scholarly journals Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Author(s):  
Deborah A. Sarkes ◽  
Justin P. Jahnke ◽  
Dimitra N. Stratis-Cullum

2015 ◽  
Vol 23 (5) ◽  
pp. 960-965 ◽  
Author(s):  
Hinnerk Saathoff ◽  
Mattias Brofelth ◽  
Anne Trinh ◽  
Benjamin L. Parker ◽  
Daniel P. Ryan ◽  
...  




1981 ◽  
Vol 256 (4) ◽  
pp. 1529-1532
Author(s):  
R.F. Venn ◽  
E.A. Barnard


2013 ◽  
Vol 51 (6) ◽  
pp. 1803-1808 ◽  
Author(s):  
J. D. Rogers ◽  
N. J. Ajami ◽  
B. G. Fryszczyn ◽  
M. K. Estes ◽  
R. L. Atmar ◽  
...  


2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.



2013 ◽  
Vol 14 (6) ◽  
pp. 571-581 ◽  
Author(s):  
Tianhe Li ◽  
Liming Xu ◽  
Guiping Ren ◽  
Chengkai Yin ◽  
Bing Zhou ◽  
...  


2007 ◽  
Vol 6 (12) ◽  
pp. 4758-4762 ◽  
Author(s):  
Angela M. Martin ◽  
Ting Liu ◽  
Bert C. Lynn ◽  
Anthony P. Sinai


2020 ◽  
Vol 21 (23) ◽  
pp. 9083
Author(s):  
Catherine Taylor ◽  
Simi Chacko ◽  
Michelle Davey ◽  
Jacynthe Lacroix ◽  
Alexander MacPherson ◽  
...  

Liquid biopsy is a minimally-invasive diagnostic method that may improve access to molecular profiling for non-small cell lung cancer (NSCLC) patients. Although cell-free DNA (cf-DNA) isolation from plasma is the standard liquid biopsy method for detecting DNA mutations in cancer patients, the sensitivity can be highly variable. Vn96 is a peptide with an affinity for both extracellular vesicles (EVs) and circulating cf-DNA. In this study, we evaluated whether peptide-affinity (PA) precipitation of EVs and cf-DNA from NSCLC patient plasma improves the sensitivity of single nucleotide variants (SNVs) detection and compared observed SNVs with those reported in the matched tissue biopsy. NSCLC patient plasma was subjected to either PA precipitation or cell-free methods and total nucleic acid (TNA) was extracted; SNVs were then detected by next-generation sequencing (NGS). PA led to increased recovery of DNA as well as an improvement in NGS sequencing parameters when compared to cf-TNA. Reduced concordance with tissue was observed in PA-TNA (62%) compared to cf-TNA (81%), mainly due to identification of SNVs in PA-TNA that were not observed in tissue. EGFR mutations were detected in PA-TNA with 83% sensitivity and 100% specificity. In conclusion, PA-TNA may improve the detection limits of low-abundance alleles using NGS.



2013 ◽  
Vol 17 (2) ◽  
pp. 357-369 ◽  
Author(s):  
Divya Chandra ◽  
Christopher J. Morrison ◽  
James Woo ◽  
Steven Cramer ◽  
Pankaj Karande


ChemInform ◽  
1989 ◽  
Vol 20 (33) ◽  
Author(s):  
C.-S. KUHN ◽  
C. P. J. GLAUDEMANS ◽  
J. LEHMANN
Keyword(s):  


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