Biology subjects

Quadeer, A. A.

Publications and source records attributed to Quadeer, A. A..

10 recordsLinked to original sources

bioRxiv2025

Back-projection improves inference from sparsely sampled genomic surveillance data

Highly transmissible SARS-CoV-2 variants have emerged throughout the COVID-19 pandemic, driving new waves of infections. Genomic surveillance data can provide insights into the viruss evolution and biology. However, delayed and limited regional data can introduce biases in epidemiological models, potentially obscuring transmission patterns. To address this issue, we used a novel, variant-specific back-projection model to estimate a distribution of likely infection times from sample collection times. We combined this approach with epidemiological modeling to estimate selection for increased transmission in a way that accounts for the uncertainty in infection times. Tests in simulations demonstrated that our method can make the inference of selection more reliable. We also applied our approach to SARS-CoV-2 data, where it excelled in smoothing and extending data from geographic regions or times with poor sampling. Overall, our method can aid in the reliable identification of mutations and variants with higher transmission rates.

evolutionary biology↗

bioRxiv2025

IFNBoost: An interpretable computational model for identifying IFNγ inducing peptides

MotivationInterferon-gamma (IFN{gamma}) is a pivotal cytokine that coordinates various aspects of the immune response, notably enhancing T-cell activation, clearing intracellular pathogens, and providing long-term immune protection. Identification of IFN{gamma}-inducing peptides is essential for the advancement of peptide-based vaccines and immunotherapies; however, the experimental determination of these peptides is hampered by the large number of potential peptide candidates present in pathogen proteins. ResultsIn this study, we present IFNBoost, a machine learning model developed to accurately predict IFN{gamma}-inducing peptides by leveraging existing immunological datasets, including both peptide sequences and associated metadata. IFNBoost demonstrates impressive performance metrics, achieving an accuracy of 0.819, an F1 score of 0.798, and a Matthews correlation coefficient (MCC) of 0.634. Evaluation against independent datasets demonstrates that IFNBoost surpasses all current models for predicting IFN{gamma}-inducing peptides, highlighting generalizability of the model. Our comprehensive analysis indicates that, in addition to peptide sequences, metadata features such as the source organism and host significantly enhance predictive accuracy. The predictions produced by IFNBoost have the potential to guide rational vaccine design, thereby improving vaccine efficacy via precise identification of peptides that elicit the desired cytokine responses. Availability and implementationTo improve the accessibility and utility of our model, we have developed a web application available at https://ifnboost.streamlit.app/.

bioinformatics↗

bioRxiv2023

HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies

The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a noncovalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remains largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modelling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness, and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.

bioinformatics↗

bioRxiv2022

Vaccinia virus vaccination is expected to elicit highly cross-reactive immunity to the 2022 monkeypox virus

Starting May 2022, a novel cluster of monkeypox virus infections was detected in humans. This has spread rapidly to non-endemic countries and sparked global concern. Vaccinia virus vaccines have demonstrated high efficacy against monkeypox viruses in the past and are considered an important outbreak control measure. Viruses observed in the current outbreak carry distinct genetic variation that have the potential to affect vaccine-induced immune recognition. Here, by investigating genetic variation with respect to orthologous immunogenic vaccinia-virus proteins, we report data that anticipates vaccine-induced immune responses to remain highly cross-reactive against the newly observed monkeypox viruses.

immunology↗

bioRxiv2021

SARS-CoV-2 T cell responses are expected to remain robust against Omicron

Omicron, the most recent SARS-CoV-2 variant of concern (VOC), harbours multiple mutations in the spike protein that were not observed in previous VOCs. Initial studies suggest Omicron to substantially reduce the neutralizing capability of antibodies induced from vaccines and previous infection. However, its effect on T cell responses remains to be determined. Here, we assess the effect of Omicron mutations on known T cell epitopes and report data suggesting T cell responses to remain broadly robust against this new variant.

immunology↗

bioRxiv2021

Evolutionary modelling of HCV subtypes provides rationale for their different disease outcomes

Hepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection, cirrhosis and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the envelope glycoprotein 2 (E2) of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of subtype 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear to be escape-resistant against both subtypes 1a and 1b, providing directions for the design of HCV vaccines having cross-subtype protection.

evolutionary biology↗

bioRxiv2020

Epitopes targeted by T cells in convalescent COVID-19 patients

Knowledge of the epitopes of SARS-CoV-2 that are targeted by T cells in convalescent patients is important for understanding T cell immunity against COVID-19. This information can aid the design, development and assessment of COVID-19 vaccines, and inform novel diagnostic technologies. Here we provide a unified description and meta-analysis of emerging data of SARS-CoV-2 T cell epitopes compiled from 15 independent studies of cohorts of convalescent COVID-19 patients. Our analysis demonstrates the broad diversity of T cell epitopes that have been collectively recorded for SARS-CoV-2, while also identifying a selected set of immunoprevalent epitopes that induced consistent T cell responses in multiple cohorts and in a large fraction of tested patients. The landscape of SARS-CoV-2 T cell epitopes that we describe can help guide future immunological studies, including those related to vaccines and diagnostics. A web-based platform has been developed to help complement these efforts.

immunology↗

bioRxiv2020

COVIDep: A web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2

We introduce COVIDep (https://COVIDep.ust.hk), a web-based platform that provides immune target recommendations for guiding SARS-CoV-2 vaccine development. COVIDep implements a protocol that pools together publicly-available genetic data for SARS-CoV-2 and epitope data for SARS-CoV to identify B cell and T cell epitopes that present potential immune targets for SARS-CoV-2. Correspondences between outputs of COVIDep and immune responses recorded in COVID-19 patients and preclinical vaccine trials are also indicated. The platform is user-friendly, flexible, and based on up-to-date data. It may help guide vaccine designs and associated experimental studies for SARS-CoV-2.

bioinformatics↗

bioRxiv2020

Preliminary identification of potential vaccine targets for 2019-nCoV based on SARS-CoV immunological studies

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the available SARS-CoV-2 sequences (as of 9 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

bioinformatics↗

bioRxiv2018

Co-evolution networks of HIV/HCV are modular with direct association to structure and function

Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies.\n\nAuthor summaryHIV and HCV cause devastating infectious diseases for which no functional vaccine exists. A key problem is that while immune cells may induce individual mutations that compromise viral fitness, this is typically restored through other \"compensatory\" mutations, leading to immune escape. These compensatory pathways are complicated and remain poorly understood. They do, however, leave co-evolutionary markers which may be inferred from measured sequence data. Here, by introducing a new robust statistical method, we demonstrated that the compensatory networks employed by both viruses exhibit a remarkably simple decomposition involving small and near-distinct groups of protein residues, with most groups having a clear association to biological function or structure. This provides insights that can be harnessed for the purpose of vaccine design.

bioinformatics↗