Mutant Proteins
These variants can arise from genetic mutations, post-translational modifications, or other processes that change the structure and/or function of the original protein.
Studying Mutant Proteins is crucial for understanding disease mechanisms, drug targeting, and protein engineering.
PubCompare.ai's AI-driven tools can help researchers optimize protocols and uncover insights by locating the best published protocols, pre-prints, and patents related to Mutant Proteins research.
Streamline your work and discover the optimal approach using PubCompare.ai's intellegent comparison features.
Most cited protocols related to «Mutant Proteins»
firestar (Lopez et al. 2007 (link), 2011 (link)) is a method that predicts functionally important residues in protein sequences.
Matador3D is locally installed and checks for structural homologs for each transcript in the PDB (Berman et al. 2000 (link)).
SPADE uses a locally installed version of the program Pfamscan (Finn et al. 2010 (link)) to identify the conservation of protein functional domains.
INERTIA detects exons with non-neutral evolutionary rates. Transcripts are aligned against related species using three different alignment methods, Kalign (Lassmann and Sonnhammer 2005 (link)), multiz (Blanchette et al. 2004 (link)), and PRANK (Loytynoja and Goldman 2005 (link)), and evolutionary rates of exons for each of the three alignments are contrasted using SLR (Massingham and Goldman 2005 (link)).
CRASH makes conservative predictions of signal peptides and mitochondrial signal sequences by using locally installed versions of the SignalP and TargetP programs (Emanuelsson et al. 2007 (link)) .
THUMP makes conservative predictions of trans-membrane helices by analyzing the output of three locally installed trans-membrane prediction methods, MemSat (Jones 2007 (link)), PRODIV (Viklund and Elofsson 2004 (link)), and PHOBIUS (Kall et al. 2004 (link)).
CExonic is a locally developed method that uses exonerate (Slater and Birney 2005 (link)) to align mouse and human transcripts and then looks for patterns of conservation in exonic structure.
CORSAIR is a locally installed method that checks for orthologs for each variant in a locally installed vertebrate protein sequence database.
Previous studies have reported performance comparisons of difference methods on predicting changes in folding free energy (ΔΔG) using these datasets (20–22 (link)). Given the unbalanced nature of the original dataset, here we have considered the hypothetical reverse mutations (22 (link)) in order to build a more robust, balanced and self-consistent predictive method. The change in folding free energy is a thermodynamic state function, and it has been proposed that the change in folding free energy of a mutation from a wild-type protein to its mutant (ΔΔGWT→MT) should be equivalent to the negative change in folding free energy of the hypothetical reverse mutation—from the mutant to the wild-type protein (–ΔΔGMT→WT) (16 (link),22–24 (link)). Including the hypothetical reverse mutations, our predictive model was trained using 4594 mutations and our blind test was comprised of 702 single-point mutations.
Most recents protocols related to «Mutant Proteins»
Example 2
This study demonstrates the efficacy of one embodiment of the Porcine Circovirus Type 2 ORF2b Vaccine against a PCV2a and/or PCV2b challenge. Cesarean-derived colostrum-deprived (CDCD) piglets are used in this study and separated into 2 groups; 1) pigs vaccinated with an experimental Porcine Circovirus Vaccine including the PCV2b ORF2 R63T variant of Example 1 (Killed Baculovirus Vector) that are challenged with virulent PCV2b and, 2) non-vaccinated challenged control pigs that are challenged with virulent PCV2b. On Day 0, Group 1 is administered 1 mL of vaccine intramuscularly (IM) whereas Group 2, non-vaccinated challenge control pigs do not receive any treatment. On Day 28, all pigs in groups 1 and 2 are challenged with virulent PCV2b 1 mL intranasally (IN) and 1 mL IM with an approximate dosage of 3.0 Log10 TCID5/mL of live virus. All pigs receive 2.0 mL Keyhole Limpet Hemocyanin emulsified in Incomplete Freunds Adjuvant (KLH/ICFA) IM on Days 25 and 31. Pigs are monitored daily for clinical signs, and blood is drawn for serologic testing periodically. On Day 56 all pigs are necropsied and select tissues are collected and gross pathology observations are made.
As a whole, vaccinated animals exhibit reduction when compared to their respective challenge control group in all parameters tested.
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
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More about "Mutant Proteins"
These protein variants can arise from genetic mutations, post-translational modifications, or other processes that change the structure and/or function of the original protein.
Studying mutant proteins is crucial for understanding disease mechanisms, drug targeting, and protein engineering.
Researchers can leverage powerful tools like PubCompare.ai to optimize their protocols and uncover valuable insights for mutant proteins research.
PubCompare.ai's AI-driven features can help locate the best published protocols, pre-prints, and patents related to mutant proteins, allowing researchers to streamline their work and discover the optimal approach.
Techniques such as Lipofectamine 2000, QuikChange site-directed mutagenesis kit, Lipofectamine 3000, and QuikChange II XL Site-Directed Mutagenesis Kit are commonly used in mutant proteins research.
These tools can be used to introduce specific mutations or modifications into proteins, enabling researchers to study the effects of these changes.
Data analysis tools like GraphPad Prism 7 can be employed to analyze the results of mutant proteins experiments, while reagents like FBS and SYPRO Orange can be used to support the research process.
Expression vectors like pcDNA3.1 and antibodies like Anti-FLAG antibody can also be valuable for studying mutant proteins.
By harnessing the power of PubCompare.ai's intelligent comparison features, researchers can streamline their mutant proteins research, uncover new insights, and discover the optimal protocols and approaches to drive their work forward.