The CDR3 region as the major driver of TREM-1 interaction with its ligands, an in silico characterization.

The triggering receptor expressed on myeloid cells-1 (TREM-1) is a pattern recognition receptor heavily investigated in infectious and non-infectious diseases. Because of its role in amplifying inflammation, TREM-1 has been explored as a diagnostic/prognostic biomarker. Further, as the receptor has been implicated in the pathophysiology of several diseases, therapies aiming at modulating its activity represent a promising strategy to constrain uncontrolled inflammatory or infectious diseases. Despite this, several aspects concerning its interaction with ligands and activation process, remain unclear. Although many molecules have been suggested as TREM-1 ligands, only five have been confirmed to interact with the receptor: actin, eCIRP, HMGB1, Hsp70 and PGLYRP1. However, the domains involved in the interaction between the receptor and these proteins are not clarified yet. Therefore, here we used in silico approaches to investigate the putative binding domains in the receptor, using hot spots analysis, molecular docking and molecular dynamics simulations between TREM-1 and its five known ligands. Our results indicated the complementarity-determining regions (CDRs) of the receptor as the main mediators of antigen recognition, especially the CDR3 loop. We believe that our study could be used as structural basis for the elucidation of TREM-1's recognition process, and may be useful for prospective in silico and biological investigations exploring the receptor in different contexts.

Immunoinformatics-guided design of a multi-valent vaccine against Rotavirus and Norovirus (ChRNV22).

Rotavirus (RV) and Norovirus (NV) are the main viral etiologic agents of acute gastroenteritis (AG), a serious pediatric condition associated with significant death rates and long-term complications. Anti-RV vaccination has been proved efficient in the reduction of severe AG worldwide, however, the available vaccines are all attenuated and have suboptimal efficiencies in developing countries, where AG leads to substantial disease burden. On the other hand, no NV vaccine has been licensed so far. Therefore, we used immunoinformatics tools to develop a multi-epitope vaccine (ChRNV22) to prevent severe AG by RV and NV. Epitopes were predicted against 17 prevalent genotypes of four structural proteins (NV's VP1, RV's VP4, VP6 and VP7), and then assembled in a chimeric protein, with two small adjuvant sequences (tetanus toxin P2 epitope and a conserved sequence of RV's enterotoxin, NSP4). Simulations of the immune response and interactions with immune receptors indicated the immunogenic properties of ChRNV22, including a Th1-biased response. In silico search for putative host-homologous, allergenic and toxic regions also indicated the vaccine safety. In summary, we developed a multi-epitope vaccine against different NV and RV genotypes that seems promising for the prevention of severe AG, which will be further assessed by in vivo tests.

Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika.

The Zika virus (ZIKV) is a neurotropic arbovirus considered a global threat to public health. Although there have been several efforts in drug discovery projects for ZIKV in recent years, there are still no antiviral drugs approved to date. Here, we describe the results of a global collaborative crowdsourced open science project, the OpenZika project, from IBM's World Community Grid (WCG), which integrates different computational and experimental strategies for advancing a drug candidate for ZIKV. Initially, molecular docking protocols were developed to identify potential inhibitors of ZIKV NS5 RNA-dependent RNA polymerase (NS5 RdRp), NS3 protease (NS2B-NS3pro), and NS3 helicase (NS3hel). Then, a machine learning (ML) model was built to distinguish active vs inactive compounds for the cytoprotective effect against ZIKV infection. We performed three independent target-based virtual screening campaigns (NS5 RdRp, NS2B-NS3pro, and NS3hel), followed by predictions by the ML model and other filters, and prioritized a total of 61 compounds for further testing in enzymatic and phenotypic assays. This yielded five non-nucleoside compounds which showed inhibitory activity against ZIKV NS5 RdRp in enzymatic assays (IC50 range from 0.61 to 17 µM). Two compounds thermally destabilized NS3hel and showed binding affinity in the micromolar range (Kd range from 9 to 35 µM). Moreover, the compounds LabMol-301 inhibited both NS5 RdRp and NS2B-NS3pro (IC50 of 0.8 and 7.4 µM, respectively) and LabMol-212 thermally destabilized the ZIKV NS3hel (Kd of 35 µM). Both also protected cells from death induced by ZIKV infection in in vitro cell-based assays. However, while eight compounds (including LabMol-301 and LabMol-212) showed a cytoprotective effect and prevented ZIKV-induced cell death, agreeing with our ML model for prediction of this cytoprotective effect, no compound showed a direct antiviral effect against ZIKV. Thus, the new scaffolds discovered here are promising hits for future structural optimization and for advancing the discovery of further drug candidates for ZIKV. Furthermore, this work has demonstrated the importance of the integration of computational and experimental approaches, as well as the potential of large-scale collaborative networks to advance drug discovery projects for neglected diseases and emerging viruses, despite the lack of available direct antiviral activity and cytoprotective effect data, that reflects on the assertiveness of the computational predictions. The importance of these efforts rests with the need to be prepared for future viral epidemic and pandemic outbreaks.

Optimization of Resveratrol Used as a Scaffold to Design Histone Deacetylase (HDAC-1 and HDAC-2) Inhibitors.

Histone deacetylases (HDAC) are epigenetic enzymes responsible for repressing gene expression through the deacetylation of histone lysine residues. Therefore, inhibition of HDACs has become an interesting approach for the treatment of several diseases, including cancer, hematology, neurodegenerative, immune diseases, bacterial infections, and more. Resveratrol (RVT) has pleiotropic effects, including pan-inhibition of HDAC isoforms; however, its ability to interfere with membranes requires additional optimization to eliminate nonspecific and off-target effects. Thus, to explore RVT as a scaffold, we designed a series of novel HDAC-1 and -2 inhibitors containing the 2-aminobenzamide subunit. Using molecular modeling, all compounds, except unsaturated compounds (4) and (7), exhibited a similar mode of interaction at the active sites of HDAC 1 and 2. The docking score values obtained from the study ranged from -12.780 to -10.967 Kcal/mol. All compounds were synthesized, with overall yields ranging from 33% to 67.3%. In an initial screening, compounds (4), (5), (7), and (20)-(26), showed enzymatic inhibitory effects ranging from 1 to 96% and 6 to 93% against HDAC-1 and HDAC-2, respectively. Compound (5), the most promising HDAC inhibitor in this series, was selected for IC50 assays, resulting in IC50 values of 0.44 µM and 0.37 µM against HDAC-1 and HDAC-2, respectively. In a panel of selectivity against HDACs 3-11, compound (5) presented selectivity towards Class I, mainly HDAC-1, 2, and 3. All compounds exhibited suitable physicochemical and ADMET properties as determined using in silico simulations. In conclusion, the optimization of the RVT structure allows the design of selective HDAC inhibitors, mainly targeting HDAC-1 and HDAC-2 isoforms.

Investigation of Unprecedented Sites and Proposition of New Ligands for Programmed Cell Death Protein I through Molecular Dynamics with Probes and Virtual Screening.

Cancer immunotherapy has attracted increasing attention over the last few years. Programmed cell death protein 1 (PD-1) promotes self-tolerance and inhibits immune responses by modulating the T-cell function. The interaction between PD-1 and programmed cell death ligand-1 (PD-L1) leads to immune exhaustion, protecting cancer cells from destruction. Here, we computationally designed a novel ligand named 1508 that binds to an unprecedented PD-1 cavity identified by MixMD and defined by amino acid residues Lys78 to Val97. We showed through a set of MD simulations totaling 12.5 µs that ligand 1508 establishes frequent cation-π and hydrogen bonding interactions with amino acid residues Lys78 and Arg86, respectively, and stabilizes the PD-1 C'D loop in a conformation that does not favor PD-1-PD-L1 complex formation. This study highlights the power of MixMD in exposing new cavities prone to protein-protein complex inhibition and establishes the basis for the design of new molecules that target the PD-1 C'D cavity as an alternative for exploring the modulation of the PD-1-PD-L1 complex in cancer therapy.

Computationally-obtained structural insights into the molecular interactions between Pidilizumab and binding partners DLL1 and PD-1.

Pidilizumab is a monoclonal antibody tested against several types of malignancies, such as lymphoma and metastatic melanoma, showing promising results. In 2016, the FDA put Pidilizumab's clinical studies on partial hold due to emerging evidence pointing to the antibody target uncertainty. Although initial studies indicated an interaction with the PD-1 checkpoint receptor, recent updates assert that Pidilizumab binds primarily to Notch ligand DLL1. However, a detailed description of which interactions coordinate antibody-antigen complex formation is lacking. Therefore, this study uses computational tools to identify molecular interactions between Pidilizumab and its reported targets PD-1 and DLL1. A docking methodology was validated and applied to determine the binding modes between modeled Pidilizumab scFvs and the two antigens. We used Molecular Dynamics (MD) simulations to verify the complexes' stability and submitted the resulting trajectory files to MM/PBSA and Principal Component Analysis. A set of different prediction tools determined scFv interface hot-spots. Whereas docking and MD simulations revealed that the antibody fragments do not interact straightforwardly with PD-1, ten scFv hot-spots, including Met93 and Leu112, mediated the interaction with the DLL1 C2 domain. The interaction triggered a conformational selection-like effect on DLL1, allowing new hydrogen bonds on the ß3-ß4 interface loop. The unprecedented structural data on Pidilizumab's interactions provided novel evidence that its legitimate target is the DLL1 protein and offered structural insight on how these molecules interact, shedding light on the pathways that could be affected by the use of this essential immunobiological. Communicated by Ramaswamy H. Sarma.

Ligand-induced conformational selection predicts the selectivity of cysteine protease inhibitors.

Cruzain, a cysteine protease of Trypanosoma cruzi, is a validated target for the treatment of Chagas disease. Due to its high similarity in three-dimensional structure with human cathepsins and their sequence identity above 70% in the active site regions, identifying potent but selective cruzain inhibitors with low side effects on the host organism represents a significant challenge. Here a panel of nitrile ligands with varying potencies against cathepsin K, cathepsin L and cruzain, are studied by molecular dynamics simulations as both non-covalent and covalent complexes. Principal component analysis (PCA), identifies and quantifies patterns of ligand-induced conformational selection that enable the construction of a decision tree which can predict with high confidence a low-nanomolar inhibitor of each of three proteins, and determine the selectivity for one against others.

Comparative Analysis of Electrostatic Models for Ligand Docking.

The precise modeling of molecular interactions remains an important goal among molecular modeling techniques. Some of the challenges in the field include the precise definition of a Hamiltonian for biomolecular systems, together with precise parameters derived from Molecular Mechanics Force Fields, for example. The problem is even more challenging when interaction energies from different species are computed, such as the interaction energy involving a ligand and a protein, given that small differences must be computed from large energies. Here we evaluated the effects of the electrostatic model for ligand binding energy evaluation in the context of ligand docking. For this purpose, a classical Coulomb potential with distance-dependent dielectrics was compared with a Poisson-Boltzmann (PB) model for electrostatic potential computation, based on DelPhi calculations. We found that, although the electrostatic energies were highly correlated for the Coulomb and PB models, the ligand pose and the enrichment of actual ligands against decoy compounds, were improved when binding energies were computed using PB as compared to the Coulomb model. We observed that the electrostatic energies computed with the Coulomb model were, on average, ten times larger than the energies computed with the PB model, suggesting a strong overestimation of the polar interactions in the Coulomb model. We also found that a slightly smoothed Lennard-Jones potential combined with the PB model resulted in a good compromise between ligand sampling and energetic scoring.

Polymorphisms in plastoquinol oxidase (PTOX) from Arabidopsis accessions indicate SNP-induced structural variants associated with altitude and rainfall.

Plant plastoquinol oxidase (PTOX) is a chloroplast oxidoreductase involved in carotenoid biosynthesis, chlororespiration, and response to environmental stresses. The present study aimed to gain insight of the potential role of nucleotide/amino acid changes linked to environmental adaptation in PTOX gene/protein from Arabidopsis thaliana accessions. SNPs in the single-copy PTOX gene were identified in 1190 accessions of Arabidopsis using the Columbia-0 PTOX as a reference. The identified SNPs were correlated with geographical distribution of the accessions according to altitude, climate, and rainfall. Among the 32 identified SNPs in the coding region of the PTOX gene, 16 of these were characterized as non-synonymous SNPs (in which an AA is altered). A higher incidence of AA changes occurred in the mature protein at positions 78 (31%), 81 (31.4%), and 323 (49.9%). Three-dimensional structure prediction indicated that the AA change at position 323 (D323N) leads to a PTOX structure with the most favorable interaction with the substrate plastoquinol, when compared with the reference PTOX structure (Columbia-0). Molecular docking analysis suggested that the most favorable D323N PTOX-plastoquinol interaction is due to a better enzyme-substrate binding affinity. The molecular dynamics revealed that plastoquinol should be more stable in complex with D323N PTOX, likely due a restraint mechanism in this structure that stabilize plastoquinol inside of the reaction center. The integrated analysis made from accession geographical distribution and PTOX SNPs indicated that AA changes in PTOX are related to altitude and rainfall, potentially due to an adaptive positive environmental selection.

Similarity search combined with docking and molecular dynamics for novel hAChE inhibitor scaffolds.

The main purpose of this study was to address the performance of virtual screening methods based on ligands and the protein structure of acetylcholinesterase (AChE) in order to retrieve novel human AChE (hAChE) inhibitors. In addition, a protocol was developed to identify novel hit compounds and propose new promising AChE inhibitors from the ZINC database with 10 million commercially available compounds. In this sense, 3D similarity searches using rapid overlay of chemical structures and similarity analysis through comparison of electrostatic overlay of docked hits were used to retrieve AChE inhibitors from collected databases. Molecular dynamics simulation of 100 ns was carried out to study the best docked compounds from similarity searches. Some key residues were identified as crucial for the dual binding mode of inhibitor with the interaction site. All results indicated the relevant use of EON and docking strategy for identifying novel hit compounds as promising potential anticholinesterase candidates, and seven new structures were selected as potential hAChE inhibitors. Graphical abstract Compound N01 in the 4M0E hAChE crystallography structure from docking results. Yellow dashed lines Hydrogen bonds, blue dashed lines π-stacking interactions, green dashed lines cation-π interactions.

The influence of hydrogen bonding on partition coefficients.

This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect 'frustration' of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.

Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors.

The HQSAR, molecular docking, and ROCS were applied to a data-set of 57 cruzain inhibitors. The best HQSAR model (q2 = .70, r2 = .95, [Formula: see text] = .62, [Formula: see text] = .09 and [Formula: see text] = .26), employing well-balanced, diverse training (40) and test (17) sets, was obtained using atoms (A), bonds (B), and hydrogen (H) as fragment distinctions and 6-9 as fragment sizes. This model was then used to predict the unknown potencies of 121 compounds (the V1 database), giving rise to a satisfactory predictive r2 value of .65 (external validation). By employing an extra external data-set comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC score well over .70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The area under the curve (AUC) obtained with the ROC curves (ROC AUC) was .72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC50 values of these new compounds. Some compounds identified using this method have shown calculated potencies higher than those which have originated them.

Hydrogen Bond Basicity Prediction for Medicinal Chemistry Design.

Hydrogen bonding is discussed in the context of medicinal chemistry design. Minimized molecular electrostatic potential (Vmin) is shown to be an effective predictor of hydrogen bond basicity (pKBHX), and predictive models are presented for a number of hydrogen bond acceptor types relevant to medicinal chemistry. The problems posed by the presence of nonequivalent hydrogen bond acceptor sites in molecular structures are addressed by using nonlinear regression to fit measured pKBHX to calculated Vmin. Predictions are made for hydrogen bond basicity of fluorine in situations where relevant experimental measurements are not available. It is shown how predicted pKBHX can be used to provide insight into the nature of bioisosterism and to profile heterocycles. Examples of pKBHX prediction for molecular structures with multiple, nonequivalent hydrogen bond acceptors are presented.

Automated molecule editing in molecular design.

The ability to modify chemical structures in an automated and controlled manner is useful in molecular design. This Perspective introduces the MUDO molecule editor and shows how automated molecule editing can be used to standardize structures, enumerate tautomeric and ionization states, identify matched molecular pairs. Unlike its predecessor Leatherface, MUDO can also process 3D structures and this capability can be used to link non-covalently docked ligands to proteins.