Applications of water molecules for analysis of macromolecule properties

Water Migration through Enzyme Tunnels Is Sensitive to the Choice of Explicit Water Model

The utilization of tunnels and water transport within enzymes is crucial for their catalytic function as water molecules can stabilize bound substrates and help with unbinding processes of products and inhibitors. Since the choice of water models for molecular dynamics simulations was shown to determine the accuracy of various calculated properties of the bulk solvent and solvated proteins, we have investigated if and to what extent water transport through the enzyme tunnels depends on the selection of the water model. Here, we focused on simulating enzymes with various well-defined tunnel geometries. In a systematic investigation using haloalkane dehalogenase as a model system, we focused on the well-established TIP3P, OPC, and TIP4P-Ew water models to explore their impact on the use of tunnels for water molecule transport. The TIP3P water model showed significantly faster migration, resulting in the transport of approximately 2.5 times more water molecules compared to that of the OPC and 1.7 times greater than that of the TIP4P-Ew. Finally, the transport was 1.4-fold more pronounced in TIP4P-Ew than in OPC. The increase in migration of TIP3P water molecules was mainly due to faster transit times through dehalogenase tunnels. We observed similar behavior in two different enzymes with buried active sites and different tunnel network topologies, i.e., alditol oxidase and cytochrome P450, indicating that our findings are likely not restricted to a particular enzyme family. Overall, this study showcases the critical importance of water models in comprehending the use of enzyme tunnels for small molecule transport. Given the significant role of water availability in various stages of the catalytic cycle and the solvation of substrates, products, and drugs, choosing an appropriate water model may be crucial for accurate simulations of complex enzymatic reactions, rational enzyme design, and predicting drug residence times.

Absolute Binding Free Energies with OneOPES

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.

Eta-Earth Revisited I: A Formula for Estimating the Maximum Number of Earth-Like Habitats

In this hypothesis article, we discuss the basic requirements of planetary environments where aerobe organisms can grow and survive, including atmospheric limitations of millimeter-to-meter-sized biological animal life based on physical limits and O2, N2, and CO2 toxicity levels. By assuming that animal-like extraterrestrial organisms adhere to similar limits, we define Earth-like habitats (EH) as rocky exoplanets in the habitable zone for complex life that host N2-O2-dominated atmospheres with minor amounts of CO2, at which advanced animal-like life or potentially even extraterrestrial intelligent life can in principle evolve and exist. We then derive a new formula that can be used to estimate the maximum occurrence rate of such Earth-like habitats in the Galaxy. This contains realistic probabilistic arguments that can be fine-tuned and constrained by atmospheric characterization with future space and ground-based telescopes. As an example, we briefly discuss two specific requirements feeding into our new formula that, although not quantifiable at present, will become scientifically quantifiable in the upcoming decades due to future observations of exoplanets and their atmospheres. Key Words: Eta-Earth-Earth-like habitats-oxygenation time-nitrogen atmospheres-carbon dioxide-animal-like life. Astrobiology 24, 897-915.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine Sulfonamides as an Important Scaffold for Anticancer Drug Discovery-In Vitro and In Silico Evaluation

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine sulfonamides (MM-compounds) are a relatively new class of heterocyclic compounds that exhibit a wide variety of biological actions, including anticancer properties. Here, we used caspase enzyme activity assays, flow cytometry analysis of propidium iodide (PI)-stained cells, and a DNA laddering assay to investigate the mechanisms of cell death triggered by the MM-compounds (MM134, -6, -7, and -9). Due to inconsistent results in caspase activity assays, we have performed a bromodeoxyuridine (BrdU) incorporation assay, colony formation assay, and gene expression profiling. The compounds' cytotoxic and pro-oxidative properties were also assessed. Additionally, computational studies were performed to demonstrate the potential of the scaffold for future drug discovery endeavors. MM-compounds exhibited strong micromolar (0.06-0.35 µM) anti-proliferative and pro-oxidative activity in two cancer cell lines (BxPC-3 and PC-3). Activation of caspase 3/7 was observed following a 24-h treatment of BxPC-3 cells with IC₅₀ concentrations of MM134, -6, and -9 compounds. However, no DNA fragmentation characteristics for apoptosis were observed in the flow cytometry and DNA laddering analysis. Gene expression data indicated up-regulation of BCL10, GADD45A, RIPK2, TNF, TNFRSF10B, and TNFRSF1A (TNF-R1) following treatment of cells with the MM134 compound. Moreover, in silico studies indicated AKT2 kinase as the primary target of compounds. MM-compounds exhibit strong cytotoxic activity with pro-oxidative, pro-apoptotic, and possibly pro-necroptotic properties that could be employed for further drug discovery approaches.

Transient binding sites at the surface of haloalkane dehalogenase LinB as locations for fine-tuning enzymatic activity

The haloalkane dehalogenase LinB is a well-known enzyme that contains buried active site and is used for many modelling studies. Using classical molecular dynamics simulations of enzymes and substrates, we searched for transient binding sites on the surface of the LinB protein by calculating maps of enzyme-ligand interactions that were then transformed into sparse matrices. All residues considered as functionally important for enzyme performance (e.g., tunnel entrances) were excluded from the analysis to concentrate rather on non-obvious surface residues. From a set of 130 surface residues, twenty-six were proposed as a promising improvement of enzyme performance. Eventually, based on rational selection and filtering out the potentially unstable mutants, a small library of ten mutants was proposed to validate the possibility of fine-tuning the LinB protein. Nearly half of the predicted mutant structures showed improved activity towards the selected substrates, which demonstrates that the proposed approach could be applied to identify non-obvious yet beneficial mutations for enzyme performance especially when obvious locations have already been explored.

Total free energy analysis of fully hydrated proteins

The total free energy of a hydrated biomolecule and its corresponding decomposition of energy and entropy provides detailed information about regions of thermodynamic stability or instability. The free energies of four hydrated globular proteins with different net charges are calculated from a molecular dynamics simulation, with the energy coming from the system Hamiltonian and entropy using multiscale cell correlation. Water is found to be most stable around anionic residues, intermediate around cationic and polar residues, and least stable near hydrophobic residues, especially when more buried, with stability displaying moderate entropy-enthalpy compensation. Conversely, anionic residues in the proteins are energetically destabilized relative to singly solvated amino acids, while trends for other residues are less clear-cut. Almost all residues lose intraresidue entropy when in the protein, enthalpy changes are negative on average but may be positive or negative, and the resulting overall stability is moderate for some proteins and negligible for others. The free energy of water around single amino acids is found to closely match existing hydrophobicity scales. Regarding the effect of secondary structure, water is slightly more stable around loops, of intermediate stability around β strands and turns, and least stable around helices. An interesting asymmetry observed is that cationic residues stabilize a residue when bonded to its N-terminal side but destabilize it when on the C-terminal side, with a weaker reversed trend for anionic residues.

Water regulates the residence time of Benzamidine in Trypsin

The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.

Imidazole and nitroimidazole derivatives as NADH-fumarate reductase inhibitors: Density functional theory studies, homology modeling, and molecular docking

Chagas disease is caused by Trypanosoma cruzi. Benznidazole and nifurtimox are drugs used for its therapy; nevertheless, they have collateral effects. NADH-fumarate (FUM) reductase is a potential pharmacological target since it is essential for survival of parasite and is not found in humans. The objectives are to design and characterize the electronic structure of imidazole and nitroimidazole derivatives at DFT-M06-2X level in aqueous solution; also, to model the NADH-FUM reductase and analyze its intermolecular interactions by molecular docking. Quantum-chemical descriptors allowed to select the molecules with the best physicochemical properties and lowest toxicity. A high-quality three-dimensional structure of NADH-FUM reductase was obtained by homology modeling. Water molecules do not have influence in the interaction between FUM and NADH-FUM reductase. The main hydrogen-binding interactions for FUM were identified in NADH, Lys172, and Arg89; while hydrophobic interactions in Phe479, Thr174, Met63. The molecules S3-8, S2-8, and S1-8 could be inhibitors of NADH-FUM reductase.

Prediction of Water Distributions and Displacement at Protein-Ligand Interfaces

The retention and displacement of water molecules during formation of ligand-protein interfaces play a major role in determining ligand binding. Understanding these effects requires a method for positioning of water molecules in the bound and unbound proteins and for defining water displacement upon ligand binding. We describe an algorithm for water placement and a calculation of ligand-driven water displacement in >9000 protein-ligand complexes. The algorithm predicts approximately 38% of experimental water positions within 1.0 Å and about 83% within 1.5 Å. We further show that the predicted water molecules can complete water networks not detected in crystallographic structures of the protein-ligand complexes. The algorithm was also applied to solvation of the corresponding unbound proteins, and this allowed calculation of water displacement upon ligand binding based on differences in the water network between the bound and unbound structures. We illustrate use of this approach through comparison of water displacement by structurally related ligands at the same binding site. This method for evaluation of water displacement upon ligand binding may be of value for prediction of the effects of ligand modification in drug design.

Structure-function relationship between soluble epoxide hydrolases structure and their tunnel network

Enzymes with buried active sites maintain their catalytic function via a single tunnel or tunnel network. In this study we analyzed the functionality of soluble epoxide hydrolases (sEHs) tunnel network, by comparing the overall enzyme structure with the tunnel's shape and size. sEHs were divided into three groups based on their structure and the tunnel usage. The obtained results were compared with known substrate preferences of the studied enzymes, as well as reported in our other work evolutionary analyses data. The tunnel network architecture corresponded well with the evolutionary lineage of the source organism and large differences between enzymes were observed from long fragments insertions. This strategy can be used during protein re-engineering process for large changes introduction, whereas tunnel modification can be applied for fine-tuning of enzyme.

Computational insights into the known inhibitors of human soluble epoxide hydrolase

Human soluble epoxide hydrolase (hsEH) is involved in the hydrolysis of epoxyeicosatrienoic acids (EETs), which have potent anti-inflammatory properties. Given that EET conversion generates nonbioactive molecules, inhibition of this enzyme would be beneficial. Past decades of work on hsEH inhibitors resulted in numerous potential compounds, of which a hundred hsEH-ligand complexes were crystallized and deposited in the Protein Data Bank (PDB). We analyzed all deposited hsEH-ligand complexes to gain insight into the binding of inhibitors and to provide feedback on the future drug design processes. We also reviewed computationally driven strategies that were used to propose novel hsEH inhibitors.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Weak spots inhibition in the Mycobacterium tuberculosis antigen 85C target for antitubercular drug design through selective irreversible covalent inhibitor-SER124

Mycobacterium tuberculosis (Mtb) encoded secreted antigen 85 enzymes (Ag85A/Ag85B/Ag85C) play that critical roles in the virulence, survival and drug-resistant TB of the pathogen. Ag85 proteins are potential antitubercular drug targets because they are essential in the catalytic synthesis of trehalose moieties and mycolic acid attachment to the Mtb cell wall. Recently, experimental protocols led to the discovery of a selective covalent Ag85 inhibitor, β-isomer monocyclic enolphosphorus Cycliphostin (CyC_8β) compound, which targets the Ag85 serine 124 to exhibit a promising therapeutic activity. For the first time, our study unravelled the structural features among Mtb Ag85C homologs and motions and dynamics of Ag85C when the CyC_8β bound covalently and in open model conformations to the protein using bioinformatics tools and integrated Molecular dynamics simulations. Comparative Ag85C sequence analysis revealed conserved regions; 70% active site, 90% Adeniyi loop L1 and 50% loop L2, which acts as a switch between open and closed conformations. The average C-α atoms RMSD (2.05 Å) and RMSF (0.9 Å) revealed instability and high induced flexibility in the CyC_8β covalent-bound compared to the apo and open model systems, which displayed more stability and lower fluctuations. DSSP showed structural transitions of α-helices to bend and loops to 3₁₀-helices in the bound systems. SASA of CyC_8β covalent bound showed active site hydrophobic residues exposure to huge solvent. Therefore, these findings present the potential opportunity hotspots in Ag85C protein that would aid the structure-based design of novel chemical entities capable of resulting in potent antitubercular drugs.Communicated by Ramaswamy H. Sarma.

ProBiS H2O MD Approach for Identification of Conserved Water Sites in Protein Structures for Drug Design

The ProBiS H2O MD approach for identification of conserved waters and water sites of interest in macromolecular systems, which is becoming a typical step in a structure-based drug design or macromolecular study in general, is described. This work explores an extension of the ProBiS H2O approach introduced by Jukič et al. Indeed, water molecules are key players in the interaction mechanisms of macromolecules and small molecules and play structural roles. Our earlier developed approach, ProBiS H2O, is a simple and transparent workflow for conserved water detection. Here we have considered generalizing the idea by supplementing the experimental data with data derived from molecular dynamics to facilitate work on less known systems. Newly developed ProBiS H2O MD workflow uses trajectory data, extracts and identifies interesting water sites, and visualizes the results. ProBiS H2O MD can thus robustly process molecular dynamic trajectory snapshots, perform local superpositions, collect water location data, and perform density-based clustering to identify discrete sites with high conservation of water molecules. This is a new approach that uses experimental data in silico to identify interesting water sites. Methodology is fast and water-model or molecular dynamics software independent. Trends in the conservation of water molecules can be followed over a variety of trajectories, and our approach has been successfully validated using reported protein systems with experimentally observed conserved water molecules. ProBiS H2O MD is freely available as PyMOL plugin at http://insilab.org.

Structural and Evolutionary Analysis Indicate That the SARS-CoV-2 Mpro Is a Challenging Target for Small-Molecule Inhibitor Design

The novel coronavirus whose outbreak took place in December 2019 continues to spread at a rapid rate worldwide. In the absence of an effective vaccine, inhibitor repurposing or de novo drug design may offer a longer-term strategy to combat this and future infections due to similar viruses. Here, we report on detailed classical and mixed-solvent molecular dynamics simulations of the main protease (Mpro) enriched by evolutionary and stability analysis of the protein. The results were compared with those for a highly similar severe acute respiratory syndrome (SARS) Mpro protein. In spite of a high level of sequence similarity, the active sites in both proteins showed major differences in both shape and size, indicating that repurposing SARS drugs for COVID-19 may be futile. Furthermore, analysis of the binding site's conformational changes during the simulation time indicated its flexibility and plasticity, which dashes hopes for rapid and reliable drug design. Conversely, structural stability of the protein with respect to flexible loop mutations indicated that the virus' mutability will pose a further challenge to the rational design of small-molecule inhibitors. However, few residues contribute significantly to the protein stability and thus can be considered as key anchoring residues for Mpro inhibitor design.