Evidence map›Paper›PMID 33876197›Full record

ArticleBriefings in bioinformatics2021

IsAb: a computational protocol for antibody design.

Tianjian Liang, Hui Chen, Jiayi Yuan, Chen Jiang, Yixuan Hao, Yuanqiang Wang, Zhiwei Feng, Xiang-Qun Xie

Open access · greenAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 17 citations in OpenAlex.

  1. AI-based IsAb2.0 for antibody design.Briefings in bioinformatics · 2024
    Article
  2. Article
  3. Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.International journal of molecular sciences · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Review
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

8 authors at 1 institution in 1 country.

Tianjian LiangSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Hui ChenSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Jiayi YuanSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Chen JiangSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Yixuan HaoSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Yuanqiang WangSchool of Pharmacy and Bioengineering, Chongqing University of Technology, Pittsburgh, PA 15261, USA.
Zhiwei FengSchool of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Xiang-Qun XieComputational Drug Abuse Research and Computational Chemogenomics Screening Center at the University of Pittsburgh, Pittsburgh, PA 15261, USA.
University of Pittsburgh · US

Funding

NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR)P30DA035778 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, XIE, XIANG-QUN · 2014 to 2018
$5.4M
National Institutes of Health, National Institute on Drug Abuse P30 DA035778A1NIDA NIH HHS P30 DA035778
6 · The paper itself

Abstract

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

Indexed as

Computer-Aided DesignMolecular Docking SimulationProtein DomainsAnimalsAntibodiesAntibodies, Monoclonal, HumanizedAntibody SpecificityAntigen-Antibody ComplexBinding Sites, AntibodyComputational BiologyComputer SimulationCrystallography, X-RayHumansProgrammed Cell Death 1 ReceptorProtein BindingAntibodiesAntibodies, Monoclonal, HumanizedAntigen-Antibody ComplexcemiplimabProgrammed Cell Death 1 Receptorantibody designcomputer-aided antibody protocolprotein engineeringprotein–protein docking

Identifiers

PMID33876197
PMCPMC8579189
OpenAlexW3153214143

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.