An effective HIV-1 vaccine must elicit broadly neutralizing antibodies (bnAbs) against highly diverse Envelope glycoproteins (Env). Since Env with the longest hypervariable (HV) loops is more resistant to the cognate bnAbs than Env with shorter HV loops, we redesigned hypervariable loops for updated Env consensus sequences of subtypes B and C and CRF01_AE. Using modeling with AlphaFold2, we reduced the length of V1, V2, and V5 HV loops while maintaining the integrity of the Env structure and glycan shield, and modified the V4 HV loop. Spacers are designed to limit strain-specific targeting. All updated Env are infectious as pseudoviruses. Preliminary structural characterization suggests that the modified HV loops have a limited impact on Env\'s conformation. Binding assays show improved binding to modified subtype B and CRF01_AE Env but not to subtype C Env. Neutralization assays show increases in sensitivity to bnAbs, although not always consistently across clades. Strikingly, the HV loop modification renders the resistant CRF01_AE Env sensitive to 10-1074 despite the absence of a glycan at N332.

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔGbind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔGbind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson\'s correlation of 0.64 [0.52, 0.76], a Spearman\'s correlation of 0.73 [0.58, 0.83], and a Kendell\'s correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔGbind calculation study provides useful information about ΔΔGbind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔGbind calculations.

Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E\'s biases during training.

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

TEOSINTE BRANCHED 1/CYCLOIDEA/PROLIFERATING CELL FACTOR 1 (TCP) transcription factors control multiple aspects of growth and development in various plant species. However, few genes were reported to be directly targeted and regulated by them through their specific binding sites, and then uncover their functions in plants. A consensus DNA-binding site motif of TCP2 was identified by random binding site selection (RBSS). DNA recognized by TCP2 contained the motif G(G/T)GGNCC(A/C), which showed high consistency with motifs bound by other TCP domain proteins. Consequently, this motif was regarded as the specific DNA-binding sites of TCP2. Circadian clock associated 1 (CCA1) and EARLY FLOWERING 3 (ELF3) were subsequently considered as potential target genes owing to the containing of the similar TCP2 binding sites or core binding sites GGNCC and found to be positively regulated by TCP2 via DNA binding. Phenotype analysis results showed that mutation and over-expression of TCP2 resulted in variations in leaf morphogenesis, especially the double or triple mutations of TCP2, 4 and 10. Mutations in TCPs caused late flowering. Finally, TCP2 was shown to influence hypocotyl elongation by mediating the jasmonate signaling pathway. Overall, these results provide a basis for future studies aimed at distinguishing the target genes of TCP2 and elucidating the important roles of TCP2 in plant growth and development.

A rich diversity of transmembrane G protein-coupled receptors (GPCRs) are used by eukaryotes to sense physical and chemical signals. In humans alone, 800 GPCRs comprise the largest and most therapeutically targeted receptor class. Recent advances in GPCR structural biology have produced hundreds of GPCR structures solved by X-ray diffraction and increasingly, cryo-electron microscopy (cryo-EM). Many of these structures are stabilized by site-specific cholesterol binding, but it is unclear whether these interactions are a product of recurring cholesterol-binding motifs and if observed patterns of cholesterol binding differ by experimental technique. Here, we comprehensively analyze the location and composition of cholesterol binding sites in the current set of 473 human GPCR structural chains. Our findings establish that cholesterol binds similarly in cryo-EM and X-ray structures and show that 92% of cholesterol molecules on GPCR surfaces reside in predictable locations that lack discernable cholesterol-binding motifs.

Understanding the effects mediated by a set of nanoparticle (NP)-bound host biomolecules, often indicated with the umbrella term of NP corona, is essential in nanomedicine, nanopharmacology, and nanotoxicology. Among the NP-adsorbed proteome, some factors mediate cell binding, endocytosis, and clearing by macrophages and other phagocytes (opsonins), while some others display few affinities for the cell surface (dysopsonins). The functional mapping of opsonins and dysopsonins is instrumental to design long-circulating and nanotoxicologically safe next-generation nanotheranostics. In this review, we critically analyze functional data identifying specific proteins with opsonin or dysopsonin properties. Special attention is dedicated to the following: (1) the simplicity or complexity of the NP proteome and its modulation, (2) the role of specific host proteins in mediating the stealth properties of uncoated or polymer-coated NPs, and (3) the ability of the innate immune system, and, in particular, of the complement proteins, to mediate NP clearance by phagocytes. Emerging species-specific peculiarities, differentiating humans from preclinical animal models (the murine especially), are highlighted throughout this overview. The operative definition of opsonin and dysopsonin and the measurement schemes to assess their in vitro efficacy is critically re-examined. This provides a shared and unbiased approach useful for NP opsonin and dysopsonin systematic identification.

The urgent global public health need presented by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has brought scientists from diverse backgrounds together in an unprecedented international effort to rapidly identify interventions. There is a pressing need to apply clinical pharmacology principles and this has already been recognized by several other groups. However, one area that warrants additional specific consideration relates to plasma and tissue protein binding that broadly influences pharmacokinetics and pharmacodynamics. The principles of free drug theory have been forged and applied across drug development but are not currently being routinely applied for SARS-CoV-2 antiviral drugs. Consideration of protein binding is of critical importance to candidate selection but requires correct interpretation, in a drug-specific manner, to avoid either underinterpretation or overinterpretation of its consequences. This paper represents a consensus from international researchers seeking to apply historical knowledge, which has underpinned highly successful antiviral drug development for other viruses, such as HIV and hepatitis C virus for decades.