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Rational Design of Novel Aptamers to Understand and Resist Snake Envenomation

Principle Supervisor: Dr Gabriele C Sosso

Secondary Supervisor(s): Dr Alex Baker

University of Registration: University of Warwick

BBSRC Research Themes:

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Deadline: 4 January, 2024

Project Outline

Every five minutes, fifty people are bitten by a snake worldwide – of whom, four will be permanently disabled and one will die. In fact, snake envenomation is a neglected tropical disease (NTD) that requires urgent attention. However, we are currently lacking robust, low-cost point-of-care diagnostics to combat snake envenomation. Current research has focused on antibody-based solutions, both in diagnostics and treatments. This does not need to be the case; other sensing units are possible. In 2021, Dr. Baker’s research put forward a new sensing unit for lateral flow devices – glycans (sugars), in a lateral flow glycoassay (LFGA) for COVID-19.1-3 The COVID-19 pandemic has highlighted the capabilities and value of point-of-care tests (POCT) such as lateral flow devices. Indeed, it is anticipated that the POCT market will be worth almost $70 billion USD by 2030.

Glycan (sugar)-protein interactions are fundamental in biology: however, they are difficult to unravel, requiring centralised high-resolution spectroscopy/spectrometry and/or arrays with labelled (non-native) protein probes. This is exemplified by the understudied snake venoms, which are known to contain glycan binding proteins (lectins), in potentially more than 200 species. The role of these lectins is not understood, and their biotechnology potential not deployed.

The aim of this project is to understand the binding of snake lectins to enable their targeting in diagnostic devices. To this end, we will adopt a multidisciplinary approach including elements of experimental biochemistry and synthetic chemistry (Dr. Baker) as well as data-driven computational modelling (Dr. Sosso). Unique glycan nanoscale probes (nanoprobes) will be developed in silico and tested in vitro versus native and un-purified venom, to identify new lectins and decode their binding capability. The outcomes of these experimental efforts will be used to craft a bespoke machine learning model capable of both identifying new binding targets as well as explaining the structural origin of their function/efficacy. Recent work by Dr. Sosso4 in the context of drug design and discovery demonstrates the feasibility of this joint approach. The experiments and simulations will thus cross-fertilise each other to build an unprecedented knowledge based that can be directly applied to the development of rapid diagnostics to detect snake envenomation in biological samples.

Key Objectives

  1. To understand the molecular-level interactions between glycans and glycan-binding proteins (particularly lectins) present in snake venoms.
  2. To achieve the rational design of molecular species capable to mitigate the efficiency of snake venoms.
  3. To develop a new generation of diagnostic tools to combat snake envenomation


  1. Baker, A. N.; Glycan-Based Flow-Through Device for the Detection of SARS-COV-2. ACS Sens 2021, 6 (10), 3696–3705.
  2. Baker, A. N.; The SARS-COV-2 Spike Protein Binds Sialic Acids and Enables Rapid Detection in a Lateral Flow Point of Care Diagnostic Device. ACS Cent Sci 2020, 6 (11), 2046–2052.
  3. Baker A.N., Glycosylated Gold Nanoparticles in Point of Care Diagnostics: from Aggregation to Lateral Flow. Chem. Soc. Rev., 2022, 51, 7238-7259
  4. SOSSO_1: Warren, M. T.; Biggs, C. I.; Bissoyi, A.; Gibson, M. I.; Sosso, G. C. Data-Driven Discovery of Potent Small Molecule Ice Recrystallisation Inhibitors. ChemRxiv August 31, 2023.


Organic synthesis and techniques, RAFT polymer synthesis and techniques, GPC/SEC, NMR (1H, 13C, 19F, 2D approaches), FTIR, UV-vis (including aggregation assays for studying binding), dynamic light scattering, mass spectrometry, biolayer interferometry/surface plasmon resonance, x-ray photoelectron spectroscopy and lateral flow/diagnostic design and testing approaches. These approaches will be utilised with the support and expertise of Dr. Baker.

The computational component of the project will involve the development of bespoke descriptors to model glycan-protein interactions. Machine learning models harnessing as well as feeding back into the experimental data will be developed thanks to the expertise of Dr. Sosso. Molecular dynamics and enhanced sampling simulations will also be employed on selected targets, so as to gain a molecular-level picture of the underpinning glycan-protein interactions.