Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Southeastern Brazil has been suffering a rapid expansion of a severe sylvatic yellow fever virus (YFV) outbreak since late 2016, which has reached one of the most populated zones in Brazil and South America, heretofore a yellow fever-free zone for more than 70 years. In the current study, we describe the complete genome of 12 YFV samples from mosquitoes, humans and non-human primates from the Brazilian 2017 epidemic. All of the YFV sequences belong to the modern lineage (sub-lineage 1E) of South American genotype I, having been circulating for several months prior to the December 2016 detection. Our data confirm that viral strains associated with the most severe YF epidemic in South America in the last 70 years display unique amino acid substitutions that are mainly located in highly conserved positions in non-structural proteins. Our data also corroborate that YFV has spread southward into Rio de Janeiro state following two main sylvatic dispersion routes that converged at the border of the great metropolitan area comprising nearly 12 million unvaccinated inhabitants. Our original results can help public health authorities to guide the surveillance, prophylaxis and control measures required to face such a severe epidemiological problem. Finally, it will also inspire other workers to further investigate the epidemiological and biological significance of the amino acid polymorphisms detected in the Brazilian 2017 YFV strains.

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.

Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein–carbohydrate interfaces. In order to better understand protein–carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein–carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein–carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.