De novo designed proteins neutralize lethal snake venom toxins.

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.

Atomically accurate de novo design of single-domain antibodies.

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

De novo design of protein structure and function with RFdiffusion.

There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Recognition of a multiple antigen peptide containing sequence from mimotope of the dengue type 3 virus NS4B protein by human antibodies

Objective: To evaluate the recognition of NS4B mimotope, as multiple antigen peptide (MAP), by dengue antibodies presents in serum samples from patients with different serotype infections. Methods: A MAP containing mimotope sequence was synthesized and used to evaluate the recognition of NS4B mimotope as MAP by a panel of 66 human sera from dengue cases by an indirect ELISA assay. Results: The MAP differentiated between sera from dengue viruses infected patients and sera from healthy individuals and the best reactivity was shown by serum from dengue type 3 virus patients. The recognition was more intense with serum from patients with secondary infection. Conclusions: The findings suggest the potential use of NS4B mimotope on the development of a multi-epitope diagnostic tool. These results are important for further immunogenicity studies.

Recognition of a multiple antigen peptide containing sequence from mimotope of the dengue type 3 virus NS4B protein by human antibodies

OBJECTIVE@#To evaluate the recognition of NS4B mimotope, as multiple antigen peptide (MAP), by dengue antibodies presents in serum samples from patients with different serotype infections.@*METHODS@#A MAP containing mimotope sequence was synthesized and used to evaluate the recognition of NS4B mimotope as MAP by a panel of 66 human sera from dengue cases by an indirect ELISA assay.@*RESULTS@#The MAP differentiated between sera from dengue viruses infected patients and sera from healthy individuals and the best reactivity was shown by serum from dengue type 3 virus patients. The recognition was more intense with serum from patients with secondary infection.@*CONCLUSIONS@#The findings suggest the potential use of NS4B mimotope on the development of a multi-epitope diagnostic tool. These results are important for further immunogenicity studies.

Predicción del serotipo del virus del dengue mediante la respuesta de anticuerpos IgM

Se presenta un estudio en 4 grupos de muestras de suero procedentes de Cuba, Costa Rica, Nicaragua y Panamá, con el objetivo de determinar el serotipo del virus dengue responsable de una epidemia o brote, aplicando el Elisa de Captura de anticuerpos IgM (MAC-ELISA). Se empleó el estuche diagnóstico Dengue-IgM con cada uno de los serotipos por separado y se calculó un valor índice (densidad óptica de la muestra / valor de corte) en cada caso, con los cuales se realizaron 2 análisis estadísticos; el análisis de varianzas de Fisher y el cálculo de la LSD (Litter significant difference). Los cálculos permitieron determinar que en Cuba y Panamá circuló el serotipo 2 y en Costa Rica el serotipo 1, esto correspondió con los serotipos aislados durante las respectivas epidemias y en el caso de Nicaragua, las respuestas son muy heterogéneas, no se pudo determinar el serotipo responsable, probablemente porque en este país han circulado más de 2 serotipos a la vez.

Dengue: where are we today?

Dengue is considered the main arthropod-borne viral disease of humans. In the last few years, an increasing number of reports of mild and severe cases have been reported. The growing dengue incidence observed in recent years has been accompanied by reports of new observations, findings and global initiatives with an improvement in our understanding of this phenomenon. The epidemiology and new clinical classification of dengue, advances in the diagnostic and pathogenesis knowledge, and vaccine development as well as control methods including new global initiatives are summarised here.