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Integrating linear optimization with structural modeling to increase HIV neutralization breadth
- Source :
- PLoS Computational Biology, Vol 14, Iss 2, p e1005999 (2018), PLoS Computational Biology
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.<br />Author summary In this article, we report a new approach for protein design, which combines traditional structural modeling with machine learning and integer programming. Using this method, we are able to design antibodies that are predicted to bind large panels of antigenically diverse HIV variants. The combination of methods from these fields allows us to surpass protein design limitations that have been seen up to this point. We predict that if we tested these modified antibodies against HIV variants they would have greater neutralization breadth than any antibodies seen to this point.
- Subjects :
- 0301 basic medicine
RNA viruses
Support Vector Machine
Linear programming
Computer science
Physiology
Amino Acid Motifs
HIV Infections
Protein Sequencing
HIV Antibodies
Pathology and Laboratory Medicine
Biochemistry
Sequence space
Machine Learning
Epitopes
Database and Informatics Methods
Protein sequencing
Mathematical and Statistical Techniques
Immunodeficiency Viruses
Immune Physiology
Medicine and Health Sciences
Biology (General)
Macromolecular Engineering
Integer programming
Software suite
Immune System Proteins
Ecology
Linear model
3. Good health
Computational Theory and Mathematics
Medical Microbiology
Modeling and Simulation
Viral Pathogens
Viruses
Regression Analysis
Engineering and Technology
Synthetic Biology
Pathogens
Algorithm
Sequence Analysis
Algorithms
Research Article
Biotechnology
Computer and Information Sciences
Bioinformatics
QH301-705.5
Protein design
Immunology
Bioengineering
Research and Analysis Methods
Microbiology
Antibodies
03 medical and health sciences
Cellular and Molecular Neuroscience
Sequence Motif Analysis
Artificial Intelligence
Support Vector Machines
Retroviruses
Genetics
Humans
Linear Programming
Molecular Biology Techniques
Sequencing Techniques
Molecular Biology
Microbial Pathogens
Ecology, Evolution, Behavior and Systematics
Lentivirus
Organisms
Computational Biology
Biology and Life Sciences
Proteins
HIV
Macromolecular Design
Antibodies, Neutralizing
Support vector machine
030104 developmental biology
Synthetic Bioengineering
HIV-1
Linear Models
Mathematical Functions
Software
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 14
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....5a33278fbbe81e4f7a72ed154ca60afa