Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting

Computational neutralization fingerprinting, NFP, is an efficient and accurate method for predicting the epitope specificities of polyclonal antibody responses to HIV-1 infection. Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. Specifically, we developed algorithms for: (a) selection of optimized virus neutralization panels for NFP analysis, (b) estimation of NFP prediction confidence for each serum sample, and (c) identification of sera with potentially novel epitope specificities. At the individual donor level, the next-generation NFP algorithms particularly improved the ability to detect multiple epitope specificities in a sample, as confirmed both for computationally simulated polyclonal sera and for samples from HIV-infected donors. Specifically, the next-generation NFP algorithms detected multiple specificities in twice as many samples of simulated sera. Further, unlike the first-generation NFP, the new algorithms were able to detect both of the previously confirmed antibody specificities, VRC01-like and PG9-like, in donor CHAVI 0219. At the cohort level, analysis of ~150 broadly neutralizing HIV-infected donor samples suggested a potential connection between clade of infection and types of elicited epitope specificities. Most notably, while 10E8-like antibodies were observed in infections from different clades, an enrichment of such antibodies was predicted for clade B samples. Ultimately, such largescale analyses of antibody responses to HIV-1 infection can help guide the design of epitope-specific vaccines that are tailored to take into account the prevalence of infecting clades within a specific geographic region. Overall, the next-generation NFP technology will be an important tool for the analysis of broadly neutralizing polyclonal antibody responses against HIV-1

Delineating antibody recognition in polyclonal sera from patterns of HIV-1 isolate neutralization.

Serum characterization and antibody isolation are transforming our understanding of the humoral immune response to viral infection. Here, we show that epitope specificities of HIV-1–neutralizing antibodies in serum can be elucidated from the serum pattern of neutralization against a diverse panel of HIV-1 isolates. We determined “neutralization fingerprints” for 30 neutralizing antibodies on a panel of 34 diverse HIV-1 strains and showed that similarity in neutralization fingerprint correlated with similarity in epitope. We used these fingerprints to delineate specificities of polyclonal sera from 24 HIV-1–infected donors and a chimeric siman-human immunodeficiency virus–infected macaque. Delineated specificities matched published specificities and were further confirmed by antibody isolation for two sera. Patterns of virus-isolate neutralization can thus afford a detailed epitope-specific understanding of neutralizing-antibody responses to viral infection

Active search for computer-aided drug design

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an av integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined avb6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays

Immunoglobulin gene insertions and deletions in the affinity maturation of HIV-1 broadly reactive neutralizing antibodies.

CAPRISA, 2014.Abstract available in pdf

Machine learning for molecular and materials science

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</p

Developmental pathway for potent V1V2-directed HIV-neutralizing antibodies.

CAPRISA, 2014.Antibodies capable of neutralizing HIV-1 often target variable regions 1 and 2 (V1V2) of the HIV-1 envelope, but the mechanism of their elicitation has been unclear. Here we define the developmental pathway by which such antibodies are generated and acquire the requisite molecular characteristics for neutralization. Twelve somatically related neutralizing antibodies (CAP256-VRC26.01-12) were isolated from donor CAP256 (from the Centre for the AIDS Programme of Research in South Africa (CAPRISA)); each antibody contained the protruding tyrosine-sulphated, anionic antigen-binding loop (complementarity-determining region (CDR) H3) characteristic of this category of antibodies. Their unmutated ancestor emerged between weeks 30-38 post-infection with a 35-residue CDR H3, and neutralized the virus that superinfected this individual 15 weeks after initial infection. Improved neutralization breadth and potency occurred by week 59 with modest affinity maturation, and was preceded by extensive diversification of the virus population. HIV-1 V1V2-directed neutralizing antibodies can thus develop relatively rapidly through initial selection of B cells with a long CDR H3, and limited subsequent somatic hypermutation. These data provide important insights relevant to HIV-1 vaccine development

Cargo Genes of Tn 7 -Like Transposons Comprise an Enormous Diversity of Defense Systems, Mobile Genetic Elements, and Antibiotic Resistance Genes

Transposons are major vehicles of horizontal gene transfer that, in addition to genes directly involved in transposition, carry cargo genes. However, characterization of these genes is hampered by the difficulty of identification of transposon boundaries.</jats:p

Dual modes of CRISPR-associated transposon homing

Tn7-like transposons have co-opted CRISPR systems, including class 1 type I-F, I-B, and class 2 type V-K. Intriguingly, although these CRISPR-associated transposases (CASTs) undergo robust CRISPR RNA (crRNA)-guided transposition, they are almost never found in sites targeted by the crRNAs encoded by the cognate CRISPR array. To understand this paradox, we investigated CAST V-K and I-B systems and found two distinct modes of transposition: (1) crRNA-guided transposition and (2) CRISPR array-independent homing. We show distinct CAST systems utilize different molecular mechanisms to target their homing site. Type V-K CAST systems use a short, delocalized crRNA for RNA-guided homing, whereas type I-B CAST systems, which contain two distinct target selector proteins, use TniQ for RNA-guided DNA transposition and TnsD for homing to an attachment site. These observations illuminate a key step in the life cycle of CAST systems and highlight the diversity of molecular mechanisms mediating transposon homing