Browsing by Author "Murrell, Ben."

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Identification of broadly neutralizing antibody epitopes in 1 the HIV-1 envelope glycoprotein using evolutionary models.
(Virology Journal, 2013) Lacerda, Miguel.; Moore, Penelope L.; Ngandu, Nobubelo K.; Seaman, Michael.; Gray, Elin Solomonovna.; Murrell, Ben.; Krishnamoorthy, Mohan.; Nonyane, Molati.; Madiga, Maphuti C.; Wibmer, Constantinos Kurt.; Sheward, Daniel J.; Bailer, Robert T.; Gao, Hongmei.; Greene, Kelli M.; Abdool Karim, Salim Safurdeen.; Mascola, John R.; Korber, Bette T. M.; Montefiori, David Charles.; Morris, Lynn.; Williamson, Carolyn.; Seoighe, Cathal.
Background: Identification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine. Methods: We have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope. Results: We applied our method to ID50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis. Conclusions: Our results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.
Structure discovery in hidden Markov models.
(2009) Murrell, Ben.; Spurrett, David.
The Baum-Welch algorithm for training hidden Markov models (HMMs) requires model topology and initial parameters to be specifed, and iteratively improves the model parameters. Sometimes prior knowledge of the process being modeled allows such specifcation, but often this knowledge is unavailable. Experimentation and guessing are resorted to. Techniques for discovering the model structure from observation data exist but their use is not commonplace. We propose a state split-ting approach to structure discovery, where states are split based on two heuristics: within-state autocorrelation and a measure of Markov violation in the state path. Statistical hypothesis testing is used to decide which states to split, providing a natural termination criterion and taking into account the number of observations assigned to each state, splitting states only when the data demands it.