Highly accurate protein structure prediction and drug screen of monkeypox virus proteome

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak

IMPORTANCE

The 2022 outbreak of the monkeypox virus already involves, by April 2023, 110 countries with 86,956 confirmed cases and 119 deaths. Understanding an emerging disease on a molecular level is essential to study infection processes and eventually guide drug discovery at an early stage. To support this, we provide the so far most comprehensive structural proteome of the monkeypox virus, which includes 210 structural models, each computed with three state-of-the-art structure prediction methods. Instead of building on a single-genome sequence, we generated our models from a consensus of 3,713 high-quality genome sequences sampled from patients within 1 year of the outbreak. Therefore, we present an average structural proteome of the currently isolated viruses, including mutational analyses with a special focus on drug-binding sites. Continuing dynamic mutation monitoring within the structural proteome presented here is essential to timely predict possible physiological changes in the evolving virus.

Predicting the spatial structure of membrane protein and B-cell epitopes of the MPXV_VEROE6 strain of monkeypox virus

By targeting the membrane (M) proteins of monkeypox virus (MPXV) strain VEROE6, we analyzed its evolutionary hierarchy and predicted its dominant antigenic B-cell epitope to provide a theoretical basis for the development of MPXV epitope vaccines and related monoclonal antibodies. In this study, phylogenetic trees were constructed based on the nucleic acid sequences of MPXV and the amino acid sequences of M proteins. The 3D structure of the MPXV_VEROE6 M proteins was predicted with AlphaFold v2.0 and the dominant antigenic B-cell epitopes were comprehensively predicted by analyzing parameters such as flexible segments, the hydrophilic index, the antigenic index, and the protein surface probability. The results showed that the M protein of MPXV_VEROE6 contained 377 amino acids, and their spatial configuration was relatively regular with a turning and random coil structure. The results of a comprehensive multiparameter analysis indicated that possible B-cell epitopes were located in the 23-28, 57-63, 67-78, 80-93, 98-105, 125-131, 143-149, 201-206, 231-237, 261-270, 291-303, and 346-362 amino acid segments. This study elucidated the structural and evolutionary characteristics of MPXV membrane proteins with the aim of providing theoretical information for the development of epitope vaccines, rapid diagnostic reagents, and monoclonal antibodies for monkeypox virus.

Supervised screening of Tecovirimat-like compounds as potential inhibitors for the monkeypox virus E8L protein

Monkeypox virus (MPXV) is a budding public health threat worldwide, and there lacks a personalized drug availability to treat MPXV infections. Tecovirimat, an antiviral drug against pox viruses, is recently confirmed to be effective against the MPXV in vitro using nanomolar concentrations. Therefore, the current study considers Tecovirimat as a reference compound for a machine learning-based guided screening to scan bioactive compounds from the DrugBank with similar chemical features or moieties as the Tecovirimat to inhibit the MPXV E8L surface binding protein. We used AlphaFold2 to model the E8L's 3D structure, followed by the conformational activity investigation of shortlisted drugs through computational structural biology approaches, including molecular docking and molecular dynamics simulations. As a result, we have shortlisted five drugs named ABX-1431, Alflutinib, Avacopan, Caspitant, and Darapalib that effectively engage the MPXV surface binding protein. Furthermore, the affinity of the proposed drugs is relatively higher than the Tecovirimat by having higher docking scores, establishing more hydrogen and hydrophobic bonds, engaging key residues in the target's structure, and exhibiting stable molecular dynamics.Communicated by Ramaswamy H. Sarma.

An Updated Review on Monkeypox Viral Disease: Emphasis on Genomic Diversity

Monkeypox virus has remained the most virulent poxvirus since the elimination of smallpox approximately 41 years ago, with distribution mostly in Central and West Africa. Monkeypox (Mpox) in humans is a zoonotically transferred disease that results in a smallpox-like disease. It was first diagnosed in 1970 in the Democratic Republic of the Congo (DRC), and the disease has spread over West and Central Africa. The purpose of this review was to give an up-to-date, thorough, and timely overview on the genomic diversity and evolution of a re-emerging infectious disease. The genetic profile of Mpox may also be helpful in targeting new therapeutic options based on genes, mutations, and phylogeny. Mpox has become a major threat to global health security, necessitating a quick response by virologists, veterinarians, public health professionals, doctors, and researchers to create high-efficiency diagnostic tests, vaccinations, antivirals, and other infection control techniques. The emergence of epidemics outside of Africa emphasizes the disease's global significance. Increased monitoring and identification of Mpox cases are critical tools for obtaining a better knowledge of the ever-changing epidemiology of this disease.

Identification and Investigation of a Cryptic Binding Pocket of the P37 Envelope Protein of Monkeypox Virus by Molecular Dynamics Simulations

The spread of the monkeypox virus has surged during the unchecked COVID-19 epidemic. The most crucial target is the viral envelope protein, p37. However, lacking p37's crystal structure is a significant hurdle to rapid therapeutic discovery and mechanism elucidation. Structural modeling and molecular dynamics (MD) of the enzyme with inhibitors reveal a cryptic pocket occluded in the unbound structure. For the first time, the inhibitor's dynamic flip from the active to the cryptic site enlightens p37's allosteric site, which squeezes the active site, impairing its function. A large force is needed for inhibitor dissociation from the allosteric site, ushering in its biological importance. In addition, hot spot residues identified at both locations and discovered drugs more potent than tecovirimat may enable even more robust inhibitor designs against p37 and accelerate the development of monkeypox therapies.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets

Glycoprotein VI (GPVI) is a platelet-specific receptor for collagen and fibrin, regulating important platelet functions such as platelet adhesion and thrombus growth. Although the blockade of GPVI function is widely recognized as a potent anti-thrombotic approach, there are limited studies focused on site-specific targeting of GPVI. Using computational modeling and bioinformatics, we analyzed collagen- and CRP-binding surfaces of GPVI monomers and dimers, and compared the interacting surfaces with other mammalian GPVI isoforms. We could predict a minimal collagen-binding epitope of GPVI dimer and designed an EA-20 antibody that recognizes a linear epitope of this surface. Using platelets and whole blood samples donated from wild-type and humanized GPVI transgenic mice and also humans, our experimental results show that the EA-20 antibody inhibits platelet adhesion and aggregation in response to collagen and CRP, but not to fibrin. The EA-20 antibody also prevents thrombus formation in whole blood, on the collagen-coated surface, in arterial flow conditions. We also show that EA-20 does not influence GPVI clustering or receptor shedding. Therefore, we propose that blockade of this minimal collagen-binding epitope of GPVI with the EA-20 antibody could represent a new anti-thrombotic approach by inhibiting specific interactions between GPVI and the collagen matrix.

Computational Repurposing of Mitoxantrone-Related Structures against Monkeypox Virus: A Molecular Docking and 3D Pharmacophore Study

Monkeypox is caused by a DNA virus known as the monkeypox virus (MPXV) belonging to the Orthopoxvirus genus of the Poxviridae family. Monkeypox is a zoonotic disease where the primary significant hosts are rodents and non-human primates. There is an increasing global incidence with a 2022 outbreak that has spread to Europe in the middle of the COVID-19 pandemic. The new outbreak has novel, previously undiscovered mutations and variants. Currently, the US Food and Drug Administration (FDA) approved poxvirus treatment involving the use of tecovirimat. However, there has otherwise been limited research interest in monkeypox. Mitoxantrone (MXN), an anthracycline derivative, an FDA-approved therapeutic for treating cancer and multiple sclerosis, was previously reported to exhibit antiviral activity against the vaccinia virus and monkeypox virus. In this study, virtual screening, molecular docking analysis, and pharmacophore ligand-based modelling were employed on anthracene structures (1-13) closely related to MXN to explore the potential repurposing of multiple compounds from the PubChem library. Four chemical structures (2), (7), (10) and (12) show a predicted high binding potential to suppress viral replication.

Comparison of Monkeypox and Wart DNA Sequences with Deep Learning Model

After the COVID-19 disease, monkeypox disease has emerged today and has started to be seen almost everywhere in the world in a short time. Monkeypox causes symptoms such as fever, chills, and headache in people. In addition, rashes are seen on the skin and lumps are formed. Early diagnosis and treatment of monkeypox, which is a contagious disease, are of great importance. An expert interpretation and clinical examination are usually needed to detect monkeypox. This may cause the treatment process to be slow. Furthermore, monkeypox is sometimes confused with warts. This leads to incorrect diagnosis and treatment. Because of these disadvantages, in this study, the DNA sequences of HPV causing warts and MPV causing monkeypox were analyzed and the classification of these sequences was performed with a deep learning algorithm. The study consisted of four stages. In the first stage, DNA sequences of viruses that cause warts and monkeypox were obtained. In the second stage, these sequences were mapped using various DNA-mapping methods. In the third stage, the mapped sequences were classified using a deep learning algorithm. At the last stage, the performances of DNA-mapping methods were compared by calculating accuracy and F1-score. At the end of the study, an average accuracy of 96.08% and an F1-score of 99.83% were obtained. These results showed that these two diseases can be effectively classified according to their DNA sequences.