Characterization of lignin degrading enzyme PmdC, which catalyzes a key step in the synthesis of polymer precursor 2-pyrone-4,6-dicarboxylic acid (PDC).

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme's mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

Are protein-ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?

Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.

Unraveling Stereochemical Structure-Activity Relationships of Sesquiterpene Lactones for Inhibitory Effects on STAT3 Activation.

Sesquiterpene lactones, a class of natural compounds abundant in the Asteraceae family, have gained attention owing to their diverse biological activities, and particularly their anti-proliferative effects on human cancer cells. In this study, we systematically investigated the structure-activity relationship of ten sesquiterpene lactones with the aim of elucidating the structural determinants for the STAT3 inhibition governing their anti-proliferative effects. Our findings revealed a significant correlation between the STAT3 inhibitory activity and the anti-proliferative effects of sesquiterpene lactones in MDA-MB-231 breast cancer cell lines. Among the compounds tested, alantolactone and isoalantolactone emerged as the most potent STAT3 inhibitors, highlighting their potential as candidates for anticancer drug development. Through protein-ligand docking studies, we revealed the structural basis of STAT3 inhibition by sesquiterpene lactones, emphasizing the critical role of hydrogen-bonding interactions with key residues, including Arg609, Ser611, Glu612, and Ser613, in the SH2 domain of STAT3. Furthermore, our conformational analysis revealed the decisive role of the torsion angle within the geometry-optimized structures of sesquiterpene lactones in their STAT3 inhibitory activity (R=0.80, p<0.01). These findings not only provide preclinical evidence for sesquiterpene lactones as promising phytomedicines against diseases associated with abnormal STAT3 activation, but also highlight the importance of stereochemical aspects in their activity.

The molecular determinants of calcium ATPase inhibition by curcuminoids.

The natural product curcumin and some of its analogs are known inhibitors of the transmembrane enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA). Despite their widespread use, the curcuminoids' binding site in SERCA and their relevant interactions with the enzyme remain elusive. This lack of knowledge has prevented the development of curcuminoids into valuable experimental tools or into agents of therapeutic value. We used the crystal structures of SERCA in its E1 conformation in conjunction with computational tools such as docking and surface screens to determine the most likely curcumin binding site, along with key enzyme/inhibitor interactions. Additionally, we determined the inhibitory potencies and binding affinities for a small set of curcumin analogs. The predicted curcumin binding site is a narrow cleft in the transmembrane section of SERCA, close to the transmembrane/cytosol interface. In addition to pronounced complementarity in shape and hydrophobicity profiles between curcumin and the binding pocket, several hydrogen bonds were observed that were spread over the entire curcumin scaffold, involving residues on several transmembrane helices. Docking-predicted interactions were compatible with experimental observations for inhibitory potencies and binding affinities. Based on these findings, we propose an inhibition mechanism that assumes that the presence of a curcuminoid in the binding site arrests the catalytic cycle of SERCA by preventing it from converting from the E1 to the E2 conformation. This blockage of conformational change is accomplished by a combination of steric hinderance and hydrogen-bond-based cross-linking of transmembrane helices that require flexibility throughout the catalytic cycle.

Toward high-resolution modeling of small molecule-ion channel interactions.

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Nickel-chelatase activity of SirB variants mimicking the His arrangement in the naturally occurring nickel-chelatase CfbA.

Metal-tetrapyrrole cofactors are involved in multiple cellular functions, and chelatases are key enzymes for the biosynthesis of these cofactors. CfbA is an ancestral, homodimeric-type class II chelatase which is able to use not only Ni2+ as a physiological metal substrate, but also Co2+ as a nonphysiological substrate with higher activity than for Ni2+. The Ni/Co-chelatase function found in CfbA is also observed in SirB, a descendant, monomeric-type class II chelatase. This is despite the distinct active site structure of CfbA and SirB; specifically, CfbA shows a unique four His residue arrangement, unlike other monomeric class II chelatases such as SirB. Herein, we studied the Ni-chelatase activity of SirB variants R134H, L200H, and R134H/L200H, the latter of which mimics the His alignment of CfbA. Our results showed that the SirB R134H variant exhibited the highest Ni-chelatase activity among the SirB enzymes, which in turn suggests that the position of His134 could be more important for the Ni-chelatase activity than that of His200. The SirB R134H/L200H variant showed lower activity than R134H, despite the four His residues found in SirB R134H/L200H. CD spectroscopy showed secondary structure denaturation and a slight difficulty in Ni-binding of SirB R134H/L200H, which may be related to its lower activity. Finally, a docking simulation suggested that the His134 of the SirB R134H variant could function as a base catalyst for the Ni-chelatase reaction in a class II chelatase architecture.

Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations.

The blood protein von Willebrand factor (VWF) is a large multimeric protein that, when activated, binds to blood platelets, tethering them to the site of vascular injury and initiating blood coagulation. This process is critical for the normal hemostatic response, but especially under inflammatory conditions, it is thought to be a major player in pathological thrombus formation. For this reason, VWF has been the target for the development of anti-thrombotic therapeutics. However, it is challenging to prevent pathological thrombus formation while still allowing normal physiological blood coagulation, as currently available anti-thrombotic therapeutics are known to cause unwanted bleeding, in particular intracranial hemorrhage. This work explores the possibility of inhibiting VWF selectively under the inflammatory conditions present during pathological thrombus formation. In particular, the A2 domain of VWF is known to inhibit the neighboring A1 domain from binding to the platelet surface receptor GpIbα, and this auto-inhibitory mechanism has been shown to be removed by oxidizing agents released during inflammation. Hence, finding drug molecules that bind at the interface between A1 and A2 only under oxidizing conditions could restore such an auto-inhibitory mechanism. Here, by using a combination of computational docking, molecular dynamics simulations, and free energy perturbation calculations, a ligand from the ZINC15 database was identified that binds at the A1A2 interface, with the interaction being stronger under oxidizing conditions. The results provide a framework for the discovery of drug molecules that bind to a protein selectively in the presence of inflammatory conditions.

QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking.

Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

Arthropod repellent interactions with olfactory receptors and ionotropic receptors analyzed by molecular modeling.

The main insect chemoreceptors are olfactory receptors (ORs), gustatory receptors (GRs) and ionotropic receptors (IRs). The odorant binding sites of many insect ORs appear to be occluded and inaccessible from the surface of the receptor protein, based on the three-dimensional structure of OR5 from the jumping bristletail Machilis hrabei (MhraOR5) and a survey of a sample of vinegar fly (Drosophila melanogaster) OR structures obtained from artificial intellegence (A.I.) modeling. Molecular dynamics simulations revealed that the occluded site can become accessible through tunnels that transiently open and close. The present study extends this analysis to examine seventeen ORs and one GR docking with ligands that have known valence: nine that signal attraction and nine that signal aversion. All but one of the receptors displayed occluded ligand binding sites analogous to MhraOR5, and docking software predicted the known attractant and repellent ligands will bind to the occluded sites. Docking of the repellent DEET was examined, and more than half of the OR ligand sites were predicted to bind DEET, including receptors that signal aversion as well as those that signal attraction. However, DEET may not actually have access to all the attractant binding sites. The larger size and lower flexibility of repellent molecules may restrict their passage through the tunnel bottlenecks, which could act as filters to select access to the ligand binding sites. In contrast to ORs and GRs, the IR ligand binding site is in an extracellular domain known to undergo a large conformational change from an open to a closed state. A.I. models of two D. melanogaster IRs of known valence and two blacklegged tick (Ixodes scapularis) IRs having unknown ligands were computationally tested for attractant and repellent binding. The ligand-binding sites in the closed state appear inaccessible to the protein surface, so attractants and repellents must bind initially at an accessible site in the open state before triggering the conformational change. In some IRs, repellent binding sites were identified at exterior sites adjacent to the ligand-binding site. These may be allosteric sites that, when occupied by repellents, can stabilize the open state of an attractant IR, or stabilize the closed state of an IR in the absence of its activating ligand. The model of D. melanogaster IR64a suggests a possible molecular mechanism for the activation of this IR by H+. The amino acids involved in this proposed mechanism are conserved in IR64a from several Dipteran pest species and disease vectors, potentially offering a route to discovery of new repellents that act via the allosteric site.

The Art and Science of Molecular Docking.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Pro197Ser and the new Trp574Leu mutations together with enhanced metabolism contribute to cross-resistance to ALS inhibiting herbicides in Sinapis alba.

White mustard, (Sinapis alba), a problematic broadleaf weed in many Mediterranean countries in arable fields has been detected as resistant to tribenuron-methyl in Tunisia. Greenhouse and laboratory studies were conducted to characterize Target-Site Resistance (TSR) and the Non-Target Site Resistance (NTSR) mechanisms in two suspected white mustard biotypes. Herbicide dose-response experiments confirmed that the two S. alba biotypes were resistant to four dissimilar acetolactate synthase (ALS)-pinhibiting herbicide chemistries indicating the presence of cross-resistance mechanisms. The highest resistance factor (>144) was attributed to tribenuron-methyl herbicide and both R populations survived up to 64-fold the recommended field dose (18.7 g ai ha-1). In this study, the metabolism experiments with malathion (a cytochrome P450 inhibitor) showed that malathion reduced resistance to tribenuron-methyl and imazamox in both populations, indicating that P450 may be involved in the resistance. Sequence analysis of the ALS gene detected target site mutations in the two R biotypes, with amino acid substitutions Trp574Leu, the first report for the species, and Pro197Ser. Molecular docking analysis showed that ALSPro197Ser enzyme cannot properly bind to tribenuron-methyl's aromatic ring due to a reduction in the number of hydrogen bonds, while imazamox can still bind. However, Trp574Leu can weaken the binding affinity between the mutated ALS enzyme and both herbicides with the loss of crucial interactions. This investigation provides substantial evidence for the risk of evolving multiple resistance in S. alba to auxin herbicides while deciphering the TSR and NTSR mechanisms conferring cross resistance to ALS inhibitors.

Similar Binding Modes of cGMP Analogues Limit Selectivity in Modulating Retinal CNG Channels via the Cyclic Nucleotide-Binding Domain.

In treating retinitis pigmentosa, a genetic disorder causing progressive vision loss, selective inhibition of rod cyclic nucleotide-gated (CNG) channels holds promise. Blocking the increased Ca2+-influx in rod photoreceptors through CNG channels can potentially delay disease progression and improve the quality of life for patients. To find inhibitors for rod CNG channels, we investigated the impact of 16 cGMP analogues on both rod and cone CNG channels using the patch-clamp technique. Although modifications at the C8 position of the guanine ring did not change the ligand efficacy, modifications at the N1 and N2 positions rendered cGMP largely ineffective in activating retinal CNG channels. Notably, PET-cGMP displayed selective potential, favoring rod over cone, whereas Rp-cGMPS showed greater efficiency in activating cone over rod CNG channels. Ligand docking and molecular dynamics simulations on cyclic nucleotide-binding domains showed comparable binding energies and binding modes for cGMP and its analogues in both rod and cone CNG channels (CNGA1 vs CNGA3 subunits). Computational experiments on CNGB1a vs CNGB3 subunits showed similar binding modes albeit with fewer amino acid interactions with cGMP due to an inactivated conformation of their C-helix. In addition, no clear correlation could be observed between the computational scores and the CNG channel efficacy values, suggesting additional factors beyond binding strength determining ligand selectivity and potency. This study highlights the importance of looking beyond the cyclic nucleotide-binding domain and toward the gating mechanism when searching for selective modulators. Future efforts in developing selective modulators for CNG channels should prioritize targeting alternative channel domains.

Network pharmacology, molecular simulation, and binding free energy calculation-based investigation of Neosetophomone B revealed key targets for the treatment of cancer.

In the current study, Neosetophomone B (NSP-B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues' flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of -36.85 kcal/mol for the FGF6-NSP-B complex, -43.87 kcal/mol for the FGF20-NSP-B complex, and -37.42 kcal/mol for the FGF22-NSP-B complex, and -41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of -30.05 kcal/mol for the FGF6-NSP-B complex, -39.62 kcal/mol for the FGF20-NSP-B complex, -34.89 kcal/mol for the FGF22-NSP-B complex, and -37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy.

Synthesis of R-GABA Derivatives via Pd(II) Catalyzed Enantioselective C(sp3)-H Arylation and Virtual Validation with GABAB1 Receptor for Potential leads.

GABA (γ-amino butyric acid) analogues like baclofen, tolibut, phenibut, etc., are well-known GABAB1 inhibitors and pharmaceutically important drugs. However, there is a huge demand for more chiral GABA aryl analogues with promising pharmacological actions. Here, we demonstrate the chiral ligand acetyl-protected amino quinoline (APAQ) mediated enantioselective synthesis of GABAB1 inhibitor drug scaffolds from easily accessible GABA via Pd-catalyzed C(sp3)-H activation. The synthetic methodology shows moderate to good yields, up to 74% of ee. We have successfully demonstrated the deprotection and removal of the directing group to synthesize R-tolibut in 86% yield. Further, we employed computation to probe the binding of R-GABA analogues to the extracellular domain of the human GABAB1 receptor. Our Rosetta-based molecular docking calculations show better binding for four R-enantiomers of GABA analogues than R-baclofen and R-phenibut. In addition, we employed GROMACS MD simulations and MMPB(GB)SA calculations to identify per-residue contribution to binding free energy. Our computational results suggest analogues (3R)-4-amino-3-(3,4-dimethylphenyl) butanoic acid, (3R)-4-amino-3-(3-fluorophenyl) butanoic acid, (3R)-3-(4-acetylphenyl)-4-aminobutanoic acid, (3R)-4-amino-3-(4-methoxyphenyl) butanoic acid, and (3R)-4-amino-3-phenylbutanoic acid are potential leads which could be synthesized from our methodology reported here.

Genome sequencing and protein modeling unraveled the 2AP biosynthesis in Bacillus cereus DB25.

2-Acetyl-1-pyrroline (2AP) is an important and major flavor aroma compound responsible for the fragrance of basmati rice, cheese, wine, and several other food products. Biosynthesis of 2AP in aromatic rice and a few other plant species is associated with a recessive Betaine aldehyde dehydrogenase 2 (BADH2) gene. However, the literature is scant on the relationship between the functional BADH2 gene and 2AP biosynthesis in prokaryotic systems. Therefore, in the present study, we aimed to explore the functionality of the BADH2 gene for 2AP biosynthesis in 2AP synthesizing rice rhizobacterial isolate Bacillus cereus DB25 isolated from the rhizosphere of basmati rice (Oryza sativa L.). Full-length BcBADH2 sequence was obtained through whole genome sequencing (WGS) and further confirmed through traditional PCR and Sanger sequencing. Then the functionality of the BcBADH2 gene was evaluated in-silico through bioinformatics analysis and protein docking studies and further experimentally validated through enzyme assay. The sequencing and bioinformatics analysis results revealed a full-length 1485 bp BcBADH2 coding sequence without any deletion or premature stop codons. Full-length BcBADH2 was found to encode a fully functional protein of 54.08 kDa with pI of 5.22 and showed the presence of the conserved amino acids responsible for enzyme activity. The docking studies confirmed a good affinity between the protein and its substrate whereas the presence of BcBADH2 enzyme activity confirmed the functionality of BADH2 enzyme in B. cereus DB25. In conclusion, the findings of the present study suggest that B. cereus DB25 is able to synthesize 2AP despite a functional BADH2 gene and there may be a different molecular mechanism responsible for 2AP biosynthesis in bacterial systems, unlike that found in aromatic rice and other eukaryotic plant species.

Harnessing deep learning for enhanced ligand docking.

Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations.

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Evolution and functional role prediction of the CYP6DE and CYP6DJ subfamilies in Dendroctonus (Curculionidae: Scolytinae) bark beetles.

Dendroctonus-bark beetles are natural components and key ecological agents of coniferous forests. They spend most of their lives under the bark, where they are exposed to highly toxic terpenes present in the oleoresin. Cytochrome P450 (CYP) is a multigene family involved in the detoxification of these compounds. It has been demonstrated that CYP6DE and CYP6DJ subfamilies hydroxylate monoterpenes, whose derivatives can act as pheromone synergist compounds or be pheromones themselves in these insects. Given the diversity and functional role of CYPs, we investigated whether these cytochromes have retained their function throughout the evolution of these insects. To test this hypothesis, we performed a Bayesian phylogenetic analysis to determine phylogenetic subgroups of cytochromes in these subfamilies. Subgroups were mapped and reconciled with the Dendroctonus phylogeny. Molecular docking analyses were performed with the cytochromes of each subgroup and enantiomers of α-pinene and ß-pinene, (+)-3-carene, ß-myrcene and R-(+)-limonene. In addition, functional divergence analysis was performed to identify critical amino acid sites that influence changes in catalytic site conformation and/or protein folding. Three and two phylogenetic subgroups were recovered for the CYP6DE and CYP6DJ subfamilies, respectively. Mapping and reconciliation analysis showed different gain and loss patterns for cytochromes of each subgroup. Functional predictions indicated that the cytochromes analyzed are able to hydroxylate all monoterpenes; however, they showed preferential affinities to different monoterpenes. Functional divergence analyses indicated that the CYP6DE subfamily has experimented type I and II divergence, whereas the CYP6DJ subfamily has evolved under strong functional constraints. Results suggest cytochromes of the CYP6DE subfamily evolve to reinforce their detoxifying capacity hydroxylating mainly α- and ß-pinene to (+) and (-)-trans-verbenol, being the negative enantiomer used as a pheromone by several Dendroctonus species; whereas cytochromes of the CYP6DJ subfamily appear to retain their original function related to the detoxification of these compounds.