PLoS Comput BiolplosploscompPLoS Computational Biology1553-7358Public Library of ScienceSan Francisco, USA10.1371/image.pcbi.v13.i11Issue ImageBiology and life sciencesPhysiologyImmune physiologyAntibodiesMedicine and health sciencesPhysiologyImmune physiologyAntibodiesBiology and life sciencesImmunologyImmune system proteinsAntibodiesMedicine and health sciencesImmunologyImmune system proteinsAntibodiesBiology and life sciencesBiochemistryProteinsImmune system proteinsAntibodiesBiology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensViral pathogensImmunodeficiency virusesHIVHIV-1Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensViral pathogensImmunodeficiency virusesHIVHIV-1Biology and life sciencesOrganismsVirusesViral pathogensImmunodeficiency virusesHIVHIV-1Biology and life sciencesOrganismsVirusesImmunodeficiency virusesHIVHIV-1Biology and life sciencesOrganismsVirusesRNA virusesRetrovirusesLentivirusHIVHIV-1Biology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensViral pathogensRetrovirusesLentivirusHIVHIV-1Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensViral pathogensRetrovirusesLentivirusHIVHIV-1Biology and life sciencesOrganismsVirusesViral pathogensRetrovirusesLentivirusHIVHIV-1Computer and information sciencesArtificial intelligenceMachine learningMedicine and health sciencesClinical medicineClinical trialsMedicine and health sciencesPharmacologyDrug research and developmentClinical trialsResearch and analysis methodsClinical trialsMedicine and health sciencesOncologyCancer treatmentAntibody therapyMedicine and health sciencesClinical medicineClinical immunologyAntibody therapyBiology and life sciencesImmunologyClinical immunologyAntibody therapyMedicine and health sciencesImmunologyClinical immunologyAntibody therapyBiology and life sciencesMicrobiologyVirologyViral structureViral envelopePLoS Computational Biology Issue Image | Vol. 13(11) November 2017Issue Image112017301120171311ev13.i112017Anna Hake/Max Planck Institute for InformaticsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.<bold>Predicting HIV-1 neutralization by antibodies using machine learning</bold>

Broadly neutralizing antibodies against HIV-1 are currently investigated in clinical trials as a new treatment option. In order to select an effective antibody therapy, the neutralization sensitivity of the patient's viral strains towards the antibodies must be ensured. Since neutralization assays are too time-consuming and expensive, they are not suitable for routine clinical practice. Applying machine learning on existing neutralization assay data, Hake and Pfeifer developed a model for accurately predicting the neutralization sensitivity of HIV-1 towards antibodies based on the viral envelope sequence. The illustration sketches how the classifier discriminates between susceptible and resistant samples.

Image Credit: Anna Hake

<fig id="image-pcbi-v13-i11-g001"><object-id pub-id-type="doi">10.1371/image.pcbi.v13.i11.g001</object-id><caption><title><bold>Predicting HIV-1 neutralization by antibodies using machine learning</bold>

Broadly neutralizing antibodies against HIV-1 are currently investigated in clinical trials as a new treatment option. In order to select an effective antibody therapy, the neutralization sensitivity of the patient's viral strains towards the antibodies must be ensured. Since neutralization assays are too time-consuming and expensive, they are not suitable for routine clinical practice. Applying machine learning on existing neutralization assay data, Hake and Pfeifer developed a model for accurately predicting the neutralization sensitivity of HIV-1 towards antibodies based on the viral envelope sequence. The illustration sketches how the classifier discriminates between susceptible and resistant samples.

Image Credit: Anna Hake

2017Anna Hake/Max Planck Institute for InformaticsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.