Reda Rawi

Crystal structures of trimeric HIV envelope with entry inhibitors BMS-378806 and BMS-626529

The HIV-1-envelope (Env) spike is a conformational machine that transitions between prefusion (closed, CD4-bound, CCR5-bound) and postfusion states to facilitate HIV-1 entry. Although the prefusion-closed conformation is a potential target for inhibition, development of small molecule leads has been stymied by difficulties in obtaining structural information. Here, we report crystal structures at 3.8-Å resolution of HIV-1-Env trimer with BMS-378806 and its derivative, BMS-626529, for which a prodrug version is currently in Phase III-clinical trials. Both lead candidates recognized an induced-binding pocket, which was mostly excluded from solvent and comprised of Env elements from a conserved helix and the β20-21-hairpin. In both structures, the β20-21-region assumed a conformation distinct from prefusion-closed and CD4-bound states. Together with biophysical and antigenicity characterizations, the structures illuminate the allosteric and competitive mechanisms whereby these small-molecule leads inhibit CD4–induced structural changes in Env.

A Neutralizing Antibody Recognizing Primarily N-Linked Glycan Targets the Silent Face of the HIV Envelope

&NA; Virtually the entire surface of the HIV‐1‐envelope trimer is recognized by neutralizing antibodies, except for a highly glycosylated region at the center of the “silent face” on the gp120 subunit. From an HIV‐1‐infected donor, #74, we identified antibody VRC‐PG05, which neutralized 27% of HIV‐1 strains. The crystal structure of the antigen‐binding fragment of VRC‐PG05 in complex with gp120 revealed an epitope comprised primarily of N‐linked glycans from N262, N295, and N448 at the silent face center. Somatic hypermutation occurred preferentially at antibody residues that interacted with these glycans, suggesting somatic development of glycan recognition. Resistance to VRC‐PG05 in donor #74 involved shifting of glycan‐N448 to N446 or mutation of glycan‐proximal residue E293. HIV‐1 neutralization can thus be achieved at the silent face center by glycan‐recognizing antibody; along with other known epitopes, the VRC‐PG05 epitope completes coverage by neutralizing antibody of all major exposed regions of the prefusion closed trimer. Graphical Abstract Figure. No caption available. HighlightsIdentified and defined crystal structure of antibody VRC‐PG05 in complex with gp120VRC‐PG05 epitope is at the center of the glycosylated silent face of HIV‐1 gp120VRC‐PG05 utilizes both glycopeptide and glycan‐cluster mechanisms of recognitionVRC‐PG05 completes neutralizing antibody coverage of the prefusion‐closed Env trimer &NA; The center of the “silent face” on the HIV‐1 envelope is shielded by glycans and has been devoid of antibody recognition. Zhou et al. identify the antibody VRC‐PG05, which binds a glycan‐dominated epitope at the silent face center and completes antibody recognition of all major exposed regions of the envelope trimer.

Surface-Matrix Screening Identifies Semi-specific Interactions that Improve Potency of a Near Pan-reactive HIV-1-Neutralizing Antibody

SUMMARY Highly effective HIV-1-neutralizing antibodies could have utility in the prevention or treatment of HIV-1 infection. To improve the potency of 10E8, an antibody capable of near pan-HIV-1 neutralization, we engineered 10E8-surface mutants and screened for improved neutralization. Variants with the largest functional enhancements involved the addition of hydrophobic or positively charged residues, which were positioned to interact with viral membrane lipids or viral glycan-sialic acids, respectively. In both cases, the site of improvement was spatially separated from the region of antibody mediating molecular contact with the protein component of the antigen, thereby improving peripheral semi-specific interactions while maintaining unmodified dominant contacts responsible for broad recognition. The optimized 10E8 antibody, with mutations to phenylalanine and arginine, retained the extraordinary breadth of 10E8 but with ~10-fold increased potency. We propose surface-matrix screening as a general method to improve antibodies, with improved semi-specific interactions between antibody and antigen enabling increased potency without compromising breadth.

Soluble Prefusion Closed DS-SOSIP.664-Env Trimers of Diverse HIV-1 Strains

The elicitation of autologous neutralizing responses by immunization with HIV-1 envelope (Env) trimers conformationally stabilized in a prefusion closed state has generated considerable interest in the HIV-1 vaccine field. However, soluble prefusion closed Env trimers have been produced from only a handful of HIV-1 strains, limiting their utility as vaccine antigens and B cell probes. Here, we report the engineering from 81 HIV-1 strains of soluble, fully cleaved, prefusion Env trimers with appropriate antigenicity. We used a 96-well expression-screening format to assess the ability of artificial disulfides and Ile559Pro substitution (DS-SOSIP) to produce soluble cleaved-Env trimers; from 180 Env strains, 20 yielded prefusion closed trimers. We also created chimeras, by utilizing structure-based design to incorporate select regions from the well-behaved BG505 strain; from 180 Env strains, 78 DS-SOSIP-stabilized chimeras, including 61 additional strains, yielded prefusion closed trimers. Structure-based design thus enables the production of prefusion closed HIV-1-Env trimers from dozens of diverse strains.

DeepSol: a deep learning framework for sequence-based protein solubility prediction

Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence‐based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning‐based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k‐mer structure and additional sequence and structural features extracted from the protein sequence. Results: DeepSol outperformed all known sequence‐based state‐of‐the‐art solubility prediction methods and attained an accuracy of 0.77 and Matthews correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins. Availability and implementation: DeepSols best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018). Supplementary information: Supplementary data are available at Bioinformatics online.

PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine

Motivation Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthews correlation coefficient, with an overall accuracy of 74% and Matthews correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. Availability and implementation PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP). Contact gwo-yu.chuang@nih.gov. Supplementary information Supplementary data are available at Bioinformatics online.

Structural survey of HIV-1-neutralizing antibodies targeting Env trimer delineates epitope categories and suggests vaccine templates

HIV-1 broadly neutralizing antibodies are desired for their therapeutic potential and as templates for vaccine design. Such antibodies target the HIV-1-envelope (Env) trimer, which is shielded from immune recognition by extraordinary glycosylation and sequence variability. Recognition by broadly neutralizing antibodies thus provides insight into how antibody can bypass these immune-evasion mechanisms. Remarkably, antibodies neutralizing >25% of HIV-1 strains have now been identified that recognize all major exposed surfaces of the prefusion-closed Env trimer. Here we analyzed all 206 broadly neutralizing antibody-HIV-1 Env complexes in the PDB with resolution suitable to define their interaction chemistries. These segregated into 20 antibody classes based on ontogeny and recognition, and into 6 epitope categories (V1V2, glycan-V3, CD4-binding site, silent face center, fusion peptide, and subunit interface) based on recognized Env residues. We measured antibody neutralization on a 208-isolate panel and analyzed features of paratope and B cell ontogeny. The number of protruding loops, CDR H3 length, and level of somatic hypermutation for broadly HIV-1 neutralizing antibodies were significantly higher than for a comparison set of non-HIV-1 antibodies. For epitope, the number of independent sequence segments was higher (P < 0.0001), as well as the glycan component surface area (P = 0.0005). Based on B cell ontogeny, paratope, and breadth, the CD4-binding site antibody IOMA appeared to be a promising candidate for lineage-based vaccine design. In terms of epitope-based vaccine design, antibody VRC34.01 had few epitope segments, low epitope-glycan content, and high epitope-conformational variability, which may explain why VRC34.01-based design is yielding promising vaccine results.

Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

BackgroundHuman tissues are invaluable resources for researchers worldwide. Biobanks are repositories of such human tissues and can have a strategic importance for genetic research, clinical care, and future discoveries and treatments. One of the aims of Qatar Biobank is to improve the understanding and treatment of common diseases afflicting Qatari population such as obesity and diabetes.MethodsIn this study we apply a panorama of state-of-the-art statistical methods and machine learning algorithms to investigate associations and risk factors for diabetes and obesity on a sample of 1000 Qatari population.ResultsRegarding diabetes, we identified pronounced associations and risk factors in Qatari population including magnesium, chloride, c-peptide of insulin, insulin, and uric acid. Similarly, for obesity, significant associations and risk factors include insulin, c-peptide of insulin, albumin, and uric acid. Moreover, our study has revealed interactions of hypomagnesemia with HDL-C, triglycerides, and free thyroxine.ConclusionsOur study strongly confirms known associations and risk factors associated with diabetes and obesity in Qatari population as previously found in other population studies in different parts of the world. Moreover, interactions of hypomagnesemia with other associations and risk factors merit further investigations.

Accurate Prediction of Antibody Resistance in Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection with several undergoing clinical trials. Due to high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to particular bNAbs. Resistant strains are commonly identified by time-consuming and expensive in vitro neutralization experiments. Here, we developed machine learning-based classifiers that accurately predict resistance of HIV-1 strains to 33 neutralizing antibodies. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of the tree-based machine learning method gradient boosting machine enabled us to identify critical epitope features that distinguish between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor will facilitate informed decisions of antibody usage in clinical settings.

Correction to: Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project

Following publication of the original article [1], the authors reported that one of the authors’ names was processed incorrectly. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.