Continuum solvent studies of the stability of RNA hairpin loops and helices

A Study on the Binding Mechanism and the Impact of Key Residue Mutations between SND1 and MTDH Peptide through Molecular Dynamics Simulations

Metastasis of breast cancer is the main cause of death for patients with breast cancer. The interaction between metadherin (MTDH) and staphylococcal nuclease domain 1 (SND1) plays a pivotal role in promoting breast cancer development. However, the binding details between MTDH and SND1 remain unclear. In this study, we employed all-atom molecular dynamics simulations (MDs) and conducted binding energy calculations to investigate the binding details and the impact of key residue mutations on binding. The mutations in key residues have not significantly affected the overall stability of the structure and the fluctuation of residues near the binding site; they have exerted a substantial impact on the binding of SND1 and MTDH peptide. The electrostatic interactions and van der Waals interactions play an important role in the binding of SND1 and the MTDH peptide. The mutations in the key residues have a significant impact on electrostatic and van der Waals interactions, resulting in weakened binding. The energy contributions of key residues mainly come from the electrostatic energy and van der Waals interactions of the side chain. In addition, the key residues form an intricate and stable network of hydrogen bonds and salt-bridge interactions with the MTDH peptide. The mutations in key residues have directly disrupt the interactions formed between SND1 and MTDH peptide, consequently leading to changes in the binding mode of the MTDH peptide. These analyses unveil the detailed atomic-level interaction mechanism between SND1 and the MTDH peptide, providing a molecular foundation for the development of antibreast cancer drugs.

Evaluation of Pregabalin bioadhesive multilayered microemulsion IOP-lowering eye drops

In spite of available treatment options, glaucoma continues to be a leading cause of irreversible blindness in the world. Current glaucoma medications have multiple limitations including: lack of sustained action; requirement for multiple dosing per day, ocular irritation and limited options for drugs with different mechanisms of action. Previously, we demonstrated that pregabalin, a drug with high affinity and selectivity for CACNA2D1, lowered IOP in a dose-dependent manner. The current study was designed to evaluate pregabalin microemulsion eye drops and to estimate its efficacy in humans using in silico methods. Molecular docking studies of pregabalin against CACNA2D1 of mouse, rabbit, and human were performed. Pregabalin microemulsion eye drops were characterized using multiple in vivo studies and its stability was evaluated over one year at different storage conditions. Molecular docking analyses and QSPR of pregabalin confirmed its suitability as a new IOP-lowering medication that functions using a new mechanism of action by binding to CACNA2D1 in all species evaluated. Because of its prolonged corneal residence time and corneal penetration enhancement, a single topical application of pregabalin ME can provide an extended IOP reduction of more than day in different animal models. Repeated daily dosing for 2 months confirms the lack of any tachyphylactic effect, which is a common drawback among marketed IOP-lowering medications. In addition, pregabalin microemulsion demonstrated good physical stability for one year, and chemical stability for 3-6 months if stored below 25 °C. Collectively, these outcomes greatly support the use of pregabalin eye drops as once daily IOP-lowering therapy for glaucoma management.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors

Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.

Structural dynamics of RAF1-HSP90-CDC37 and HSP90 complexes reveal asymmetric client interactions and key structural elements

RAF kinases are integral to the RAS-MAPK signaling pathway, and proper RAF1 folding relies on its interaction with the chaperone HSP90 and the cochaperone CDC37. Understanding the intricate molecular interactions governing RAF1 folding is crucial for comprehending this process. Here, we present a cryo-EM structure of the closed-state RAF1-HSP90-CDC37 complex, where the C-lobe of the RAF1 kinase domain binds to one side of the HSP90 dimer, and an unfolded N-lobe segment of the RAF1 kinase domain threads through the center of the HSP90 dimer. CDC37 binds to the kinase C-lobe, mimicking the N-lobe with its HxNI motif. We also describe structures of HSP90 dimers without RAF1 and CDC37, displaying only N-terminal and middle domains, which we term the semi-open state. Employing 1 μs atomistic simulations, energetic decomposition, and comparative structural analysis, we elucidate the dynamics and interactions within these complexes. Our quantitative analysis reveals that CDC37 bridges the HSP90-RAF1 interaction, RAF1 binds HSP90 asymmetrically, and that HSP90 structural elements engage RAF1's unfolded region. Additionally, N- and C-terminal interactions stabilize HSP90 dimers, and molecular interactions in HSP90 dimers rearrange between the closed and semi-open states. Our findings provide valuable insight into the contributions of HSP90 and CDC37 in mediating client folding.

Revealing the binding mechanism of BACE1 inhibitors through molecular dynamics simulations

Alzheimer's disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs molecular dynamics simulations and binding energy analysis to investigate the binding interactions between BACE1 and four selected small molecules: CNP520, D9W, NB641, and NB360. The binding model analysis indicates that the binding of BACE1 with four molecules are stable, except the loop regions show significant fluctuation. The binding free energy analyses reveal that NB360 exhibits the highest binding affinity with BACE1, surpassing other molecules (CNP520, D9W, and NB641). Detailed energy component assessments highlight the critical roles of electrostatic interactions and van der Waals forces in the binding process. Furthermore, residue contribution analysis identifies key amino acids influencing the binding process across all systems. Hydrogen bond analysis reveals a limited number of bonds between BACE1 and each small molecule, highlighting the importance of structural modifications to enable more stable hydrogen bonds. This research provides valuable insights into the molecular mechanisms of potential Alzheimer's disease therapeutics, guiding the way for improved drug design and the development of effective treatments targeting BACE1.Communicated by Ramaswamy H. Sarma.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Brazilin is a natural product inhibitor of the NLRP3 inflammasome

Excessive or aberrant NLRP3 inflammasome activation has been implicated in the progression and initiation of many inflammatory conditions; however, currently no NLRP3 inflammasome inhibitors have been approved for therapeutic use in the clinic. Here we have identified that the natural product brazilin effectively inhibits both priming and activation of the NLRP3 inflammasome in cultured murine macrophages, a human iPSC microglial cell line and in a mouse model of acute peritoneal inflammation. Through computational modeling, we predict that brazilin can adopt a favorable binding pose within a site of the NLRP3 protein which is essential for its conformational activation. Our results not only encourage further evaluation of brazilin as a therapeutic agent for NLRP3-related inflammatory diseases, but also introduce this small-molecule as a promising scaffold structure for the development of derivative NLRP3 inhibitor compounds.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

Exploring the interactions of antihistamine with retinoic acid receptor beta (RARB) by molecular dynamics simulations and genome-wide meta-analysis

Kaposi sarcoma (KS) is one of the most common AIDS-related malignant neoplasms, which can leave lesions on the skin among HIV patients. These lesions can be treated with 9-cis-retinoic acid (9-cis-RA), an endogenous ligand of retinoic acid receptors that has been FDA-approved for treatment of KS. However, topical application of 9-cis-RA can induce several unpleasant side effects, like headache, hyperlipidemia, and nausea. Hence, alternative therapeutics with less side effects are desirable. There are case reports associating over-the-counter antihistamine usage with regression of KS. Antihistamines competitively bind to H1 receptor and block the action of histamine, best known for being released in response to allergens. Furthermore, there are already dozens of antihistamines that are FDA-approved with less side effects than 9-cis-RA. This led our team to conduct a series of in-silico assays to determine whether antihistamines can activate retinoic acid receptors. First, we utilized high-throughput virtual screening and molecular dynamics simulations to model high-affinity interactions between antihistamines and retinoic acid receptor beta (RARβ). We then performed systems genetics analysis to identify a genetic association between H1 receptor itself and molecular pathways involved in KS. Together, these findings advocate for exploration of antihistamines against KS, starting with our two promising hit compounds, bepotastine and hydroxyzine, for experimental validation study in the future.

Computational Tool to Design Small Synthetic Inhibitors Selective for XIAP-BIR3 Domain

X-linked inhibitor of apoptosis protein (XIAP) exercises its biological function by locking up and inhibiting essential caspase-3, -7 and -9 toward apoptosis execution. It is overexpressed in multiple human cancers, and it plays an important role in cancer cells' death skipping. Inhibition of XIAP-BIR3 domain and caspase-9 interaction was raised as a promising strategy to restore apoptosis in malignancy treatment. However, XIAP-BIR3 antagonists also inhibit cIAP1-2 BIR3 domains, leading to serious side effects. In this study, we worked on a theoretical model that allowed us to design and optimize selective synthetic XIAP-BIR3 antagonists. Firstly, we assessed various MM-PBSA strategies to predict the XIAP-BIR3 binding affinities of synthetic ligands. Molecular dynamics simulations using hydrogen mass repartition as an additional parametrization with and without entropic term computed by the interaction entropy approach produced the best correlations. These simulations were then exploited to generate 3D pharmacophores. Following an optimization with a training dataset, five features were enough to model XIAP-BIR3 synthetic ligands binding to two hydrogen bond donors, one hydrogen bond acceptor and two hydrophobic groups. The correlation between pharmacophoric features and computed MM-PBSA free energy revealed nine residues as crucial for synthetic ligand binding: Thr308, Glu314, Trp323, Leu307, Asp309, Trp310, Gly306, Gln319 and Lys297. Ultimately, and three of them seemed interesting to use to improve XIAP-BR3 versus cIAP-BIR3 selectivity: Lys297, Thr308 and Asp309.

Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant

Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.

AmberMDrun: A Scripting Tool for Running Amber MD in an Easy Way

MD simulations have been widely applied and become a powerful tool in the field of biomacromolecule simulations and computer-aided drug design, etc., which can estimate binding free energy between receptor and ligand. However, the inputs and force field preparation for performing Amber MD is somewhat complicated, and challenging for beginners. To address this issue, we have developed a script for automatically preparing Amber MD input files, balancing the system, performing Amber MD for production, and predicting receptor-ligand binding free energy. This script is open-source, extensible and can support customization. The core code is written in C++ and has a Python interface, providing both efficient performance and convenience.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein-Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method

Computational alanine scanning with the molecular mechanics generalized Born surface area (MM/GBSA) method constitutes a widely used approach for identifying critical residues at protein-protein interfaces. Despite its popularity, the MM/GBSA method still has certain drawbacks due to its dependence on many factors. Here, we performed a systematical study on the impact of four different parameters, namely, the internal dielectric constant, the generalized Born model, the entropic term, and the inclusion of structural waters on the accuracy of computational alanine scanning calculations with the MM/GBSA method. Our results show that the internal dielectric constant is the most critical parameter for getting accurate predictions. The introduction of entropy and interfacial water molecules decreased the quality of the predictions, while the generalized Born model had little to no effect. Considering the significance of the internal dielectric value, we proposed a methodology based on the energetic predominance of a particular set of amino acids at the protein-protein interface for selecting an appropriate value for this variable. We hope that these results serve as a guideline for future studies of protein-protein complexes using the MM/GBSA method.

The vitamin D receptor as a potential target for the toxic effects of per- and polyfluoroalkyl substances (PFASs): An in-silico study

Due to their persistence and toxicity, perfluoroalkyl and polyfluoroalkyl substances (PFASs) constitute significant hazards to human health and the environment. Their effects include immune suppression, altered hormone levels, and osteoporosis. Recently, the most studied PFAS, perfluorooctanoic acid (PFOA), was shown to competitively binding to the Vitamin D receptor (VDR). VDR plays a crucial role in regulating genes involved in maintaining immune, endocrine, and calcium homeostasis, suggesting it may be a target for at least some of the health effects of PFAS. Hence, this study examined the potential binding of 5206 PFASs to VDR using molecular docking, molecular dynamics, and free energy binding calculations. We identified 14 PFAS that are predicted to interact strongly with VDR, similar to the natural ligands. We further investigated the interactions of VDR with 256 PFASs of established commercial importance. Eighty-three (32%) of these 256 commercially important PFAS were predicted to be stronger binders to VDR than PFOA. At least 16 PFASs of regulatory importance, because they have been identified in water supplies and human blood samples, were also more potent binders to VDR than PFOA. Further, PFASs are usually found together in contaminated drinking water and human blood samples, which raises the concern that multiple PFASs may act together as a mixture on VDR function, potentially producing harmful effects on the immune, endocrine, and bone homeostasis.

Ligands-induced open-close conformational change during DapE catalysis: Insights from molecular dynamics simulations

The microbial enzyme DapE plays a critical role in the lysine biosynthetic pathway and is considered as a potentially safe antibiotic target. In this study, atomistic simulations are employed to identify the modes of essential dynamics that define the conformational response of the enzyme to ligand binding and unbinding. The binding modes and the binding affinities of the products to the DapE enzyme are estimated from the MM-PBSA method, and the residues contributing to the ligand binding are identified. Various structural analyses and the principal component analysis of the molecular dynamics trajectories reveal that the removal of products from the active site causes a significant change in the overall enzyme structure. Both Cartesian and dihedral principal component analyses are used to characterize the structural changes in terms of domain unfolding and domain twisting motions. In the most dominant mode, that is, the domain unfolding motion, the two catalytic domains move away from the two dimerization domains of the dimeric enzyme, representing a closed-to-open conformational change. The conformational changes are initiated by the coordinated movement of three loops (Asp75-Pro82, Gly240-Asn244, and Thr347-Glu353) that trigger a domain-level movement. From multiple short trajectories, the time constant associated with the domain opening motion is estimated as 43.6 ns. Physiologically, this close-to-open conformational change is essential for the regeneration of the initial state of the enzyme for the subsequent cycle of catalytic action and provides the apo enzyme enough flexibility for efficient substrate binding.

SARS-CoV-2 and MERS-CoV Spike Protein Binding Studies Support Stable Mimic of Bound 9-O-Acetylated Sialic Acids

Many disease-causing viruses target sialic acids (Sias), a class of nine-carbon sugars known to coat the surface of many cells, including those in the lungs. Human beta coronaviridae, known for causing respiratory tract diseases, often bind Sias, and some preferentially bind to those with 9-O-Ac-modification. Currently, co-binding of SARS-CoV-2, a beta coronavirus responsible for the COVID-19 pandemic, to human Sias has been reported and its preference towards α2-3-linked Neu5Ac has been shown. Nevertheless, O-acetylated Sias-protein binding studies are difficult to perform, due to the ester lability. We studied the binding free energy differences between Neu5,9Ac2α2-3GalβpNP and its more stable 9-NAc mimic binding to SARS-CoV-2 spike protein using molecular dynamics and alchemical free energy simulations. We identified multiple Sia-binding pockets, including two novel sites, with similar binding affinities to those of MERS-CoV, a known co-binder of sialic acid. In our binding poses, 9-NAc and 9-OAc Sias bind similarly, suggesting an experimentally reasonable mimic to probe viral mechanisms.

Glucocorticoids Bind to SARS-CoV-2 S1 at Multiple Sites Causing Cooperative Inhibition of SARS-CoV-2 S1 Interaction With ACE2

Dexamethasone may reduce mortality in COVID-19 patients. Whether dexamethasone or endogenous glucocorticoids, such as cortisol, biochemically interact with SARS-CoV-2 spike 1 protein (S1), or its cellular receptor ACE2, is unknown. Using molecular dynamics (MD) simulations and binding energy calculations, we identified 162 druggable pockets in various conformational states of S1 and all possible binding pockets for cortisol and dexamethasone. Through biochemical binding studies, we confirmed that cortisol and dexamethasone bind to S1. Limited proteolysis and mass spectrometry analyses validated several MD identified binding pockets for cortisol and dexamethasone on S1. Interaction assays indicated that cortisol and dexamethasone separately and cooperatively disrupt S1 interaction with ACE2, through direct binding to S1, without affecting ACE2 catalytic activity. Cortisol disrupted the binding of the mutant S1 Beta variant (E484K, K417N, N501Y) to ACE2. Delta and Omicron variants are mutated in or near identified cortisol-binding pockets in S1, which may affect cortisol binding to them. In the presence of cortisol, we find increased inhibition of S1 binding to ACE2 by an anti-SARS-CoV-2 S1 human chimeric monoclonal antibody against the receptor binding domain. Whether glucocorticoid/S1 direct interaction is an innate defence mechanism that may have contributed to mild or asymptomatic SARS-CoV-2 infection deserves further investigation.

Vandetanib drives growth arrest and promotes sensitivity to imatinib in chronic myeloid leukemia by targeting ephrin type-B receptor 4

The oncogenic role of ephrin type‐B receptor 4 (EPHB4) has been reported in many types of tumors, including chronic myeloid leukemia (CML). Here, we found that CML patients have a higher EPHB4 expression level than healthy subjects. EPHB4 knockdown inhibited growth of K562 cells (a human immortalized myelogenous leukemia cell line). In addition, transient transfection of EPHB4 siRNA led to sensitization to imatinib. These growth defects could be fully rescued by EPHB4 transfection. To identify an EPHB4‐specific inhibitor with the potential of rapid translation into the clinic, a pool of clinical compounds was screened and vandetanib was found to be most sensitive to K562 cells, which express a high level of EPHB4. Vandetanib mainly acts on the intracellular tyrosine kinase domain and interacts stably with a hydrophobic pocket. Furthermore, vandetanib downregulated EPHB4 protein via the ubiquitin‐proteasome pathway and inhibited PI3K/AKT and MAPK/ERK signaling pathways in K562 cells. Vandetanib alone significantly inhibited tumor growth in a K562 xenograft model. Furthermore, the combination of vandetanib and imatinib exhibited enhanced and synergistic growth inhibition against imatinib‐resistant K562 cells in vitro and in vivo. These findings suggest that vandetanib drives growth arrest and overcomes the resistance to imatinib in CML via targeting EPHB4.

A colorimetric sandwich-type bioassay for SARS-CoV-2 using a hACE2-based affinity peptide pair

The metallopeptidase angiotensin-converting enzyme 2 (ACE2) is the SARS-CoV-2 receptor required for viral entry based on its specific recognition of the spike protein receptor binding domain (S_{_RBD}) on SARS-CoV-2. We constructed a human ACE2 (hACE2)-based peptide pair by ligating discontinuous key residues involved in the hACE2-S_{_RBD} interaction. We firstly performed in silico simulations to identify a 12-mer and 15-mer peptide pair with capability to bind to the SARS-CoV-2 S_{_RBD} via different binding sites. Then, the bio-layer interferometry validated the specific interactions between the peptides and S_{_RBD}, with affinities at the nanomolar level. Lastly, we developed a colorimetric sandwich-type bioassay based on S_{_RBD}-specific peptide-modified gold nanoparticles and found the colorimetric bioassay offered fast (<30 min), simple, and sensitive detection of S_{_RBD} protein at levels as low as 0.01 nM (0.26 ng mL^-1) in SARS-CoV-2. The linear signals ranging from 10⁵ to 10⁷ virus copies mL^-1 were achieved in typical types of environmental waters spiked with lysed SARS-CoV-2 pseudovirus. The technology can serve as a beneficial supplement to the routine virus nucleic acid detection in environment media and wastewater treatment.

An entropy driven catalytic reaction powered DNA motor for simultaneous detection of ochratoxin A and chloramphenicol in food

An entropy driven catalytic reaction powered DNA motor was proposed for simultaneous detection of ochratoxin A (OTA) and chloramphenicol (CAP) in food. The dumbbell hairpin structure was formed by the two aptamers of OTA and CAP. The dumbbell hairpin can be opened by the interaction of OTA and CAP with their aptamers. The tails of the end of dumbbell hairpin sequence can induce the entropy driven catalytic reactions on the AuNPs, causing the sustained releasing of the fluorophore labeled DNA sequences. The recovery of fluorescent intensities can be used for quantitative detection of OTA and CAP. The limit of detection reached 2 pM for OTA and 6 pM for CAP respectively, which was great improved by entropy driven amplification of the self-powdered DNA motor. This strategy is simple and sensitive and only needs one-step operation. It exhibits promising potentiality in food quality control and food security supervision.

Characterization of Amyloidogenic Peptide Aggregability in Helical Subspace

Prototypical amyloidogenic peptides amyloid-β (Aβ) and α-synuclein (αS) can undergo helix-helix associations via partially folded helical conformers, which may influence pathological progression to Alzheimer's (AD) and Parkinson's disease (PD), respectively. At the other extreme, stable folded helical conformers have been reported to resist self-assembly and amyloid formation. Experimental characterisation of such disparities in aggregation profiles due to subtle differences in peptide stabilities is precluded by the conformational heterogeneity of helical subspace. The diverse physical models used in molecular simulations allow sampling distinct regions of the phase space and are extensive in capturing the ensemble of rich helical subspace. Robust and powerful computational predictive methods utilizing network theory and free energy mapping can model the origin of helical population shifts in amyloidogenic peptides, which highlight their inherent aggregability. In this chapter, we discuss computational models, methods, design rules, and strategies to identify the driving force behind helical self-assembly and the molecular origin of aggregation resistance in helical intermediates of Aβ42 and αS. By extensive multiscale mapping of intrapeptide interactions, we show that the computational models can capture features that are otherwise imperceptible to experiments. Our models predict that targeting terminal residues may allow modulation and control of initial pathogenic aggregability of amyloidogenic peptides.

Synthesis and Biological Activity of Novel Antifungal Leads: 3,5-Dichlorobenzyl Ester Derivatives

Succinate dehydrogenase (SDH) is one of the most important molecular targets for the development of new fungicides. Carboxamide fungicides are a class of SDH inhibitors widely used to inhibit highly destructive plant pathogens. Although cases of resistance have been found in fungal pathogens due to the unrestricted use in recent years, there is still demand for new compounds with improved fungicidal activity. Therefore, a series of ester compounds were designed to investigate potential novel antifungal molecules. First, the antifungal activity of different benzyl alcohol compounds (A1-A21) was tested, and a highly active fragment (3,5-dichlorobenzyl alcohol) was found. Subsequently, various compounds were synthesized by esterification between different acids and 3,5-dichlorobenzyl alcohol, among which compound 5 exhibited remarkable antifungal activity against Botrytis cinerea and Rhizoctonia solani with EC₅₀ values of 6.60 and 1.61 mg/L, respectively, which were comparable to those of commercial fungicide boscalid (EC₅₀ = 1.24 and 1.01 mg/L). In vivo testing further demonstrated that compound 5 was effective in suppressing B. cinerea (200 mg/L, 50.9%). Moreover, SDH inhibition assays, fluorescence quenching analysis, and determination of mitochondrial membrane potential revealed that compound 5 has similar effects to boscalid. Furthermore, the fungicidal activity of target compounds can be maintained by modifying the amide bond to an ester bond. These results will provide basis for the development of novel fungicides.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation

This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R² of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.

Destabilization of the Alzheimer's amyloid-β peptide by a proline-rich β-sheet breaker peptide: a molecular dynamics simulation study

The amyloid-β peptide exists in the form of fibrils in the plaques found in the brains of patients with Alzheimer's disease. One of the therapeutic strategies is the design of molecules which can destabilize these fibrils. We present a designed peptide KLVFFP₅ with two segments: the self-recognition sequence KLVFF and a β-sheet breaker proline pentamer. Molecular dynamics simulations and docking results showed that this peptide could bind to the protofibrils and destabilize them by establishing hydrophobic contacts and hydrogen bonds with a higher affinity than the KLVFF peptide. In the presence of the KLVFFP₅ peptide, the β-sheet content of the protofibrils was reduced significantly; the hydrogen bonding network and the salt bridges were disrupted to a greater extent than the KLVFF peptide. Our results indicate that the KLVFFP₅ peptide is an effective β-sheet disruptor which can be considered in the therapy of Alzheimer's disease.

Vanillin enones as selective inhibitors of the cancer associated carbonic anhydrase isoforms IX and XII. The out of the active site pocket for the design of selective inhibitors?

New C-glycosides and α,β-unsaturated ketones incorporating the 4-hydroxy-3-methoxyphenyl (vanillin) moiety as inhibitors of carbonic anhydrase (CA, EC 4.2.1.1) isoforms have been investigated. The inhibition profile of these compounds is presented against four human CA (hCA) isozymes, comprising hCAs I and II (cytosolic, ubiquitous enzymes) and hCAs IX and XII (tumour associated isozymes). Docking analysis of the inhibitors within the active sites of these enzymes has been performed and is discussed, showing that the observed selectivity could be explained in terms of an alternative pocket out of the CA active site where some of these compounds may bind. Several derivatives were identified as selective inhibitors of the tumour-associated hCA IX and XII. Their discovery might be a step in the strategy for finding an effective non-sulfonamide CA inhibitor useful in therapy/diagnosis of hypoxic tumours or other pathologies in which CA isoforms are involved.

Potential colchicine binding site inhibitors unraveled by virtual screening, molecular dynamics and MM/PBSA

Microtubules have been widely studied in recent decades as an important pharmacological target for the treatment of cancer especially due to its key role in the mitosis process. Among the constituents of the microtubules, αβ-tubulin dimers stand out in view of their four distinct interaction sites, including the so-called colchicine binding site (CBS) - a promising target for the development of new tubulin modulators. When compared to other tubulin sites, targeting the CBS is advantageous because this site is able to host ligands with lower molecular volume and lipophilicity, thus reducing the chances of entailing the phenomenon of multiple drug resistance (MDR) - one of the main reasons of failure in chemotherapy. However, colchicine, the first ligand ever discovered with affinity towards the CBS, despite modulating the action of microtubules, has shown toxicity in clinical studies. Therefore, in order to expand the known chemical space of scaffolds capable of interacting with CBS and to design non-toxic colchicine binding site inhibitors, we conducted a robust virtual screening pipeline. This has been rigorously validated and consisted of ligand- and structure-based methodologies, which allowed us to select four promising CBS inhibitors called tubLCQF1-4. These four compounds were also evaluated with long trajectories molecular dynamics simulations and respective results were used for the theoretical determination of the free energy released in the formation of the complexes, using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) methodology.

Evolution of the interactions between GII.4 noroviruses and histo-blood group antigens: Insights from experimental and computational studies

Norovirus (NoV) is the major pathogen causing the outbreaks of the viral gastroenteritis across the world. Among the various genotypes of NoV, GII.4 is the most predominant over the past decades. GII.4 NoVs interact with the histo-blood group antigens (HBGAs) to invade the host cell, and it is believed that the receptor HBGAs may play important roles in selecting the predominate variants by the nature during the evolution of GII.4 NoVs. However, the evolution-induced changes in the HBGA-binding affinity for the GII.4 NoV variants and the mechanism behind the evolution of the NoV-HBGA interactions remain elusive. In the present work, the virus-like particles (VLPs) of the representative GII.4 NoV stains epidemic in the past decades were expressed by using the Hansenula polymorpha yeast expression platform constructed by our laboratory, and then the enzyme linked immunosorbent assay (ELISA)-based HBGA-binding assays as well as the molecular dynamics (MD) simulations combined with the molecular mechanics/generalized born surface area (MMGBSA) calculations were performed to investigate the interactions between various GII.4 strains and different types of HBGAs. The HBGA-binding assays show that for all the studied types of HBGAs, the evolution of GII.4 NoVs results in the increased NoV-HBGA binding affinities, where the early epidemic strains have the lower binding activity and the newly epidemic strains exhibit relative stronger binding intensity. Based on the MD simulation and MMGBSA calculation results, a physical mechanism that accounts for the increased HBGA-binding affinity was proposed. The evolution-involved residue mutations cause the conformational rearrangements of loop-2 (residues 390–396), which result in the narrowing of the receptor-binding pocket and thus tighten the binding of the receptor HBGAs. Our experimental and computational studies are helpful for better understanding the mechanism behind the evolution-induced increasing of HBGA-binding affinity, which may provide useful information for the drug and vaccine designs against GII.4 NoVs.

Human norovirus (NoV) has been recognized as the leading cause of the epidemic acute gastroenteritis worldwide and more than 50% acute gastroenteritis outbreaks are associated with NoVs. NoVs are highly infectious and may result in serious dehydration, malnutrition and even death, which severely threatens human health and brings heavy economic burden. NoVs are highly genetically diverse, in which the GII.4 genotype is the most predominant. The reported outbreaks of NoV infections have risen sharply from 2002, and it is suggested that the increasing NoV infections are attributed to the emergence of new strains with more infectiousness. GII.4 NoV evolves rapidly and on average every 2–3 years a new strain appears. It has been revealed that the histo-blood group antigens (HBGAs) serve as the recognition receptor for the GII.4 NoVs infecting the host cell, and the NoV-HBGA interactions may play an important role in selecting the predominate variants during the evolution of GII.4 NoVs. However, the molecular mechanism behind the evolution of the NoV-HBGA binding affinities is still not clear. In this work, the representative GII.4 NoV strains prevalent in the past decades were expressed, and the changes in the interactions between these strains and the receptor HBGAs were investigated by using the experimental measurements combined with computational simulations. Based on the experimental and computational results, a molecular mechanism that accounts for the increasing of the NoV-HBGA binding affinities during the evolution of GII.4 NoVs was proposed. Our studies are helpful for the understanding of the evolution mechanism of GII.4 NoVs and provide valuable information for the drug and vaccine designs against GII.4 NoVs.

Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations

Sirtuin 2 (Sirt2) nicotinamide adenine dinucleotide-dependent deacetylase enzyme has been reported to alter diverse biological functions in the cells and onset of diseases, including cancer, aging, and neurodegenerative diseases, which implicate the regulation of Sirt2 function as a potential drug target. Available Sirt2 inhibitors or modulators exhibit insufficient specificity and potency, and even partially contradictory Sirt2 effects were described for the available inhibitors. Herein, we applied computational screening and evaluation of FDA-approved drugs for highly selective modulation of Sirt2 activity via a unique inhibitory mechanism as reported earlier for SirReal2 inhibitor. Application of stringent molecular docking results in the identification of 48 FDA-approved drugs as selective putative inhibitors of Sirt2, but only top 10 drugs with docking scores > - 11 kcal/mol were considered in reference to SirReal2 inhibitor for computational analysis. The molecular dynamics simulations and post-simulation analysis of Sirt2-drug complexes revealed substantial stability for Fluphenazine and Nintedanib with Sirt2. Additionally, developed 3D-QSAR-models also support the inhibitory potential of drugs, which exclusively revealed highest activities for Nintedanib (pIC50 ≥ 5.90 µM). Conclusively, screened FDA-approved drugs were advocated as promising agents for Sirt2 inhibition and required in vitro investigation for Sirt2 targeted drug development.

Enhancing the binding of the β-sheet breaker peptide LPFFD to the amyloid-β fibrils by aromatic modifications: A molecular dynamics simulation study

Alzheimer's is a fatal neurodegenerative disease for which there is no cure at present. The disease is characterized by the presence of plaques in the brains of a patient, which are composed mainly of aggregates of the amyloid-β peptide in the form of β-sheet fibrils. Here, we investigated the possibility of exploiting the superior binding ability of aromatic amino acids to a particular model of the amyloid-β fibrils. which is a difficult target for drug design. The β-sheet breaker peptide LPFFD was modified with aromatic amino acids and its binding to these fibrils was studied. We found that the orientation and the electrostatic complementarity of the modified peptide with respect to the fibrils played a crucial role in determining whether its binding was improved by the aromatic amino acids. The modified LPFFD peptides were able to bind to those fibril residues. which are important in the aggregation of amyloid-β peptides and thus can potentially inhibit the further aggregation of the amyloid-beta peptides by blocking their interactions. We found that the tryptophan modified LPFFD peptides had the best binding affinities. In most cases, the aromatic amino acids in the N-terminus of the modified peptides made more contacts with the fibrils than those in the C-terminus. We also found that increasing the aromatic content did not significantly improve the binding of the LPFFD peptide to the fibrils. Our study can serve as a basis for the design of novel peptide-based drugs for Alzheimer's disease in which aromatic interactions play an important role.

Binding of azobenzene and p-diaminoazobenzene to the human voltage-gated sodium channel Nav1.4

The activity of voltage-gated ion channels can be controlled by the binding of photoswitches inside their internal cavity and subsequent light irradiation. We investigated the binding of azobenzene and p-diaminoazobenzene to the human Na_v1.4 channel in the inactivated state by means of Gaussian accelerated molecular dynamics simulations and free-energy computations. Three stable binding pockets were identified for each of the two photoswitches. In all the cases, the binding is controlled by the balance between the favorable hydrophobic interactions of the ligands with the nonpolar residues of the protein and the unfavorable polar solvation energy. In addition, electrostatic interactions between the ligand and the polar aminoacids are also relevant for p-diaminoazobenzene due to the presence of the amino groups on the benzene moieties. These groups participate in hydrogen bonding in the most favorable binding pocket and in long-range electrostatic interactions in the other pockets. The thermodinamically preferred binding sites found for both photoswitches are close to the selectivity filter of the channel. Therefore, it is very likely that the binding of these ligands will induce alterations in the ion conduction through the channel.

Prediction of Drug Potencies of BACE1 Inhibitors: A Molecular Dynamics Simulation and MM_GB(PB)SA Scoring

Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder. One of the important therapeutic approaches of AD is the inhibition of β-site APP cleaving enzyme-1 (BACE1). This enzyme plays a central role in the synthesis of the pathogenic β-amyloid peptides (Aβ) in Alzheimer’s disease. A group of potent BACE1 inhibitors with known X-ray structures (PDB ID 5i3X, 5i3Y, 5iE1, 5i3V, 5i3W, 4LC7, 3TPP) were studied by molecular dynamics simulation and binding energy calculation employing MM_GB(PB)SA. The calculated binding energies gave Kd values of 0.139 µM, 1.39 nM, 4.39 mM, 24.3 nM, 1.39 mM, 29.13 mM, and 193.07 nM, respectively. These inhibitors showed potent inhibitory activities in enzymatic and cell assays. The Kd values are compared with experimental values and the structures are discussed in view of the energy contributions to binding. Drug likeness of these inhibitors is also discussed. Accommodation of ligands in the catalytic site of BACE1 is discussed depending on the type of fragment involved in each structure. Molecular dynamics (MD) simulations and energy studies were used to explore the recognition of the selected BACE1 inhibitors by Asp32, Asp228, and the hydrophobic flap. The results show that selective BACE1 inhibition may be due to the formation of strong electrostatic interactions with Asp32 and Asp228 and a large number of hydrogen bonds, in addition to π–π and van der Waals interactions with the amino acid residues located inside the catalytic cavity. Interactions with the ligands show a similar binding mode with BACE1. These results help to rationalize the design of selective BACE1 inhibitors.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Improving the performance of the MM/PBSA and MM/GBSA methods in recognizing the native structure of the Bcl-2 family using the interaction entropy method

Correct discrimination of native structure plays an important role in drug design. IE method significantly improves the performance of MM/PB(GB)SA method in discriminating native and decoy structures in protein–ligand/protein systems of Bcl-2 family.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r_p = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r_p = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Erythromycin, Cethromycin and Solithromycin display similar binding affinities to the E. coli's ribosome: A molecular simulation study

Macrolide antibiotics bind to the exit tunnel of the ribosome and inhibit protein synthesis blocking its translocation. Thus, antibiotics including the known macrolide Erythromycin (ERY) are active against bacteria. However, at present, some bacteria show resistance to drugs, which requires the development of new powerful antibacterial agents. One possible way is to use the ERY structure, but change its side chains, while the size of the lactone ring can remain unchanged or change. In this work we consider Cethromycin (CET) and Solithromycin (SOL), which are ketolides with quinolylallyl group at C6 and aminophenyl at C11, respectively (both of them have the same lactone ring as ERY). Experiments have shown that these ketolides have improved efficacy against pathogens, but their binding affinity to the E. coli's ribosome is almost identical. To clarify this issue, we have studied in detail the binding mechanisms of ERY, CET and SOL using the docking and molecular dynamic simulations. In agreement with the experiments, we showed that these compounds have similar binding affinities. Desosamine and lactone ring groups play a critical role in the binding of ERY to the ribosome. In CET and SOL, the contribution of keto and alkylaryl groups is balanced by cyclic carbamate. We have demonstrated that increased fluctuations in the ribosomal residues at the binding site led to an increase in the entropic term in the free binding energy of ERY compared to SOL and CET. The alkyl-aryl arm of both ketolides strongly interacts with A752 and U2609. In addition, the presence of macrolides in the exit tunnel can alter the conformation of U2585, which is located in the peptidyl transferase center, through non-bonded interaction. Therefore, the side chain of ketolides affects not only the binding site but also other residues possibly leading to a strong effect on the protein synthesis process. We predict that to combat bacterial mutations, it is necessary either to design a bulk and charged group as a cladinose, or to use several groups with different signs of charges. This prediction can be used for the development of new efficient antibiotics.

Generalized Born Implicit Solvent Models for Biomolecules

It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.

Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge

Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.

L-Captopril and its derivatives as potential inhibitors of microbial enzyme DapE: A combined approach of drug repurposing and similarity screening

The perils of antimicrobial drug resistance can be overcome by finding novel antibiotic targets and corresponding small molecule inhibitors. Microbial enzyme DapE is a promising antibiotic target due to its importance to the bacterial survival. The potency of L-Captopril, a well known angiotensin-converting enzyme inhibitor, as an inhibitor of DapE enzyme has been evaluated by analyzing its binding modes and binding affinity towards DapE enzyme. L-Captopril is found to bind the metal centers of DapE enzyme either via its thiolate group or through its carboxylate group. While the latter binding mode is found to be thermodynamically favorable, the former binding mode, also seen in the crystal structure, is kinetically favored. To optimize the binding affinity of the inhibitor towards DapE enzyme, a series of L-Captopril-based inhibitors have been modelled by changing the side groups of L-Captopril. The introduction of a bipolar functional group at the C4 position of the pyrrolidine ring of L-Captopril and the substitution of the thiol group with a carboxylate group, have been shown to provide excellent enzyme affinity that supersedes the binding affinity of DapE enzyme towards its natural substrate, thus making this molecule a potential inhibitor with great promise.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

NIPS, a 3D network-integrated predictor of deleterious protein SAPs, and its application in cancer prognosis

Identifying deleterious mutations remains a challenge in cancer genome sequencing projects, reflecting the vast number of candidate mutations per tumour and the existence of interpatient heterogeneity. Based on a 3D protein interaction network profiled via large-scale cross-linking mass spectrometry, we propose a weighted average formula involving the combination of three types of information into a 'meta-score'. We assume that a single amino acid polymorphism (SAP) may have a deleterious effect if the mutation rarely occurs naturally during evolution, if it inhibits binding between a pair of interacting proteins when located at their interface, or if it plays an important role in a protein interaction (PPI) network. Cross-validation indicated that this new method presents an AUC value of 0.93 and outperforms other widely used tools. The application of this method to the CPTAC colorectal cancer dataset enabled the accurate identification of validated deleterious mutations and yielded insights into their potential pathogenesis. Survival analysis showed that the accumulation of deleterious SAPs is significantly associated with a poor prognosis. The new method provides an alternative method to identifying and ranking deleterious cancer SAPs based on a 3D PPI network and will contribute to the understanding of pathogenesis and the discovery of prognostic biomarkers.

Electrostatics Explains the Position-Dependent Effect of G⋅U Wobble Base Pairs on the Affinity of RNA Kissing Complexes

In the RNA realm, non-Watson-Crick base pairs are abundant and can affect both the RNA 3D structure and its function. Here, we investigated the formation of RNA kissing complexes in which the loop-loop interaction is modulated by non-Watson-Crick pairs. Mass spectrometry, surface plasmon resonance, and UV-melting experiments show that the G⋅U wobble base pair favors kissing complex formation only when placed at specific positions. We tried to rationalize this effect by molecular modeling, including molecular mechanics Poisson-Boltzmann surface area (MMPBSA) thermodynamics calculations and PBSA calculations of the electrostatic potential surfaces. Modeling reveals that the G⋅U stabilization is due to a specific electrostatic environment defined by the base pairs of the entire loop-loop region. The loop is not symmetric, and therefore the identity and position of each base pair matters. Predicting and visualizing the electrostatic environment created by a given sequence can help to design specific kissing complexes with high affinity, for potential therapeutic, nanotechnology or analytical applications.