Evidence map›Paper›PMID 41023073›Full record

ArticleScientific reports2025

Epitope mapping strategies for immunogenicity mitigation in streptokinase therapeutics: an in-silico study.

Mohammad Soroosh Hajizade, Mohammad Reza Rahbar, Maryam Kabiri, Mahdie Hajimonfarednejad, Mohammad Javad Raee

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohammad Soroosh HajizadeDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.ORCID http://orcid.org/0009-0005-4722-6503
Mohammad Reza RahbarPharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Maryam KabiriCollege of Graduate Studies, Upstate Medical University, State University of New York, New York, U.S.
Mahdie HajimonfarednejadResearch Center for Traditional Medicine and History of Medicine, Department of Persian Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Mohammad Javad RaeeDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran. raeem@sums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fibrinolytic drugs, particularly streptokinase (SK), are crucial for the management of blood clotting disorders. However, SK's bacterial origin triggers immune responses that generate neutralizing antibodies, diminishing its effectiveness. This study aimed to identify and eliminate B-cell epitopes in SK to reduce its immunogenicity through targeted point mutations. By utilizing advanced in silico tools, we predicted the SK structure and identified both linear and conformational B-cell epitopes. Hot spot residues within these epitopes were identified using a combination of epitope prediction algorithms, molecular dynamics and docking simulations, conservancy analysis, and propensity scales. Our innovative approach suggested key antigenic residues E53, D174, and S258 that were strategically mutated to minimize immunogenicity. Immuno-informatics tools indicated that the modified SK could exhibit a significantly reduced immunogenic profile. Molecular dynamics simulations supported the structural integrity of the modified SK, and docking studies suggested its preserved interaction potential with plasminogen. Our results propose that the mutein E53M-D174M-S258W could reduce the immunogenic response, thus improving its therapeutic potential.

Indexed as

Epitope MappingEpitopes, B-LymphocyteFibrinolytic AgentsStreptokinaseComputer SimulationHumansMolecular Docking SimulationMolecular Dynamics SimulationEpitopes, B-LymphocyteFibrinolytic AgentsStreptokinaseB-cell epitopesImmunogenicity reductionIn silicoPoint mutationStreptokinase

Identifiers

PMID41023073
PMCPMC12479907

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.