019). VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Research, 48(D1), D1057-D1062.
Publication 2019
Amino Acid Sequence Amino Acid Substitution Epitopes Genes Homologous Sequences Tissue Donors

Most recents protocols related to «Amino Acid Substitution»

1
Conserved Amino Acid Substitutions in Canine H3N8 Influenza Viruses
Not available on PMC !

Example 10

There were conserved amino acid substitutions in all 6 canine isolates that differentiated them from contemporary equine influenza viruses (Table 9). These conserved substitutions were 115M, N83S, W222L, I328T, and N483T. Phylogenetic comparisons of the mature HA protein showed that the canine/Jax/05, canine/Miami/05, and canine/Iowa/05 viruses formed a subgroup with the canine/TX/04 isolate (FIG. 4). There were 3 amino acid changes (L118V, K261N, and G479E) that differentiated this subgroup from the other canine viruses (Table 9). There were 2 amino acid changes (F79L and G218E) that differentiated the 2005 isolates from their canine/TX/04 root. Furthermore, the 2005 isolates from non-greyhound dogs, canine/Jax/05 and canine/Miami/05, differed from the canine/Iowa/05 greyhound isolate by one amino acid change, R492K. Finally, canine/Jax/05 differed from canine/Miami/05 at a single amino acid, S107P. In all other H3N8 equine and canine viruses, S is conserved at position 107 except for A/Equine/Jilin/1/89 which has a T (Guo Y. et al., 1992).

US11865172B2. Materials and methods for respiratory disease control in canines (2024-01-09). UNIVERSITY OF FLORIDA RESEARCH FOUNDATION, INC. [US], CORNELL RESEARCH FOUNDATION, INC. [US], The Government of The United States of America as represented by The Secretary of the Department of [US]. Inventors: Patti Cynthia Crawford [US], Paul J. Gibbs [US], Edward J. Dubovi [US], Ruben Omar Donis [US], Jacqueline Katz [US], Alexander I. Klimov [US], Nallakannu P. Lakshmanan [US], Melissa Anne Lum [US], Daniel Ghislena Emiel Goovaerts [NL], Mark William Mellencamp [US], Nancy J. Cox [US], William L. Castleman [US].
Patent 2024
Amino Acids Amino Acid Substitution Canis familiaris Equus caballus Influenza Orthomyxoviridae Plant Roots Proteins Virus
2
Anti-C3d Antibody Fusion Protein
Not available on PMC !

Example 4

An exemplary fusion protein construct was designed, comprising an exemplary anti-C3d antibody (3d8b) connected to a CR1 (1-10) complement modulator polypeptide, illustrated in FIG. 13. Numbering of amino acid positions mentioned in the below exemplary design is according to the EU index as in Kabat. The anti-C3d antibody comprises a light chain (domains VL and CK) comprising the sequence in SEQ ID NO: 69, a first heavy chain (domains VH-hinge-CH1-CH2-CH3; as in the sequence in SEQ ID NO: 89) comprising amino acid substitutions Thr366Ser, Met368Ala, and Tyr407Val, forming an Fc region comprising a hole, which pairs with a second heavy chain (domains hinge-CH2-CH3) Fc region comprising amino acid substitution Thr366Trp, forming an Fc region with a knob, the second heavy chain is connected to the CR1 (1-10) complement modulator polypeptide at the hinge region, via the linker (G4SG4S) (SEQ ID NO: 242), as in the sequence of SEQ ID NO: 90.

US11879008B2. Methods of treating complement mediated diseases with fusion protein constructs comprising anti-C3d antibody and a complement modulator (2024-01-23). Q32 Bio Inc. [US]. Inventors: Michael Steven Curtis [US], Michael Storek [US], Shelia Marie Violette [US], Susan L. Kalled [US], Kelly C. Fahnoe [US], Cheng Ran Huang [US], Ellen Garber Stark [US], Frederick Robbins Taylor [US], Justin Andrew Caravella [US], Vernon Michael Holers [US].
Patent 2024
Amino Acids Amino Acid Substitution Antibodies, Anti-Idiotypic Light Polypeptides Proteins
3
PCV2b ORF2 BDH Mutant Expression
Not available on PMC !

Example 3

Several other substitutions at amino acid site 63 were produced to compare to the PCV2b ORF BDH native strain. The results from the evaluation of the PCV2b ORF2 BDH mutant constructs are shown in FIGS. 7A and 7B. The results demonstrate that in addition to the amino acid mutation from arginine (R) to threonine (T) at position 63, arginine (R) 63 to glycine (G), arginine (R) 63 to glutamine (Q), and arginine (R) 63 to aspartate (D) increased the expression of PCV2b ORF2 BDH in Sf+ cells at least Four-fold as compared to the wild type. In particular the single mutations R63G and R63Q increased PCV2b ORF2 BDH expression in Sf+ cells to levels similar to PCV2a ORF2.

US11858963B2. PCV2 ORF2 protein variant and virus like particles composed thereof (2024-01-02). Boehringer Ingelheim Animal Health USA Inc. [US]. Inventors: Luis Alejandro Hernandez [US], Christine Margaret Muehlenthaler [US], Eric Martin Vaughn [US], Gregory Haiwick [US].
Patent 2024
Amino Acids Amino Acid Substitution Arginine Aspartate Cells Figs Glutamine Glycine Mutant Proteins Mutation Strains Threonine Virion
4
Phylogenetic Analysis of N-Metabolic Genes
N-metabolic genes were selected, and alignment of these genes was performed. The top three similar gene sequences of nitrate assimilation and denitrification were retrieved after doing BLASTP against the NCBI Nr database for the sequence alignment. Sequence Manipulation Suite version 2 was used for alignment and polished the protein sequences [20 (link)]. All the protein sequences of N-metabolism genes (assimilatory and respiratory nitrate reductase, nitrite reductase, nitric oxide reductase, hydroxylamine reductase, and glutamine synthetase) of Lelliottia amnigena and their similarities genes were analyzed by BLASTP and saved in FASTA format as an input file. To investigate the phylogenetic relationship of selected nitrogen metabolism genes was performed with the help of the MEGA 11(Mega Evolutionary Genetic Analysis version 11) tool. First, the protein sequence was aligned with MUSCLE and phylogenetic tree was constructed based on neighbor-joining [21 (link)]. The percentage of bootstrap [22 (link)] values were shown at the nodes. The evolutionary distances were computed using the Jones Taylor Thornton method [23 (link)] and are in the units of the number of amino acids substitutions per site. Branch length are given below the node. It defines the genetic changes i.e., longer the branch more genetic changes.
Thakur P, & Gauba P. (2023). Identification and examination of nitrogen metabolic genes in Lelliottia amnigena PTJIIT1005 for their ability to perform nitrate remediation. BMC Genomics, 24, 104.
Publication 2023
Amino Acid Sequence Amino Acid Substitution Biological Evolution Denitrification Genes Glutamate-Ammonia Ligase hydroxylamine reductase Lelliottia amnigena MEGA 11 Metabolism Muscle Tissue Nitrate Reductase Nitrates nitric oxide reductase Nitrite Reductase Nitrogen nucleoprotein, Measles virus Reproduction Sequence Alignment
5
Phylogenetic Validation of Mitochondrial Proteins
To confirm the taxonomic identity of the putative mitochondrial-related protein identified in P. canceri and eliminate the possibility of residual contamination, maximum-likelihood phylogenetic trees were constructed (supplementary fig. S5, Supplementary Material online). Except for the mitochondrial ABC transporter gene (atm1), the phylogenetic analysis workflow was performed as follows. All mitochondrial-related proteins identified in P. canceri were queried against the NCBI nr database (August, 2020) with BLAST v.2.1.9 (Altschul et al. 1990 (link)) using the BLASTP algorithm. The top 5,000 hits with an e-value less than 1e-10 (or 1e-5 if few hits were identified) were retrieved and clustered at 90% identity with CD-HIT v.4.8.1 (Edgar 2010 (link)). The predicted proteomes of M. mackini and C. pagurus were searched with BLASTP to retrieve homologous proteins. Lastly, a reciprocal BLASTP in all P. canceri predicted proteoms was performed. The sequences were aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-auto). The alignments were trimmed of ambiguous sites with (trimAL v.1.4.1 (Capella-Gutierrez et al. 2009 (link)), -automated1). The amino acid substitution model was determined with IQ-TREE2.1.6.5 using the default settings (Kalyaanamoorthy et al. 2017 (link)). Phylogenies and 1,000 ultrafast bootstrap trees with 1,000 SH-aLRT replicates were constructed with IQ-TREE2 v.1.6.5 (Minh et al. 2013 (link)). These initial phylogenies were visualized in FigTree v.1.4.4 and manually pruned to reduce the number of taxa. The reduced data set was aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-linsi). Removal of ambiguous sites, evaluation of amino acid substitution models, and phylogenetic reconstruction proceeded as above. For the putative atm1 transporter, a Hidden Markov Model profile for orthologous group KOG0057 (retrieved from EggNOG 5.0.0 (Huerta-Cepas et al. 2019 (link)) database) was used to retrieve the protein models of P. canceri and M. mackini using the default settings of with hmmsearch. The resulting hits were used as queries against the NCBI nr database (August 2020) as described above. This dataset was supplemented with atm1 sequences reported previously (Freibert et al. 2017 (link)). The proteins were aligned with hmmalign from HMMER v.3.2.1 (http://hmmer.org/) and the Atm1 phylogeny was performed as described above.
Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.
Publication 2023
Amino Acid Substitution ATP-Binding Cassette Transporters FCER2 protein, human Genes, Mitochondrial Membrane Transport Proteins Mitochondrial Proteins Pagurus Proteins Proteome Trees

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More about "Amino Acid Substitution"

Amino Acid Substitution, also known as Amino Acid Replacement or Residue Mutation, refers to the process of exchanging one amino acid for another in a protein sequence.
This can occur naturally due to genetic mutations or be intentionally introduced in the laboratory using techniques like site-directed mutagenesis, such as the QuikChange Lightning, QuikChange, Q5, QuikChange II, and QuikChange II XL kits.
Understanding the effects of these substitutions is crucial for advancements in drug discovery, protein engineering, and evolutionary biology research.
Amino acid substitutions can impact a protein's structure, function, and stability, making them a vital area of study.
Researchers often utilize computational tools like PyMOL and PhyML to model and analyze the effects of these changes.
The QuikChange kits, Lipofectamine 2000, and QIAquick PCR Purification Kit are commonly used to facilitate the mutagenesis and purification process.
Analyzing amino acid substitutions involves examining the physicochemical properties of the original and replacement amino acids, as well as their impact on factors like folding, binding interactions, and catalytic activity.
This knowledge helps scientists engineer proteins with desired characteristics and gain insights into evolutionary mechanisms.
By leveraging AI-powered tools like those offered by PubCompare.ai, researchers can efficiently locate and evaluate the most reliable protocols for studying amino acid substitutions, empowering them to make informed decisions and drive their work forward with confidence.
Experiance the power of AI-driven analysis today and enhance the reproducibility and accuracy of your amino acid substitution research.