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Classification model of amino acid sequences prone to aggregation of therapeutic proteins

Overview of attention for article published in In Silico Pharmacology, July 2016
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Title
Classification model of amino acid sequences prone to aggregation of therapeutic proteins
Published in
In Silico Pharmacology, July 2016
DOI 10.1186/s40203-016-0019-4
Pubmed ID
Authors

Monika Marczak, Krystyna Okoniewska, Tomasz Grabowski

Abstract

Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug - anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates - aggregate prone regions (APR). The aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins. Two groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences. Classification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented. The study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 45%
Researcher 4 18%
Other 2 9%
Professor 1 5%
Student > Doctoral Student 1 5%
Other 2 9%
Unknown 2 9%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 5 23%
Biochemistry, Genetics and Molecular Biology 4 18%
Agricultural and Biological Sciences 4 18%
Medicine and Dentistry 2 9%
Immunology and Microbiology 1 5%
Other 3 14%
Unknown 3 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2016.
All research outputs
#13,475,442
of 22,880,230 outputs
Outputs from In Silico Pharmacology
#23
of 75 outputs
Outputs of similar age
#190,140
of 355,364 outputs
Outputs of similar age from In Silico Pharmacology
#1
of 1 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 75 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,364 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them