Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Computational Docking as a Tool in Guiding the Drug Design of Rutaecarpine Derivatives as Potential SARS-CoV-2 Inhibitors.

COVID-19 continues to spread around the world. This is mainly because new variants of the SARS-CoV-2 virus emerge due to genomic mutations, evade the immune system and result in the effectiveness of current therapeutics being reduced. We previously established a series of detection platforms, comprising computational docking analysis, S-protein-based ELISA, pseudovirus entry, and 3CL protease activity assays, which allow us to screen a large library of phytochemicals from natural products and to determine their potential in blocking the entry of SARS-CoV-2. In this new screen, rutaecarpine (an alkaloid from Evodia rutaecarpa) was identified as exhibiting anti-SARS-CoV-2 activity. Therefore, we conducted multiple rounds of structure-activity-relationship (SAR) studies around this phytochemical and generated several rutaecarpine analogs that were subjected to in vitro evaluations. Among these derivatives, RU-75 and RU-184 displayed remarkable inhibitory activity when tested in the 3CL protease assay, S-protein-based ELISA, and pseudovirus entry assay (for both wild-type and omicron variants), and they attenuated the inflammatory response induced by SARS-CoV-2. Interestingly, RU-75 and RU-184 both appeared to be more potent than rutaecarpine itself, and this suggests that they might be considered as lead candidates for future pharmacological elaboration.

Use of phosphotyrosine-containing peptides to target SH2 domains: Antagonist peptides of the Crk/CrkL-p130Cas axis.

Protein-protein interactions between SH2 domains and segments of proteins that include a post-translationally phosphorylated tyrosine residue (pY) underpin numerous signal transduction cascades that allow cells to respond to their environment. Dysregulation of the writing, erasing, and reading of these posttranslational modifications is a hallmark of human disease, notably cancer. Elucidating the precise role of the SH2 domain-containing adaptor proteins Crk and CrkL in tumor cell migration and invasion is challenging because there are no specific and potent antagonists available. Crk and CrkL SH2s interact with a region of the docking protein p130Cas containing 15 potential pY-containing tetrapeptide motifs. This chapter summarizes recent efforts toward peptide antagonists for this Crk/CrkL-p130Cas interaction. We describe our protocol for recombinant expression and purification of Crk and CrkL SH2s for functional assays and our procedure to determine the consensus binding motif from the p130Cas sequence. To develop a more potent antagonist, we employ methods often associated with structure-based drug design. Computational docking using Rosetta FlexPepDock, which accounts for peptides having a greater number of conformational degrees of freedom than small organic molecules that typically constitute libraries, provides quantitative docking metrics to prioritize candidate peptides for experimental testing. A battery of biophysical assays, including fluorescence polarization, differential scanning fluorimetry and saturation transfer difference nuclear magnetic resonance spectroscopy, were employed to assess the candidates. In parallel, GST pulldown competition assays characterized protein-protein binding in vitro. Taken together, our methodology yields peptide antagonists of the Crk/CrkL-p130Cas axis that will be used to validate targets, assess druggability, foster in vitro assay development, and potentially serve as lead compounds for therapeutic intervention.

Synthesis and Evaluation of 5-(Heteroarylmethylene)hydantoins as Glycogen Synthase Kinase-3ß Inhibitors.

Glycogen synthase kinase-3 (GSK-3) is a serine/threonine kinase which plays a center role in the phosphorylation of a wide variety of proteins, generally leading to their inactivation. As such, GSK-3 is viewed as a therapeutic target. An ever-increasing number of small organic molecule inhibitors of GSK-3 have been reported. Phenylmethylene hydantoins are known to exhibit a wide range of inhibitory activities including for GSK-3ß. A family of fourteen 2-heterocycle substituted methylene hydantoins (14, 17-29) were prepared and evaluated for the inhibition of GSK-3ß at 25 µM. The IC50 values of five of these compounds was determined; the two best inhibitors are 5-[(4'-chloro-2-pyridinyl)methylene]hydantoin (IC50 = 2.14 ± 0.18 µM) and 5-[(6'-bromo-2-pyridinyl)methylene]hydantoin (IC50 = 3.39 ± 0.16 µM). The computational docking of the compounds with GSK-3ß (pdb 1q41) revealed poses with hydrogen bonding to the backbone at Val135. The 5-[(heteroaryl)methylene]hydantoins did not strongly inhibit other metalloenzymes, demonstrating poor inhibitory activity against matrix metalloproteinase-12 at 25 µM and against human carbonic anhydrase at 200 µM, and were not inhibitors for Staphylococcus aureus pyruvate carboxylase at concentrations >1000 µM.

Assessment of fragment docking and scoring with the endothiapepsin model system.

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Structure-Affinity relationships of novel σ2R/TMEM97 ligands.

The sigma 2 receptor (σ2R), which was recently identified as the transmembrane protein 97 (TMEM97), is increasingly attracting interest as a possible therapeutic target for indications in neuroscience. Toward identifying novel modulators of σ2R/TMEM97, we prepared a collection of benzoxazocine, benzomorphan, and methanobenzazepine ligands related to the known bioactive norbenzomorphans DKR-1677, FEM-1689, and EES-1686 and determined their Ki values for σ2R/TMEM97 and the sigma 1 receptor (σ1R). The σ2R/TMEM97 binding affinities and selectivities relative to σ1R of these new benzoxazocine, benzomorphan, and methanobenzazepine analogs are lower, often significantly lower, than their respective norbenzomorphan counterparts, suggesting the spatial orientation of pharmacophoric substituents is critical for binding to the two proteins. The benzoxazocine, benzomorphan, and methanobenzazepine congeners of DKR-1677 and FEM-1689 tend to be weakly selective for σ2R/TMEM97 versus σ1R, whereas EES-1686 derivatives exhibit the greatest selectivity, suggesting the size and/or nature of the substituent on the nitrogen atom of the scaffold may be important for selectivity. Computational docking studies were performed for the 1S,5R-and 1R,5S-enantiomers of DKR-1677, FEM-1689, and EES-1686 and their benzoxazocine, benzomorphan, and methanobenzazepine counterparts. These computations predict that the protonated amino group of each ligand forms a highly conserved salt bridge and a H-bonding interaction with Asp29 as well as a cation-π interaction with Tyr150 of σ2R/TMEM97. These electrostatic interactions are major driving forces for binding to σ2R/TMEM97 and are similar, though not identical, for each ligand. Other interactions within the well-defined binding pocket also tend to be comparable, but there are some major differences in how the hydrophobic aryl groups of various ligands interact with the protein surface external to the binding pocket. Overall, these studies show that the orientations of aryl and N-substituents on the norbenzomorphan and related scaffolds are important determinants of binding affinity of σ2R/TMEM97 ligands, and small changes can have significant effects upon binding profiles.

Actinobacterial peroxidase-mediated biodeterioration of hazardous explosive, 2, 4, 6, trinitrophenol by in silico and in vitro approaches.

Explosives are perilous and noxious to aquatic biota disrupting their endocrinal systems. Supplementarily, they exhibit carcinogenic, teratogenic and mutagenic effects on humans and animals. Henceforth, the current study has been targeted to biotransform the explosive, 2, 4, 6 trinitrophenol (TNP) by wetland peroxidase from Streptomyces coelicolor. A total peroxidase yield of 20,779 mg/l with 51.6 folds of purification was observed. In silico molecular docking cum in vitro appraisals were accomplished to assess binding energy and interacting binding site residues of peroxidase and TNP complex. TNP required a minimal binding energy of-6.91 kJ/mol and was subjected to biodeterioration (89.73%) by peroxidase in purified form, with 45 kDa and a similarity score of 34 by MASCOT protein analysis. Moreover, the peroxidase activity was confirmed with Zymogram analysis. Characterization of peroxidase revealed that optimum values of pH and temperature as 6 and 40 °C, respectively, with their corresponding stability varying from 3.5 to 7. Interestingly, the kinetic parameters such as Km and Vmax on 2,2'-azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and H2O2 were 19.27 µm and 0.41 µm/min; 21.4 µm and 0.1 µm/min, respectively. Among the diverse substrates, chemicals and trace elements, ABTS (40 mM), citric acid (5 mM) and Fe2+ (5 mM) displayed the highest peroxidase activity. Computational docking and in vitro results were corroborative and UV-Vis spectroscopy, HPLC, FTIR and GC-MS indicated the presence of simple metabolites of TNP such as nitrophenols and benzoquinone, showcasing the efficacy of S. coelicolor peroxidase to biotransform TNP. Henceforth, the current study offers a promising channel for biological treatment of explosive munitions, establishing a sustainable green earth.

Comparison of interactions between alpha-lactalbumin and three protopanaxadiol ginsenosides: Impacts on the structure and antitumor properties.

In this research, interactions between α-lactalbumin (ALA) and three protopanaxadiol ginsenosides [20(S)-Rg3, 20(S)-Rh2, and 20(S)-PPD] were compared to explore the effects of similar ligand on structure and cytotoxicity of ALA. Multi-spectroscopy revealed the binding between ALA and ginsenoside changed the conformation of ALA, which related to different structures and solubility of ligands. Scanning electron microscope illustrated that all ALA-ginsenoside complexes exhibited denser structures via hydrophobic interactions. Additionally, the cytotoxic experiments confirmed that the cytotoxicity of ginsenoside was enhanced after binding with ALA. Molecular docking showed all three ginsenosides were bound to the sulcus depression region of ALA via hydrogen bonding and hydrophobic interaction. Furthermore, molecular dynamics simulation elucidated the precise binding sites and pertinent system properties. Among all three composite systems, 20(S)-Rh2 had optimal binding affinity. These findings enhanced understanding of the synergistic utilization of ALA and ginsenosides as functional ingredients in food, medicine, and cosmetics.

The enhancement mechanisms of mucin and lactoferrin on α-amylase activity in saliva: Exploring the interactions using QCM-D and molecular docking.

α-Amylase activity differs between individuals and is influenced by dietary behavior and salivary constituents, but limited information is available on the relationship between α-amylase activity and saliva components. This study investigated the impact of salivary proteins on α-amylase activity, their various correlations, the effect of mucin (MUC5B and MUC7) and lactoferrin on the enzymatic kinetics of α-amylase, and the mechanisms of these interactions using the quartz crystal microbalance with dissipation (QCM-D) technique and molecular docking. The results showed that α-amylase activity was significantly correlated with the concentrations of MUC5B (R2 = 0.42, p < 0.05), MUC7 (R2 = 0.35, p < 0.05), and lactoferrin (R2 = 0.35, p < 0.05). An in vitro study demonstrated that α-amylase activity could be significantly increased by mucins and lactoferrin by decreasing the Michaelis constant (Km) of α-amylase. Moreover, the results from the QCM-D and molecule docking suggested that mucin and lactoferrin could interact with α-amylase to form stable α-amylase-mucin and α-amylase-lactoferrin complexes through hydrophobic interactions, electrostatic interactions, Van der Waals forces, and hydrogen bonds. In conclusion, these findings indicated that the salivary α-amylase activity depended not only on the α-amylase content, but also could be enhanced by the interactions of mucin/lactoferrin with α-amylase.

Structure-affinity relationships of stereoisomers of norbenzomorphan-derived σ2R/TMEM97 modulators.

The sigma 2 receptor (σ2R), which is identical to transmembrane protein 97 (TMEM97), is attracting increasing interest as a possible therapeutic target for various indications in neuroscience. In continuation of a program to identify novel compounds that bind with high affinity and selectivity to σ2R/TMEM97, we performed structure-affinity-relationship (SAfiR) studies of several sets of σ2R/TMEM97 ligands having a B-norbenzomorphan ring core. Binding data for σ2R/TMEM97 and σ1R of several enantiomeric pairs of piperazine-substituted norbenzomorphans show the (1S,5R)-enantiomers have affinities (Ki = 9-75 nM) for σ2R/TMEM97 that are 2-3-fold higher than their enantiomorphic (1R,5S)-analogs; however, there is no clear trend for selectivity for σ2R/TMEM97 vs σ1R. A series of N-alkyl piperazino (1S,5R)-norbenzomorphans was then evaluated, and with the exception of compounds having N-alkyl groups substituted with oxygen or amino groups at C (2) of an ethylene chain, Ki values for σ2R/TMEM97 are less than 25 nM, and several compounds have good selectivities (ca 7-16-fold) for σ2R/TMEM97 vs σ1R. Mono-substituted carbobenzyloxy analogs have Ki values for σ2R/TMEM97 comparable to the unsubstituted parent (Ki = ca 7-27 nM), but replacing the N-acyloxy group with N-acyl or N-arylsulfonyl groups provides analogs having lower affinity and selectivity. Some congeners with bioisosteric replacements of the piperazine group on the (1S,5R)-norbenzomorphan core have high affinity (Ki = <30 nM) for σ2R/TMEM97, but selectivities are modest. Computational docking studies for racemic pairs of piperazino norbenzomorphans show that individual (1S,5R)- and (1R,5S)-enantiomers adopt distinct poses upon binding to σ2R/TMEM97, whereas ligands belongingto the same enantiomeric series adopt closely similar binding poses. The protonated amino group in each of the enantiomorphic ligands engages in highly conserved salt bridges with Asp29 and cation-π interactions with Tyr150 that are the primary determinants of binding affinity. There is no correlation between any of the computational parameter outputs and Ki values, but this is unsurprising given the small energetic differences involved. Modeling also suggest sthat some compounds can extend deeper into σ2R/TMEM97 binding pocket forming salt bridges with Glu73.

Computational docking investigation of phytocompounds from bergamot essential oil against Serratia marcescens protease and FabI: Alternative pharmacological strategy.

The rapid development of multi-drug resistant (MDR) pathogens adds urgency to search for novel and safe drugs having promising action on new and re-emerging infectious pathogens. Serratia marcescens is an MDR pathogen that causes several-healthcare associated infections. Curbing bacterial virulence, rather than inhibiting its growth, is a promising strategy to diminish the pathogenesis of infectious bacteria, reduce the development of antimicrobial resistance, and boost the host immune power to eradicate infections. Bergamot essential oil (BEO) is a remarkable source of promising therapeutics against pathogens. Therefore, the present investigation aimed to analyze the major phytocompounds from BEO against S. marcescens virulent proteins using in silico studies. The analysis of BEO phytocompounds was achieved by Gas chromatography-mass spectrometry (GC-MS) method. The molecular docking was carried out using the SP and XP docking protocol of the Glide program. The drug-likeness and pharmacokinetics properties (ADMET properties) were analyzed with SwissADME and pkCSM server. The results revealed that the major compounds present in BEO are Linalool (8.17%), D-Limonene (21.26%), and Linalyl acetate (26.91%). Molecular docking analysis revealed that these compounds docked strongly within the binding cavities of Serratia protease and FabI model which in turn curb the pathogenesis of this bacteria. Linalool interacted with the Serratia protease and FabI with a binding energy of - 3.130 kcal/mol and - 3.939 kcal/mol, respectively. Based on the pharmacokinetics findings all lead BEO phytocompounds appear to be promising drug candidates. Overall, these results represent a significant step in the development of plant-based compounds as a promising inhibitor of the virulent proteins of the MDR S. marcescens.

Elucidating the inhibitory effects of rationally designed novel hexapeptide against hen egg white lysozyme fibrillation at acidic and physiological pH.

Inhibition of highly ordered cross-ß-sheet-rich aggregates of misfolded amyloid proteins using rationally designed sequence-based short peptides is a promising therapeutic strategy for the treatment of neurodegenerative diseases. Here, we have explored the anti-amyloidogenic potency of a rationally designed hexapeptide (Tyr-Pro-Gln-Ile-Pro-Asn) on in vitro hen egg white lysozyme (HEWL) amyloid fibril formation at acidic pH and physiological pH using computational docking as well as various biophysical techniques such as fluorescence spectroscopy, UV-vis spectroscopy, FTIR spectroscopy, confocal microscopy and TEM. The peptide was designed based on the aggregation-prone region (APR) of HEWL and thus referred to as SqP1 (Sequence-based Peptide 1). SqP1 showed over 70% inhibition of HEWL amyloid formation at pH 2.2 and approximately 50% inhibition at pH 7.5. We propose that SqP1 binds to the APR of HEWL and interacts strongly with the Trp62/Trp63, ultimately stabilizing monomeric HEWL at both the pH conditions and preventing conformation changes in the structure of HEWL, leading to the formation of amyloidogenic fibrillar structures. A sequence-based peptide inhibitor of HEWL amyloid formation was not reported previously, making this a critical study that will further emphasize the importance of short synthetic peptides as amyloid inhibitors.

Synthesis and biological evaluation of novel N-benzyltriazolyl-hydroxamate derivatives as selective histone deacetylase 6 inhibitors.

Histone deacetylases (HDAC) regulate post-translational acetylation and the inhibition of these enzymes has emerged as an intriguing disease therapeutic. Among them, class IIb HDAC6 has the unique characteristic of mainly deacetylating cytoplasmic proteins, suggesting clinical applications for neurodegenerative diseases, inflammation, and cancer. In this study, we designed a novel N-benzyltriazolyl-hydroxamate scaffold based on the known HDAC6 inhibitors nexturastat A and tubastatin A. Among the 27 derivatives, 3-fluoro-4-((3-(2-fluorophenyl)-1H-1,2,4-triazol-1-yl)methyl)-N-hydroxybenzamide 4u (HDAC6 IC50 = 7.08 nM) showed nanomolar HDAC6 inhibitory activity with 42-fold selectivity over HDAC1. Structure-activity relationship (SAR) and computational docking studies were conducted to optimize the triazole capping group. Docking analysis revealed that the capping group aligned with the conserved L1 pocket of HDAC6 and was associated with subtype selectivity. Overall, our study explored the triazole-based biaryl capping group and its substitution and orientation, suggesting a rationale for the design of HDAC6-selective inhibitors.

Bioprospecting of novel peroxidase from Streptomyces coelicolor strain SPR7 for carcinogenic azo dyes decolorization.

Peroxidase (POX) is a heme-containing oxidoreductase, its voluminous immuno-diagnostic and bioremediatory intuitions have incited optimization and large scale-generation from novel microbial repertoires. Azo dyes are the most detrimental classes of synthetic dyes and they are the common ecotoxic industrial pollutants in wastewater. In addition, azo dyes are refractory to degradation owing to their chemical nature, comprising of azoic linkages, amino moieties with recalcitrant traits. Moreover, they are major carcinogenic and mutagenic on humans and animals, whereby emphasizing the need for decolorization. In the present study, a novel POX from Streptomyces coelicolor strain SPR7 was investigated for the deterioration of ecotoxic dyestuffs. The initial medium component screening for POX production was achieved using, One Factor at a Time and Placket-Burman methodologies with starch, casein and temperature as essential parameters. In auxiliary, Response Surface Methodology (RSM) was recruited and followed by model validation using Back propagation algorithm (BPA). RSM-BPA composite approach prophesied that combination of starch, casein, and temperature at optimal values 2.5%, 0.035% and 35 °C respectively, has resulted in 7 folds enhancement of POX outturn (2.52 U/mL) compared to the unoptimized media (0.36 U/mL). The concentrated enzyme decolorized 75.4% and 90% of the two azo dyes with lignin (10 mM), respectively. Hence, this investigation confirms the potentiality of mangrove actinomycete derived POX for elimination of noxious azo dyes to overcome their carcinogenic, mutagenic and teratogenic effects on humans and aquatic organisms.

High throughput virtual screening and molecular dynamics simulation analysis of phytomolecules against BfmR of Acinetobacter baumannii: anti-virulent drug development campaign.

Acinetobacter baumannii is a notorious multidrug resistant bacterium responsible for several hospital acquired infections assisted by its capacity to develop biofilms. A. baumannii BfmR (RstA), a response regulator from the BfmR/S two-component signal transduction system, is the major controller of A. baumannii biofilm development and formation. As a result, BfmR represents a novel target for anti-biofilm treatment against A. baumannii. The discovery of the high-resolution crystal structure of BfmR provides a good chance for computational screening of its probable inhibitors. Therefore, in this study we aim to search new, less toxic, and natural BfmR inhibitors from 8450 phytomolecules available in the Indian Medicinal Plants, Phytochemistry and Therapeutic (IMPPAT) database by analyzing molecular docking against BfmR (PDB ID: 6BR7). Out of these 8450 phytomolecules 6742 molecules were successfully docked with BfmR with the docking score range -6.305 kcal/mol to +5.120 kcal/mol. Structure based-molecular docking (SB-MD) and ADMET (absorption, distribution, metabolism, excretion, & toxicity) profile examination revealed that Norepinephrine, Australine, Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline phytocompounds strongly binds to the active site residues of BfmR. Furthermore, molecular dynamics simulation (MDS) studies for 100 ns and the binding free energy (MM/GBSA) analysis elucidated the binding mechanism of Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline to BfmR. In summary, these phytocompounds seems to have the promising molecules against BfmR, and thus necessitates further verification by both in vitro and in vivo experiments. HighlightsBfmR plays a key role in biofilm development and exopolysaccharide (EPS) synthesis in A. baumannii.Computational approach to search for promising BfmR inhibitors from IMPAAT database.The lead phytomolecules such as Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline displayed significant binding with BfmR active site.The outcome of BfmR binding phytomolecules has broadened the scope of hit molecules validation.Communicated by Ramaswamy H. Sarma.

Identifying medically relevant xenon protein targets by in silico screening of the structural proteome.

In a previous study, in silico screening of the binding of almost all proteins in the Protein Data Bank to each of the five noble gases xenon, krypton, argon, neon, and helium was reported. This massive and rich data set requires analysis to identify the gas-protein interactions that have the best binding strengths, those where the binding of the noble gas occurs at a site that can modulate the function of the protein, and where this modulation might generate clinically relevant effects. Here, we report a preliminary analysis of this data set using a rational, heuristic score based on binding strength and location. We report a partial prioritized list of xenon protein targets and describe how these data can be analyzed, using arginase and carbonic anhydrase as examples. Our aim is to make the scientific community aware of this massive, rich data set and how it can be analyzed to accelerate future discoveries of xenon-induced biological activity and, ultimately, the development of new "atomic" drugs.

Evaluation of AlphaFold2 structures as docking targets.

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.

X-ray structure and mechanism of ZgHAD, a l-2-haloacid dehalogenase from the marine Flavobacterium Zobellia galactanivorans.

Haloacid dehalogenases are potentially involved in bioremediation of contaminated environments and few have been biochemically characterized from marine organisms. The l-2-haloacid dehalogenase (l-2-HAD) from the marine Bacteroidetes Zobellia galactanivorans DsijT (ZgHAD) has been shown to catalyze the dehalogenation of C2 and C3 short-chain l-2-haloalkanoic acids. To better understand its catalytic properties, its enzymatic stability, active site, and 3D structure were analyzed. ZgHAD demonstrates high stability to solvents and a conserved catalytic activity when heated up to 60°C, its melting temperature being at 65°C. The X-ray structure of the recombinant enzyme was solved by molecular replacement. The enzyme folds as a homodimer and its active site is very similar to DehRhb, the other known l-2-HAD from a marine Rhodobacteraceae. Marked differences are present in the putative substrate entrance sites of the two enzymes. The H179 amino acid potentially involved in the activation of a catalytic water molecule was confirmed as catalytic amino acid through the production of two inactive site-directed mutants. The crystal packing of 13 dimers in the asymmetric unit of an active-site mutant, ZgHAD-H179N, reveals domain movements of the monomeric subunits relative to each other. The involvement of a catalytic His/Glu dyad and substrate binding amino acids was further confirmed by computational docking. All together our results give new insights into the catalytic mechanism of the group of marine l-2-HAD.

Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates.

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.