The chemistry of snake venom and its medicinal potential

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Abstract

The fascination and fear of snakes dates back to time immemorial, with the first scientific treatise on snakebite envenoming, the Brooklyn Medical Papyrus, dating from ancient Egypt. Owing to their lethality, snakes have often been associated with images of perfidy, treachery and death. However, snakes did not always have such negative connotations. The curative capacity of venom has been known since antiquity, also making the snake a symbol of pharmacy and medicine. Today, there is renewed interest in pursuing snake-venom-based therapies. This Review focuses on the chemistry of snake venom and the potential for venom to be exploited for medicinal purposes in the development of drugs. The mixture of toxins that constitute snake venom is examined, focusing on the molecular structure, chemical reactivity and target recognition of the most bioactive toxins, from which bioactive drugs might be developed. The design and working mechanisms of snake-venom-derived drugs are illustrated, and the strategies by which toxins are transformed into therapeutics are analysed. Finally, the challenges in realizing the immense curative potential of snake venom are discussed, and chemical strategies by which a plethora of new drugs could be derived from snake venom are proposed.

Abstract

Snake venom contains a diversity of bioactive compounds. This Review highlights the complex chemistry of snake venom and shows how venom can be used to derive new therapeutic drugs.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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UniProt: the universal protein knowledgebase in 2021

(2020)

Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

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The ClusPro web server for protein–protein docking

Dima Kozakov, David R. Hall, BING XIA … (2017)

ClusPro is a web server that performs rigid-body docking of two proteins by sampling billions of conformations. Low-energy docked structures are clustered, and centers of the largest clusters are used as likely models of the complex.

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Author and article information

Contributors

Pedro A. Fernandes:

ORCID: http://orcid.org/0000-0003-2748-4722

pafernan@fc.up.pt

Journal

Journal ID (nlm-ta): Nat Rev Chem

Journal ID (iso-abbrev): Nat Rev Chem

Title: Nature Reviews. Chemistry

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2397-3358

Publication date (Electronic): 10 June 2022

Pages: 1-19

Affiliations

[1 ]GRID grid.5808.5, ISNI 0000 0001 1503 7226, Department of Chemistry and Biochemistry, Faculty of Sciences, , University of Porto, ; Porto, Portugal

[2 ]GRID grid.5808.5, ISNI 0000 0001 1503 7226, LAQV/Requimte, , University of Porto, ; Porto, Portugal

[3 ]GRID grid.418068.3, ISNI 0000 0001 0723 0931, Biotechnology Laboratory for Proteins and Bioactive Compounds from the Western Amazon, , Oswaldo Cruz Foundation, National Institute of Epidemiology in the Western Amazon (INCT-EpiAmO), ; Porto Velho, Brazil

[4 ]Sao Lucas Universitary Center (UniSL), Porto Velho, Brazil

Author information

Ana L. Oliveira http://orcid.org/0000-0001-6594-6022

Matilde F. Viegas http://orcid.org/0000-0002-4472-9691

Saulo L. da Silva http://orcid.org/0000-0002-7029-2016

Andreimar M. Soares http://orcid.org/0000-0003-1032-2188

Maria J. Ramos http://orcid.org/0000-0002-7554-8324

Pedro A. Fernandes http://orcid.org/0000-0003-2748-4722

Article

Publisher ID: 393

DOI: 10.1038/s41570-022-00393-7

PMC ID: 9185726

PubMed ID: 37117308

SO-VID: c6f1ecc2-0ce8-4709-a58d-01a6ccc5116e

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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

The chemistry of snake venom and its medicinal potential

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Drug_transporters

Most cited references 231

Highly accurate protein structure prediction with AlphaFold

UniProt: the universal protein knowledgebase in 2021

The ClusPro web server for protein–protein docking

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