ABSTRACT
Mayaro virus (MAYV) is an emerging arbovirus of the Americas that may cause a debilitating arthritogenic disease. The biology of MAYV is not fully understood and largely inferred from related arthritogenic alphaviruses. Here, we present the structure of MAYV at 4.4 Å resolution, obtained from a preparation of mature, infective virions. MAYV presents typical alphavirus features and organization. Interactions between viral proteins that lead to particle formation are described together with a hydrophobic pocket formed between E1 and E2 spike proteins and conformational epitopes specific of MAYV. We also describe MAYV glycosylation residues in E1 and E2 that may affect MXRA8 host receptor binding, and a molecular "handshake" between MAYV spikes formed by N262 glycosylation in adjacent E2 proteins. The structure of MAYV is suggestive of structural and functional complexity among alphaviruses, which may be targeted for specificity or antiviral activity.
Subject(s)
Alphavirus Infections/virology , Alphavirus/ultrastructure , Cryoelectron Microscopy , Mass Spectrometry , Alphavirus/immunology , Alphavirus Infections/immunology , Animals , Antibodies, Neutralizing , Chlorocebus aethiops , Glycosylation , Humans , Immunoglobulins , Membrane Proteins , Vero CellsABSTRACT
In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures.
