Adaptive simulations, towards interactive protein-ligand modeling

Evolutionary history and activity towards oligosaccharides and polysaccharides of GH3 glycosidases from an Antarctic marine bacterium

Glycoside hydrolases (GHs) are pivotal in the hydrolysis of the glycosidic bonds of sugars, which are the main carbon and energy sources. The genome of Marinomonas sp. ef1, an Antarctic bacterium, contains three GHs belonging to family 3. These enzymes have distinct architectures and low sequence identity, suggesting that they originated from separate horizontal gene transfer events. M-GH3_A and M-GH3_B, were found to differ in cold adaptation and substrate specificity. M-GH3_A is a bona fide cold-active enzyme since it retains 20 % activity at 10 °C and exhibits poor long-term thermal stability. On the other hand, M-GH3_B shows mesophilic traits with very low activity at 10 °C (< 5 %) and higher long-term thermal stability. Substrate specificity assays highlight that M-GH3_A is a promiscuous β-glucosidase mainly active on cellobiose and cellotetraose, whereas M-GH3_B is a β-xylosidase active on xylan and arabinoxylan. Structural analysis suggests that such functional differences are due to their differently shaped active sites. The active site of M-GH3_A is wider but has a narrower entrance compared to that of M-GH3_B. Genome-based prediction of metabolic pathways suggests that Marinomonas sp. ef1 can use monosaccharides derived from the GH3-catalyzed hydrolysis of oligosaccharides either as a carbon source or for producing osmolytes.

Structure and mechanisms of transport of human Asc1/CD98hc amino acid transporter

Recent cryoEM studies elucidated details of the structural basis for the substrate selectivity and translocation of heteromeric amino acid transporters. However, Asc1/CD98hc is the only neutral heteromeric amino acid transporter that can function through facilitated diffusion, and the only one that efficiently transports glycine and D-serine, and thus has a regulatory role in the central nervous system. Here we use cryoEM, ligand-binding simulations, mutagenesis, transport assays, and molecular dynamics to define human Asc1/CD98hc determinants for substrate specificity and gain insights into the mechanisms that govern substrate translocation by exchange and facilitated diffusion. The cryoEM structure of Asc1/CD98hc is determined at 3.4-3.8 Å resolution, revealing an inward-facing semi-occluded conformation. We find that Ser 246 and Tyr 333 are essential for Asc1/CD98hc substrate selectivity and for the exchange and facilitated diffusion modes of transport. Taken together, these results reveal the structural bases for ligand binding and transport features specific to human Asc1.

Molecular docking, molecular dynamics simulations and binding free energy studies of interactions between Mycobacterium tuberculosis Pks13, PknG and bioactive constituents of extremophilic bacteria

Mycobacterial pathogens present a significant challenge to disease control efforts globally due to their inherent resistance to multiple antibiotics. The rise of drug-resistant strains of Mycobacterium tuberculosis has prompted an urgent need for innovative therapeutic solutions. One promising way to discover new tuberculosis drugs is by utilizing natural products from the vast biochemical space. Multidisciplinary methods can used to harness the bioactivity of these natural products. This study aimed to evaluate the antimycobacterial efficacy of functional crude extracts from bacteria isolated from gold mine tailings in South Africa. Bacterial strains were identified using 16S rRNA sequencing. The crude extracts obtained from the bacteria were tested against Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. Untargeted HPLC-qTOF and molecular networking were used to identify the functional constituents present in extracts that exhibited inhibitory activity. A virtual screening workflow (VSW) was used to filter compounds that were strong binders to Mycobacterium tuberculosis Pks13 and PknG. The ligands returned from the VSW were subjected to optimization using density functional theory (DFT) at M06-2X/6-311++ (d,p) level of theory and basis set implemented in Gaussian16 Rev.C01. The optimized ligands were re-docked against Mycobacterium tuberculosis Pks13 and PknG. Molecular dynamics simulation and molecular mechanics generalized born surface area were used to evaluate the stability of the protein-ligand complexes formed by the identified hits. The hit that showed promising binding characteristics was virtually modified through multiple synthetic routes using reaction-driven enumeration. Three bacterial isolates showed significant activity against the two strains of Mycobacterium, while only two, Bacillus subtilis and Bacillus licheniformis, exhibited activity against both Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. The tentatively identified compounds from the bacterial crude extracts belonged to various classes of natural compounds associated with antimicrobial activity. Two compounds, cyclo-(L-Pro-4-OH-L-Leu) and vazabitide A, showed strong binding against PknG and Pks13, with pre-MD MM-GBSA values of - 42.8 kcal/mol and - 47.6 kcal/mol, respectively. The DFT-optimized compounds exhibited the same docking scores as the ligands optimized using the OPSL-4 force field. After modifying vazabitide A, its affinity to the Pks13 binding site increased to - 85.8 kcal/mol, as revealed by the post-MD MM-GBSA analysis. This study highlights the potential of bacteria isolates from gold mine tailings as a source of new scaffolds for designing and optimizing anti-Mycobacterium agents. These agents synthesized in-silico can be further tested in-vitro to evaluate their efficacy.

Profile of a Multivariate Observation under Destructive Sampling-A Monte Carlo Approach to a Case of Spina Bifida

A biodegradable hybrid polymer patch was invented at the University of Cincinnati to cover gaps on the skin over the spinal column of a growing fetus, characterized by the medical condition spina bifida. The inserted patch faces amniotic fluid (AF) on one side and cerebrospinal fluid on the other side. The goal is to provide a profile of the roughness of a patch over time at 0, 4, 8, 12, and 16 weeks with a 95% confidence band. The patch is soaked in a test tube filled with either amniotic fluid (AF) or phosphate-buffered saline (PBS) in the lab. If roughness is measured at any time point for a patch, the patch is destroyed. Thus, it is impossible to measure roughness at all weeks of interest for any patch. It is important to assess the roughness of a patch because the rougher the patch is, the faster the skin grows under the patch. We use a model-based approach with Monte Carlo simulations to estimate the profile over time with a 95% confidence band. The roughness profiles are similar with both liquids. The profile can be used as a template for future experiments on the composition of patches.

Enhancing the Hydrolytic Activity of a Lipase towards Larger Triglycerides through Lid Domain Engineering

Lipases have valuable potential for industrial use, particularly those mostly active against water-insoluble substrates, such as triglycerides composed of long-carbon chain fatty acids. However, in most cases, engineered variants often need to be constructed to achieve optimal performance for such substrates. Protein engineering techniques have been reported as strategies for improving lipase characteristics by introducing specific mutations in the cap domain of esterases or in the lid domain of lipases or through lid domain swapping. Here, we improved the lipase activity of a lipase (WP_075743487.1, or LipMRD) retrieved from the Marine Metagenomics MarRef Database and assigned to the Actinoalloteichus genus. The improvement was achieved through site-directed mutagenesis and by substituting its lid domain (FRGTEITQIKDWLTDA) with that of Rhizopus delemar lipase (previously R. oryzae; UniProt accession number, I1BGQ3) (FRGTNSFRSAITDIVF). The results demonstrated that the redesigned mutants gain activity against bulkier triglycerides, such as glyceryl tridecanoate and tridodecanoate, olive oil, coconut oil, and palm oil. Residue W89 (LipMRD numbering) appears to be key to the increase in lipase activity, an increase that was also achieved with lid swapping. This study reinforces the importance of the lid domains and their amino acid compositions in determining the substrate specificity of lipases, but the generalization of the lid domain swapping between lipases or the introduction of specific mutations in the lid domain to improve lipase activity may require further investigation.

Selective inhibitors of the PSEN1-gamma-secretase complex

Clinical development of γ-secretases, a family of intramembrane cleaving proteases, as therapeutic targets for a variety of disorders including cancer and Alzheimer's disease, was aborted because of serious mechanism based side effects in phase III trials of unselective inhibitors. Selective inhibition of specific γ-secretase complexes, containing either PSEN1 or PSEN2 as catalytic subunit and APH1A or APH1B as supporting subunits, do provide a feasible therapeutic window in preclinical models of these disorders. We explore here the pharmacophoric features required for PSEN1 versus PSEN2 selective inhibition. We synthesized a series of brain penetrant 2-azabicyclo[2,2,2]octane sulfonamides and identified a compound with low nanomolar potency and high selectivity (>250-fold) towards the PSEN1-APH1B sub-complex versus PSEN2 sub-complexes. We used modelling and site directed mutagenesis to identify critical amino acids along the entry part of this inhibitor into the catalytic site of PSEN1. Specific targeting one of the different γ-secretase complexes might provide safer drugs in the future.

AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design

With advances in protein structure predictions, the number of available high-quality structures has increased dramatically. In light of these advances, structure-based enzyme engineering is expected to become increasingly important for optimizing biocatalysts for industrial processes. Here, we present AsiteDesign, a Monte Carlo-based protocol for structure-based engineering of active sites. AsiteDesign provides a framework for introducing new catalytic residues in a given binding pocket to either create a new catalytic activity or alter the existing one. AsiteDesign is implemented using pyRosetta and incorporates enhanced sampling techniques to efficiently explore the search space. The protocol was tested by designing an alternative catalytic triad in the active site of Pseudomonas fluorescens esterase (PFE). The designed variant was experimentally verified to be active, demonstrating that AsiteDesign can find alternative catalytic triads. Additionally, the AsiteDesign protocol was employed to enhance the hydrolysis of a bulky chiral substrate (1-phenyl-2-pentyl acetate) by PFE. The experimental verification of the designed variants demonstrated that F158L/F198A and F125A/F158L mutations increased the hydrolysis of 1-phenyl-2-pentyl acetate from 8.9 to 66.7 and 23.4%, respectively, and reversed the enantioselectivity of the enzyme from (R) to (S)-enantiopreference, with 32 and 55% enantiomeric excess (ee), respectively.

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

Recent PELE Developments and Applications in Drug Discovery Campaigns

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.

Design of Artificial Enzymes Bearing Several Active Centers: New Trends, Opportunities and Problems

Harnessing enzymes which possess several catalytic activities is a topic where intense research has been carried out, mainly coupled with the development of cascade reactions. This review tries to cover the different possibilities to reach this goal: enzymes with promiscuous activities, fusion enzymes, enzymes + metal catalysts (including metal nanoparticles or site-directed attached organometallic catalyst), enzymes bearing non-canonical amino acids + metal catalysts, design of enzymes bearing a second biological but artificial active center (plurizymes) by coupling enzyme modelling and directed mutagenesis and plurizymes that have been site directed modified in both or in just one active center with an irreversible inhibitor attached to an organometallic catalyst. Some examples of cascade reactions catalyzed by the enzymes bearing several catalytic activities are also described. Finally, some foreseen problems of the use of these multi-activity enzymes are described (mainly related to the balance of the catalytic activities, necessary in many instances, or the different operational stabilities of the different catalytic activities). The design of new multi-activity enzymes (e.g., plurizymes or modified plurizymes) seems to be a topic with unarguable interest, as this may link biological and non-biological activities to establish new combo-catalysis routes.

Investigation of small molecule inhibitors of the SARS-CoV-2 papain-like protease by all-atom microsecond modelling, PELE Monte Carlo simulations, and in vitro activity inhibition

The SARS-CoV-2 papain-like (PL^pro) protease is essential for viral replication. We investigated potential antiviral effects of hypericin relative to the well-known noncovalent PL^pro inhibitor GRL-0617. Molecular dynamics and PELE Monte Carlo simulations highlight favourable binding of hypericin and GRL-0617 to the naphthalene binding pocket of PL^pro. Although not potent as GRL-0617 (45.8 vs 1.6 µM for protease activity, respectively), in vitro fluorogenic enzymatic assays with hypericin show concentration-dependent inhibition of both PL^pro protease and deubiquitinating activities. Given its use in supplementations and the FDA conditional approval of a synthetic version, further evaluation of hypericin as a potential SARS-CoV-2 antiviral is warranted.

Structural basis for substrate specificity of heteromeric transporters of neutral amino acids

Despite having similar structures, each member of the heteromeric amino acid transporter (HAT) family shows exquisite preference for the exchange of certain amino acids. Substrate specificity determines the physiological function of each HAT and their role in human diseases. However, HAT transport preference for some amino acids over others is not yet fully understood. Using cryo-electron microscopy of apo human LAT2/CD98hc and a multidisciplinary approach, we elucidate key molecular determinants governing neutral amino acid specificity in HATs. A few residues in the substrate-binding pocket determine substrate preference. Here, we describe mutations that interconvert the substrate profiles of LAT2/CD98hc, LAT1/CD98hc, and Asc1/CD98hc. In addition, a region far from the substrate-binding pocket critically influences the conformation of the substrate-binding site and substrate preference. This region accumulates mutations that alter substrate specificity and cause hearing loss and cataracts. Here, we uncover molecular mechanisms governing substrate specificity within the HAT family of neutral amino acid transporters and provide the structural bases for mutations in LAT2/CD98hc that alter substrate specificity and that are associated with several pathologies.

Small molecule interactions with the SARS-CoV-2 main protease: In silico all-atom microsecond MD simulations, PELE Monte Carlo simulations, and determination of in vitro activity inhibition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the ongoing COVID-19 pandemic. With some notable exceptions, safe and effective vaccines, which are now being widely distributed globally, have largely begun to stabilise the situation. However, emerging variants of concern and vaccine hesitancy are apparent obstacles to eradication. Therefore, the need for the development of potent antivirals is still of importance. In this context, the SARS-CoV-2 main protease (M^pro) is a critical target and numerous clinical trials, predominantly in the private domain, are currently in progress. Here, our aim was to extend our previous studies, with hypericin and cyanidin-3-O-glucoside, as potential inhibitors of the SARS-CoV-2 M^pro. Firstly, we performed all-atom microsecond molecular dynamics simulations, which highlight the stability of the ligands in the M^pro active site over the duration of the trajectories. We also invoked PELE Monte Carlo simulations which indicate that both hypericin and cyanidin-3-O-glucoside preferentially interact with the M^pro active site and known allosteric sites. For further validation, we performed an in vitro enzymatic activity assay that demonstrated that hypericin and cyanidin-3-O-glucoside inhibit M^pro activity in a dose-dependent manner at biologically relevant (μM) concentrations. However, both ligands are much less potent than the well-known covalent antiviral GC376, which was used as a positive control in our experiments. Nevertheless, the biologically relevant activity of hypericin and cyanidin-3-O-glucoside is encouraging. In particular, a synthetic version of hypericin has FDA orphan drug designation, which could simplify potential clinical evaluation in the context of COVID-19.

Mapping Potential Determinants of Peroxidative Activity in an Evolved Fungal Peroxygenase from Agrocybe aegerita

Fungal unspecific peroxygenases (UPOs) are hybrid biocatalysts with peroxygenative activity that insert oxygen into non-activated compounds, while also possessing convergent peroxidative activity for one electron oxidation reactions. In several ligninolytic peroxidases, the site of peroxidative activity is associated with an oxidizable aromatic residue at the protein surface that connects to the buried heme domain through a long-range electron transfer (LRET) pathway. However, the peroxidative activity of these enzymes may also be initiated at the heme access channel. In this study, we examined the origin of the peroxidative activity of UPOs using an evolved secretion variant (PaDa-I mutant) from Agrocybe aegerita as our point of departure. After analyzing potential radical-forming aromatic residues at the PaDa-I surface by QM/MM, independent saturation mutagenesis libraries of Trp24, Tyr47, Tyr79, Tyr151, Tyr265, Tyr281, Tyr293 and Tyr325 were constructed and screened with both peroxidative and peroxygenative substrates. These mutant libraries were mostly inactive, with only a few functional clones detected, none of these showing marked differences in the peroxygenative and peroxidative activities. By contrast, when the flexible Gly314-Gly318 loop that is found at the outer entrance to the heme channel was subjected to combinatorial saturation mutagenesis and computational analysis, mutants with improved kinetics and a shift in the pH activity profile for peroxidative substrates were found, while they retained their kinetic values for peroxygenative substrates. This striking change was accompanied by a 4.5°C enhancement in kinetic thermostability despite the variants carried up to four consecutive mutations. Taken together, our study proves that the origin of the peroxidative activity in UPOs, unlike other ligninolytic peroxidases described to date, is not dependent on a LRET route from oxidizable residues at the protein surface, but rather it seems to be exclusively located at the heme access channel.

Elucidation of ligand binding and dimerization of NADPH:protochlorophyllide (Pchlide) oxidoreductase from pea (Pisum sativum L.) by structural analysis and simulations

NADPH:protochlorophyllide (Pchlide) oxidoreductase (POR) is a key enzyme of chlorophyll biosynthesis in angiosperms. It is one of few known photoenzymes, which catalyzes the light-activated trans-reduction of the C17-C18 double bond of Pchlide's porphyrin ring. Due to the light requirement, dark-grown angiosperms cannot synthesize chlorophyll. No crystal structure of POR is available, so to improve understanding of the protein's three-dimensional structure, its dimerization, and binding of ligands (both the cofactor NADPH and substrate Pchlide), we computationally investigated the sequence and structural relationships among homologous proteins identified through database searches. The results indicate that α4 and α7 helices of monomers form the interface of POR dimers. On the basis of conserved residues, we predicted 11 functionally important amino acids that play important roles in POR binding to NADPH. Structural comparison of available crystal structures revealed that they participate in formation of binding pockets that accommodate the Pchlide ligand, and that five atoms of the closed tetrapyrrole are involved in non-bonding interactions. However, we detected no clear pattern in the physico-chemical characteristics of the amino acids they interact with. Thus, we hypothesize that interactions of these atoms in the Pchlide porphyrin ring are important to hold the ligand within the POR binding site. Analysis of Pchlide binding in POR by molecular docking and PELE simulations revealed that the orientation of the nicotinamide group is important for Pchlide binding. These findings highlight the complexity of interactions of porphyrin-containing ligands with proteins, and we suggest that fit-inducing processes play important roles in POR-Pchlide interactions.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

Reinforcement learning to boost molecular docking upon protein conformational ensemble

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

Advances in enzymatic oxyfunctionalization of aliphatic compounds

Selective oxyfunctionalizations of aliphatic compounds are difficult chemical reactions, where enzymes can play an important role due to their stereo- and regio-selectivity and operation under mild reaction conditions. P450 monooxygenases are well-known biocatalysts that mediate oxyfunctionalization reactions in different living organisms (from bacteria to humans). Unspecific peroxygenases (UPOs), discovered in fungi, have arisen as "dream biocatalysts" of great biotechnological interest because they catalyze the oxyfunctionalization of aliphatic and aromatic compounds, avoiding the necessity of expensive cofactors and regeneration systems, and only depending on H₂O₂ for their catalysis. Here, we summarize recent advances in aliphatic oxyfunctionalization reactions by UPOs, as well as the molecular determinants of the enzyme structures responsible for their activities, emphasizing the differences found between well-known P450s and the novel fungal peroxygenases.

Known Evolutionary Paths Are Accessible to Engineered ß-Lactamases Having Altered Protein Motions at the Timescale of Catalytic Turnover

The evolution of new protein functions is dependent upon inherent biophysical features of proteins. Whereas, it has been shown that changes in protein dynamics can occur in the course of directed molecular evolution trajectories and contribute to new function, it is not known whether varying protein dynamics modify the course of evolution. We investigate this question using three related ß-lactamases displaying dynamics that differ broadly at the slow timescale that corresponds to catalytic turnover yet have similar fast dynamics, thermal stability, catalytic, and substrate recognition profiles. Introduction of substitutions E104K and G238S, that are known to have a synergistic effect on function in the parent ß-lactamase, showed similar increases in catalytic efficiency toward cefotaxime in the related ß-lactamases. Molecular simulations using Protein Energy Landscape Exploration reveal that this results from stabilizing the catalytically-productive conformations, demonstrating the dominance of the synergistic effect of the E014K and G238S substitutions in vitro in contexts that vary in terms of sequence and dynamics. Furthermore, three rounds of directed molecular evolution demonstrated that known cefotaximase-enhancing mutations were accessible regardless of the differences in dynamics. Interestingly, specific sequence differences between the related ß-lactamases were shown to have a higher effect in evolutionary outcomes than did differences in dynamics. Overall, these ß-lactamase models show tolerance to protein dynamics at the timescale of catalytic turnover in the evolution of a new function.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

Extensible and Scalable Adaptive Sampling on Supercomputers

The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of high-performance computer (HPC) systems. Utilizing only "brute force" molecular dynamics (MD) simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy, the speed up can be more than 1 order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies and reliably execute resulting workflows at scale on HPC platforms. Here, the folding dynamics of four proteins are predicted with no a priori information.

Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models

We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 μs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.

Δ9 -Tetrahydrocannabinolic acid alleviates collagen-induced arthritis: Role of PPARγ and CB1 receptors

BACKGROUND AND PURPOSE

Δ⁹ -Tetrahydrocannabinolic acid (Δ⁹ -THCA-A), the precursor of Δ⁹ -THC, is a non-psychotropic phytocannabinoid that shows PPARγ agonist activity. Here, we investigated the ability of Δ⁹ -THCA-A to modulate the classic cannabinoid CB₁ and CB₂ receptors and evaluated its anti-arthritis activity in vitro and in vivo.

EXPERIMENTAL APPROACH

Cannabinoid receptors binding and intrinsic activity, as well as their downstream signalling, were analysed in vitro and in silico. The anti-arthritis properties of Δ⁹ -THCA-A were studied in human chondrocytes and in the murine model of collagen-induced arthritis (CIA). Plasma disease biomarkers were identified by LC-MS/MS based on proteomic and elisa assays.

KEY RESULTS

Functional and docking analyses showed that Δ⁹ -THCA-A can act as an orthosteric CB₁ receptor agonist and also as a positive allosteric modulator in the presence of CP-55,940. Also, Δ⁹ -THCA-A seemed to be an inverse agonist for CB₂ receptors. In vivo, Δ⁹ -THCA-A reduced arthritis in CIA mice, preventing the infiltration of inflammatory cells, synovium hyperplasia, and cartilage damage. Furthermore, Δ⁹ -THCA-A inhibited expression of inflammatory and catabolic genes on knee joints. The anti-arthritic effect of Δ⁹ -THCA-A was blocked by either SR141716 or T0070907. Analysis of plasma biomarkers, and determination of cytokines and anti-collagen antibodies confirmed that Δ⁹ -THCA-A mediated its activity mainly through PPARγ and CB₁ receptor pathways.

CONCLUSION AND IMPLICATIONS

Δ⁹ -THCA-A modulates CB₁ receptors through the orthosteric and allosteric binding sites. In addition, Δ⁹ -THCA-A exerts anti-arthritis activity through CB₁ receptors and PPARγ pathways, highlighting its potential for the treatment of chronic inflammatory diseases such as rheumatoid arthritis.

Laplacian score and genetic algorithm based automatic feature selection for Markov State Models in adaptive sampling based molecular dynamics

Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results.

Methods

In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes.

Results

Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.

Correlation functions, mean first passage times, and the Kemeny constant

Markov processes are widely used models for investigating kinetic networks. Here, we collate and present a variety of results pertaining to kinetic network models in a unified framework. The aim is to lay out explicit links between several important quantities commonly studied in the field, including mean first passage times (MFPTs), correlation functions, and the Kemeny constant. We provide new insights into (i) a simple physical interpretation of the Kemeny constant, (ii) a relationship to infer equilibrium distributions and rate matrices from measurements of MFPTs, and (iii) a protocol to reduce the dimensionality of kinetic networks based on specific requirements that the MFPTs in the coarse-grained system should satisfy. We prove that this protocol coincides with the one proposed by Hummer and Szabo [J. Phys. Chem. B 119, 9029 (2014)], and it leads to a variational principle for the Kemeny constant. Finally, we introduce a modification of this protocol, which preserves the Kemeny constant. Our work underpinning the theoretical aspects of kinetic networks will be useful in applications including milestoning and path sampling algorithms in molecular simulations.

FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design

The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem. To aid in this matter, we present FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment from a bound core while exploring the protein-ligand conformational space. We tested the ability of FragPELE to predict crystallographic data, even in cases where cryptic sub-pockets open because of the presence of particular R-groups. Additionally, we evaluated the potential of the software on growing and scoring five congeneric series from the 2015 FEP+ dataset, comparing them to FEP+, SP and Induced-Fit Glide, and MMGBSA simulations. Results show that FragPELE could be useful not only for finding new cavities and novel binding modes in cases where standard docking tools cannot but also to rank ligand activities in a reasonable amount of time and with acceptable precision.

Monte Carlo simulations using PELE to identify a protein-protein inhibitor binding site and pose

In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein–protein disruptors.

PELE prospectively unveils the binding site and mode of a protein–protein disruptor.

Atomistic simulations shed new light on the activation mechanisms of RORγ and classify it as Type III nuclear hormone receptor regarding ligand-binding paths

The molecular recognition of the RORγ nuclear hormone receptor (NHR) ligand-binding domain (LBD) has been extensively studied with numerous X-ray crystal structures. However, the picture afforded by these complexes is static and does not fully explain the functional behavior of the LBD. In particular, the apo structure of the LBD seems to be in a fully active state, with no obvious differences to the agonist-bound structure. Further, several atypical in vivo inverse agonists have surprisingly been found to co-crystallize with the LBD in agonist mode (with co-activator), leading to a disconnection between molecular recognition and functional activity. Moreover, the experimental structures give no clues on how RORγ LBD binders access the interior of the LBD. To address all these points, we probe here, with a variety of simulation techniques, the fine structural balance of the RORγ LBD in its apo vs. holo form, the differences in flexibility and stability of the LBD in complex with agonists vs. inverse agonists and how binders diffuse in and out of the LBD in unbiased simulations. Our data conclusively point to the stability afforded by the so-called “agonist lock” between H479 and Y502 and the precise location of Helix 12 (H12) for the competence of the LBD to bind co-activator proteins. We observe the “water trapping” mechanism suggested previously for the atypical inverse agonists and discover a different behavior for the latter when co-activator is present or absent, which might help explain their conflicting data. Additionally, we unveil the same entry/exit path for agonists and inverse agonist into and out of the LBD for RORγ, suggesting it belongs to the type III NHR sub-family.

Sequential oxidation of 5-hydroxymethylfurfural to furan-2,5-dicarboxylic acid by an evolved aryl-alcohol oxidase

Furan-2,5-dicarboxylic acid (FDCA) is a building block of biodegradable plastics that can be used to replace those derived from fossil carbon sources. In recent years, much interest has focused on the synthesis of FDCA from the bio-based 5-hydroxymethylfurfural (HMF) through a cascade of enzyme reactions. Aryl-alcohol oxidase (AAO) and 5-hydroxymethylfurfural oxidase (HMFO) are glucose-methanol-choline flavoenzymes that may be used to produce FDCA from HMF through three sequential oxidations, and without the assistance of auxiliary enzymes. Such a challenging process is dependent on the degree of hydration of the original aldehyde groups and of those formed, the rate-limiting step lying in the final oxidation of the intermediate 5-formyl-furancarboxylic acid (FFCA) to FDCA. While HMFO accepts FFCA as a final substrate in the HMF reaction pathway, AAO is virtually incapable of oxidizing it. Here, we have engineered AAO to perform the stepwise oxidation of HMF to FDCA through its structural alignment with HMFO and directed evolution. With a 3-fold enhanced catalytic efficiency for HMF and a 6-fold improvement in overall conversion, this evolved AAO is a promising point of departure for further engineering aimed at generating an efficient biocatalyst to synthesize FDCA from HMF.

Evolutionary significance and functional characterization of streptomycin adenylyltransferase from Serratia marcescens

Complete functional annotations of proteins are essential to understand the role and mechanisms in pathogenesis. Aminoglycoside nucleotidyltransferases are the subclasses of aminoglycosides modifying enzymes conferring resistance to organisms. Insight into the structural and functional understanding of nucleotidyltransferase family protein provides vital information to combat pathogenesis. Phylogenetic analysis is employed to identify the evolutionary significance and common motif's present among the homologs of nucleotidyltransferase family protein. Structure, sequence based approaches and molecular docking were implemented to predict the exact function of the protein. Wide distribution of the nucleotidyltransferase family protein in gram-positive and gram-negative organisms are evidenced from phylogenetic analysis. Five common motifs were present in all the homolog's of nucleotidyltransferase family protein. Sequence-structure based functional annotations predicts that the targeted protein function as ATP-Mg dependent streptomycin adenylyltransferase. Structural comparisons and docking studies correlate well with the identified function. The complete function of nucleotidyltransferase family protein was identified as Streptomycin adenylyltransferase and it could be targeted as a potential therapeutic target to overcome antibiotic resistance.Communicated by Ramaswamy H. SarmaAbbreviationsAACaminoglycoside acetyltransferasesAMEaminoglycoside modifying enzymeANTaminoglycoside nucleotidyltransferasesAPHaminoglycoside phosphotransferasesATPadenosine triphosphateCASTpcomputer atlas and surface topography of proteinsDUFdomains of unknown functionGlidegrid-based ligand docking with energeticHMMhidden Markov modelMASTmotif alignment and search toolMEGAmolecular evolutionary genetics analysisMEMEmultiple Em for motif elicitationMSAmultiple sequence alignmentNMPnucleoside monophosphateNTPnucleoside triphosphateNTnucleotidyltransferaseOPLSoptimized potential for liquid simulationXPextra precision.

Quantitative comparison of adaptive sampling methods for protein dynamics

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. In this work, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.

Switching the substrate preference of fungal aryl-alcohol oxidase: towards stereoselective oxidation of secondary benzyl alcohols

Using PELE computational simulations the ability to deracemize secondary benzylic alcohols was introduced (by I500M/F501W double mutation) in stereoselective AAO.

Selective synthesis of 4-hydroxyisophorone and 4-ketoisophorone by fungal peroxygenases

Some fungal peroxygenases (UPOs) selectively oxidize α-isophorone to 4-hydroxyisophorone (4HIP) and 4-ketoisophorone (4KIP) while others are less selective or unable.

Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Substantial improvements in enzyme activity demand multiple mutations at spatially proximal positions in the active site. Such mutations, however, often exhibit unpredictable epistatic (non-additive) effects on activity. Here we describe FuncLib, an automated method for designing multipoint mutations at enzyme active sites using phylogenetic analysis and Rosetta design calculations. We applied FuncLib to two unrelated enzymes, a phosphotriesterase and an acetyl-CoA synthetase. All designs were active, and most showed activity profiles that significantly differed from the wild-type and from one another. Several dozen designs with only 3-6 active-site mutations exhibited 10- to 4,000-fold higher efficiencies with a range of alternative substrates, including hydrolysis of the toxic organophosphate nerve agents soman and cyclosarin and synthesis of butyryl-CoA. FuncLib is implemented as a web server (http://FuncLib.weizmann.ac.il); it circumvents iterative, high-throughput experimental screens and opens the way to designing highly efficient and diverse catalytic repertoires.

Multiple implications of an active site phenylalanine in the catalysis of aryl-alcohol oxidase

Aryl-alcohol oxidase (AAO) has demonstrated to be an enzyme with a bright future ahead due to its biotechnological potential in deracemisation of chiral compounds, production of bioplastic precursors and other reactions of interest. Expanding our understanding on the AAO reaction mechanisms, through the investigation of its structure-function relationships, is crucial for its exploitation as an industrial biocatalyst. In this regard, previous computational studies suggested an active role for AAO Phe397 at the active-site entrance. This residue is located in a loop that partially covers the access to the cofactor forming a bottleneck together with two other aromatic residues. Kinetic and affinity spectroscopic studies, complemented with computational simulations using the recently developed adaptive-PELE technology, reveal that the Phe397 residue is important for product release and to help the substrates attain a catalytically relevant position within the active-site cavity. Moreover, removal of aromaticity at the 397 position impairs the oxygen-reduction activity of the enzyme. Experimental and computational findings agree very well in the timing of product release from AAO, and the simulations help to understand the experimental results. This highlights the potential of adaptive-PELE to provide answers to the questions raised by the empirical results in the study of enzyme mechanisms.

The ins and outs of vanillyl alcohol oxidase: Identification of ligand migration paths

Vanillyl alcohol oxidase (VAO) is a homo-octameric flavoenzyme belonging to the VAO/PCMH family. Each VAO subunit consists of two domains, the FAD-binding and the cap domain. VAO catalyses, among other reactions, the two-step conversion of p-creosol (2-methoxy-4-methylphenol) to vanillin (4-hydroxy-3-methoxybenzaldehyde). To elucidate how different ligands enter and exit the secluded active site, Monte Carlo based simulations have been performed. One entry/exit path via the subunit interface and two additional exit paths have been identified for phenolic ligands, all leading to the si side of FAD. We argue that the entry/exit path is the most probable route for these ligands. A fourth path leading to the re side of FAD has been found for the co-ligands dioxygen and hydrogen peroxide. Based on binding energies and on the behaviour of ligands in these four paths, we propose a sequence of events for ligand and co-ligand migration during catalysis. We have also identified two residues, His466 and Tyr503, which could act as concierges of the active site for phenolic ligands, as well as two other residues, Tyr51 and Tyr408, which could act as a gateway to the re side of FAD for dioxygen. Most of the residues in the four paths are also present in VAO’s closest relatives, eugenol oxidase and p-cresol methylhydroxylase. Key path residues show movements in our simulations that correspond well to conformations observed in crystal structures of these enzymes. Preservation of other path residues can be linked to the electron acceptor specificity and oligomerisation state of the three enzymes. This study is the first comprehensive overview of ligand and co-ligand migration in a member of the VAO/PCMH family, and provides a proof of concept for the use of an unbiased method to sample this process.

Enzymes are bionanomachines, which speed up chemical reactions in organisms. To understand how they achieve that, we need to study their mechanisms. Computational enzymology can show us what happens in the enzyme’s active site during a reaction. But molecules need first to reach the active site before a reaction can start. The process of substrate entry and product exit to the active site is often neglected when studying enzymes. However, these two events are of fundamental importance to the proper functioning of any enzyme. We are interested in these dynamic processes to complete our understanding of the mode of action of enzymes. In our work, we have studied substrate and product migration in vanillyl alcohol oxidase. This enzyme can produce the flavour vanillin and enantiopure alcohols, but also catalyses other reactions. The named products are of interest to the flavour- and fine-chemical industries.