Theory and applications of the generalized Born solvation model in macromolecular simulations

Chain stretching in brushes favors sequence recognition for nucleobase-functionalized flexible precise oligomers

Six different flexible stereocontrolled oligo(triazole-urethane)s substituted by precise sequences of nucleobases or analogs are synthesized. Molecular dynamics simulations indicate that the flexibility of the backbone leads to unspecific complexation of pairs of oligomers, irrespective of the complementarity of their sequences. This is ascribed to the existence of other interactions between pairs of oligomers, as well as to the spatial blurring of the sequence order encoded in the chemical structure of the chain due to its flexibility. The same conclusions are drawn when investigating the irreversible adsorption of different probe oligomers onto a layer of target oligomers grafted by click chemistry in a mushroom configuration on a silicon substrate. In contrast, when the target oligomers are grafted in denser brush configurations, irreversible adsorption becomes more specific, with it being twice as probable that probe chains of complementary sequence would be irreversibly-bound to the layer of target chains than those of non-complementary sequence. This is ascribed to lateral excluded volume interactions between chains in the brush, leading to partial chain stretching and increased spatial preservation of the information contained in the monomer sequence of the chains. At even higher grafting densities, however, the penetration of the probe chains in the brush becomes increasingly difficult, resulting in a loss of binding efficiency. Our work thus demonstrates the adverse role of chain flexibility in the specificity of complexation between nucleobase-functionalized oligomers and provides directions for an improvement of specificity by tuning the grafting density of target chains on a substrate.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Mechanism of 7H-Dibenzo[c,g]carbazole metabolism in cytochrome P450 1A1: Insights from computational studies

7H-Dibenzo[c,g]carbazole (DBC) is a prevalent environmental contaminant that induces tumorigenesis in several experimental animals. Recently, it has been utilized to develop high-performance solar cells and organic phosphorescent materials. It is imperative to strengthen investigations of DBC metabolism to understand its potential risks to human health. In this study, human CYP1A1 was employed as the metabolic enzyme to investigate the metabolic mechanism of DBC by molecular docking, molecular dynamics (MD) simulation, and quantum mechanical (QM) calculation. The results indicate that DBC binds to CYP1A1 in two modes (mode 1 and mode 2) mainly through nonpolar solvation energies (ΔGnonpolar). The formation of the two binding modes is attributed to the anchoring effect of the hydrogen bond formed by DBC with Asp320 (mode 1) or Ser116 (mode 2). Mode 1 is a "reactive" conformation, while mode 2 is not considered a "reactive" conformation. C5 is identified as the dominant site, and the pyrrole nitrogen cannot participate in the metabolism. DBC is metabolized mainly by a distinct electrophilic addition-rearrangement mechanism, with an energy barrier of 21.74 kcal/mol. The results provide meaningful insights into the biometabolic process of DBC and contribute to understanding its environmental effects and health risks.

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

Molecular basis for differential recognition of an allosteric inhibitor by receptor tyrosine kinases

Understanding kinase-inhibitor selectivity continues to be a major objective in kinase drug discovery. We probe the molecular basis of selectivity of an allosteric inhibitor (MSC1609119A-1) of the insulin-like growth factor-I receptor kinase (IGF1RK), which has been shown to be ineffective for the homologous insulin receptor kinase (IRK). Specifically, we investigated the structural and energetic basis of the allosteric binding of this inhibitor to each kinase by combining molecular modeling, molecular dynamics (MD) simulations, and thermodynamic calculations. We predict the inhibitor conformation in the binding pocket of IRK and highlight that the charged residues in the histidine-arginine-aspartic acid (HRD) and aspartic acid-phenylalanine-glycine (DFG) motifs and the nonpolar residues in the binding pocket govern inhibitor interactions in the allosteric pocket of each kinase. We suggest that the conformational changes in the IGF1RK residues M1054 and M1079, movement of the ⍺C-helix, and the conformational stabilization of the DFG motif favor the selectivity of the inhibitor toward IGF1RK. Our thermodynamic calculations reveal that the observed selectivity can be rationalized through differences observed in the electrostatic interaction energy of the inhibitor in each inhibitor/kinase complex and the hydrogen bonding interactions of the inhibitor with the residue V1063 in IGF1RK that are not attained with the corresponding residue V1060 in IRK. Overall, our study provides a rationale for the molecular basis of recognition of this allosteric inhibitor by IGF1RK and IRK, which is potentially useful in developing novel inhibitors with improved affinity and selectivity.

A theoretical study on the activity and selectivity of IDO/TDO inhibitors

Indoleamine 2,3-dioxygenase 1 (IDO) is a tryptophan (Trp) metabolic enzyme along the kynurenine (NFK) pathway. Under pathological conditions, IDO overexpressed by tumor cells causes depletion of tryptophan and the accumulation of metabolic products, which inhibit the local immune response and form immune escape. Therefore, the suppression of IDO activity is one of the strategies for tumor immunotherapy, and drug design for this target has been the focus of research for more than two decades. Apart from IDO, tryptophan dioxygenase (TDO) of the same family can also catalyze the same biochemical reaction in the human body, but it has different tissue distribution and substrate selectivity from IDO. Based on the principle of drug design with high potency and low cross-reactivity to specific targets, in this subject, the activity and selectivity of IDO and TDO toward small molecular inhibitors were studied from the perspective of thermodynamics and kinetics. The aim was to elucidate the structural requirements for achieving favorable biological activity and selectivity of IDO and TDO inhibitors. Specifically, the interactions of inhibitors from eight families with IDO and TDO were initially investigated through molecular docking and molecular dynamics simulations, and the thermodynamic data for binding of inhibitors were predicted by the molecular mechanics/generalized Born surface area (MM/GBSA) method. Secondly, we explored the free energy landscape of JKloops, the kinetic control element of IDO/TDO, using temperature replica exchange molecular dynamics (T-REMD) simulations and elucidated the connection between the rules of IDO/TDO conformational changes and the inhibitor selectivity mechanism. Furthermore, the binding and dissociation processes of the C1 inhibitor (NLG919) were simulated by the adaptive steering molecular dynamics (ASMD) method, which not only addressed the possible stable, metastable, and transition states for C1 inhibitor-IDO/TDO interactions, but also accurately predicted kinetic data for C1 inhibitor binding and dissociation. In conclusion, we have constructed a complete process from enzyme (IDO/TDO) conformational activation to inhibitor binding/dissociation and used the thermodynamic and kinetic data of each link as clues to verify the control mechanism of IDO/TDO on inhibitor selectivity. This is of great significance for us to understand the design principles of tumor immunotherapy drugs and to avoid drug resistance of immunotherapy drugs.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Structural, computational and in silico studies of 4-bromo-3-flurobenzonitrile as anti-Alzheimer and anti-Parkinson agents

A novel dimer of the 4-bromo-3-fluorobenzonitrile was crystallized and studied using a spectroscopic method such as the scanning electron microscope method. The computational simulations substantiated the structural analysis findings. The Hirshfeld surface analysis has been performed for visualizing, exploring and quantifying the intra and inter-molecular interactions that stabilize the crystal packing of the compound. The NBO and QTAIM analyses were applied to study the nature and origin of the attractive forces involved in the crystal structure. Further, the pharmacokinetic properties of the compound were evaluated, indicating good brain-blood barrier and central nervous system penetration capability. Hence, in silico studies was carried out to explore the binding pattern of the titled compound against acetylcholinesterase and butyrylcholinesterase, and tumor necrosis factor-alpha converting enzyme proteins using molecular docking and molecular dynamics simulations approach. Further, the titled compound is compared with standard drugs through molecular docking studies. The in silico studies finally predicts that the compound under investigation may act as a good inhibitor for treating Alzheimer's disease and further in vitro and in vivo studies may provide its therapeutic potential.Communicated by Ramaswamy H. Sarma.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase

The allosteric inhibition of insulin-like growth factor receptor 1 kinase (IGF1RK) is a potential strategy to overcome selectivity barriers for targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives that have been reported as allosteric inhibitors of IGF1RK. We further studied the dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation: forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen-bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the positioning of chemical substituents with different pharmacophore features at the R1 indole ring influences molecular interactions and binding conformations of indole-butyl-amine derivatives and, hence, dramatically affects their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase

The allosteric inhibition of Insulin-like Growth Factor Receptor 1 Kinase (IGF1RK) is a potential strategy to overcome selectivity barriers in targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives which have been reported as allosteric inhibitors of IGF1RK. We further studied dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation, forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the position of chemical substituents at the R1 indole ring with different pharmacophore features influences molecular interactions and binding conformations of the indole-butyl-amine derivatives, hence dramatically affecting their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

Comprehensive Analysis of Coupled Proline Cis-Trans States in Bradykinin Using ωBP-REMD Simulations

It is well-known that proline (Pro) cis-trans isomerization plays a decisive role in the folding and stabilization of proteins. The conformational coupling between isomerization states of different Pro residues in proteins during conformational adaptation processes is not well understood. In the present work, we investigate the coupled cis-trans isomerization of three Pro residues using bradykinin (BK), a partially unstructured nonapeptide hormone, as a model system. We use a recently developed enhanced-sampling molecular dynamics method (ω-bias potential replica exchange molecular dynamics; ωBP-REMD) that allows us to exhaustively sample all combinations of Pro isomer states and obtain converged probability densities of all eight state combinations within 885 ns ωBP-REMD simulations. In agreement with experiment, the all-trans state is seen to be the preferred isomer of zwitterionic aqueous BK. In about a third of its structures, this state presents the characteristic C-terminal β-turn conformation; however, other isomer combinations also contribute significantly to the structural ensemble. Unbiased probabilities can be projected onto the peptide bond dihedral angles of the three Pro residues. This unveils the interdependence of the individual Pro isomerization states, i.e., a possible coupling of the different Pro isomers. The cis/trans equilibrium of a Pro residue can change by up to 2.5 kcal·mol-1, depending on the isomerization state of other Pro residues. For example, for Pro7, the simulations indicate that its cis state becomes favored compared to its trans state when Pro2 is switched from the trans state to the cis state. Our findings demonstrate the efficiency of the ωBP-REMD methodology and suggest that the coupling of Pro isomerization states may play an even more decisive role in larger folded proteins subject to more conformational restraints.

The Three-Dimensional Reference Interaction Site Model Approach as a Promising Tool for Studying Hydrated Viruses and Their Complexes with Ligands

Viruses are the most numerous biological form living in any ecosystem. Viral diseases affect not only people but also representatives of fauna and flora. The latest pandemic has shown how important it is for the scientific community to respond quickly to the challenge, including critically assessing the viral threat and developing appropriate measures to counter this threat. Scientists around the world are making enormous efforts to solve these problems. In silico methods, which allow quite rapid obtention of, in many cases, accurate information in this field, are effective tools for the description of various aspects of virus activity, including virus-host cell interactions, and, thus, can provide a molecular insight into the mechanism of virus functioning. The three-dimensional reference interaction site model (3D-RISM) seems to be one of the most effective and inexpensive methods to compute hydrated viruses, since the method allows us to provide efficient calculations of hydrated viruses, remaining all molecular details of the liquid environment and virus structure. The pandemic challenge has resulted in a fast increase in the number of 3D-RISM calculations devoted to hydrated viruses. To provide readers with a summary of this literature, we present a systematic overview of the 3D-RISM calculations, covering the period since 2010. We discuss various biophysical aspects of the 3D-RISM results and demonstrate capabilities, limitations, achievements, and prospects of the method using examples of viruses such as influenza, hepatitis, and SARS-CoV-2 viruses.

(2-Cyclohexyl-1-methylpropyl) cyclohexane isolated from garlic extract exhibits antidepressant-like activity: extraction, docking, drug-like properties, molecular dynamics simulations and MM/GBSA studies

Depressive disorders are among most common psychiatric diseases and second most common form of psychiatric illness globally. Commonly available chemical drugs used for treatment of nervous system disorders exert undesirable effects. Therefore, there is a growing need towards exploring novel antidepressants of herbal origin. Earlier, the antidepressant effect of methanolic extract of garlic has been shown. In this study, the ethanolic extract of garlic was prepared and chemically analysed using Gas Chromatography - Mass Spectrometry (GC-MS) screening. A total of 35 compounds were found to be present, which might act as antidepressant. Using computational analyses, these compounds were screened as potential inhibitors (selective serotonin reuptake inhibitor (SSRI)) against serotonin transporter (SERT)/leucine receptor (LEUT). In silico docking studies and other physicochemical, bioactivity and ADMET studies resulted in the selection of compound 1 ((2-Cyclohexyl-1-methylpropyl) cyclohexane) as potential SSRI (binding energy -8.1 kcal/mol) compared to known reference SSRI fluoxetine (binding energy -8.0 kcal/mol). Analysis of conformational stability, residue flexibility, compactness, binding interactions, solvent accessible surface area (SASA), dynamic correlation, and binding free energy predicted from molecular mechanics (MD) with generalised Born and surface area solvation (MM/GBSA) studies revealed formation of a more stable SSRI like complex with compound 1 having strong inhibitory interaction compared to known SSRI fluoxetine/reference complex. Thus, compound 1 may act as an active SSRI leading to discovery of potential antidepressant drug.Communicated by Ramaswamy H. Sarma.

Assessment of the bound conformation of bombesin to the BB1 and BB2 receptors

Bombesin is an endogenous peptide involved in a wide spectrum of physiological activities ranging from satiety, control of circadian rhythm and thermoregulation in the central nervous system, to stimulation of gastrointestinal hormone release, activation of macrophages and effects on development in peripheral tissues. Actions of the peptide are mediated through the two high affinity G-protein coupled receptors BB1R and BB2R. Under pathophysiological conditions, these receptors are overexpressed in many different types of tumors, such as prostate cancer, breast cancer, small and non-small cell lung cancer and pancreatic cancer. This observation has been used for designing cell markers, but it has not been yet exploited for therapeutical purposes. Despite the enormous biological interest of the peptide, little is known about the stereochemical features that contribute to their activity. On the one hand, mutagenesis studies identified a few receptor residues important for high bombesin affinity and on the other, a few studies focused on the relevance of diverse residues of the peptide for receptor activation. Models of the peptide bound to BB1R and BB2R can be helpful to improve our understanding of the stereochemical features granting bombesin activity. Accordingly, the present study describes the computational process followed to construct such models by means of Steered Molecular Dynamics, using models of the peptide and its receptors. Present results provide new insights into the structure-activity relationships of bombesin and its receptors, as well as render an explanation for the differential binding affinity observed towards BB1R and BB2R. Finally, these models can be further exploited to help for designing novel small molecule peptidomimetics with improved pharmacokinetics profile.

Elucidating TH301's influence on the torsion angle of CRY1 W399 using replica exchange with solute tempering (REST) molecular dynamics (MD) simulations

Cryptochrome 1 (CRY1) is a protein involved in the circadian clock and associated with various diseases. Targeting CRY1 for drug development requires the discovery of competitive inhibitors that target its FAD binding site through ubiquitination. During the development of compounds to regulate CRY1, an intriguing compound called TH301 was identified. Despite binding to CRY1, TH301 does not induce the expected reaction and is considered an inactive compound. However, it has been observed that TH301 affects the torsion angle of CRY1's W399 residue, which plays a crucial role in the regulation of ubiquitination by influencing the movement of the lid loop. In our research, we aimed to understand how TH301 induces the torsion angle of CRY1's W399 to shift to an "out-form" by performing REST-based MD simulations. The cyclopentane of TH301 tends to align parallel with W292, creating a repulsive force when W399 is in the "in-form", leading to a flip. In the "out-form", W399's side chain interacts with TH301's chlorobenzene through a π-π interaction, stabilizing this pose. This analysis helps identify compounds binding to CRY1 and filter out inactive ones. We found that assessing the interaction energy between TH301 and W399 is crucial to evaluate whether W399 flips or not. These findings contribute to the development of drugs targeting CRY1 and enhance our understanding of its regulatory mechanisms.

A sulfonamide chalcone inhibited dengue virus with a potential target at the SAM-binding site of viral methyltransferase

Dengue infection is a global health problem as climate change facilitates the spread of mosquito vectors. Infected patients could progress to severe plasma leakage and hemorrhagic shock, where current standard treatment remains supportive. Previous reports suggested that several flavonoid derivatives inhibited mosquito-borne flaviviruses. This work aimed to explore sulfonamide chalcone derivatives as dengue inhibitors and to identify molecular targets. We initially screened 27 sulfonamide chalcones using cell-based antiviral and cytotoxic screenings. Two potential compounds, SC22 and SC27, were identified with DENV1-4 EC50s in the range of 0.71-0.94 and 3.15-4.46 μM, and CC50s at 14.63 and 31.02 μM, respectively. The compounds did not show any elevation in ALT or Cr in C57BL/6 mice on the 1st, 3rd, and 7th days after being administered intraperitoneally with 50 mg/kg SC22 or SC27 in a single dose. Moreover, the SAM-binding site of NS5 methyltransferase was a potential target of SC27 identified by computational and enzyme-based assays. The main target of SC22 was in a late stage of viral replication, but the exact target molecule had yet to be identified. In summary, a sulfonamide chalcone, SC27, was a potential DENV inhibitor that targeted viral methyltransferase. Further investigation should be the study of the structure-activity relationship of SC27 derivatives for higher potency and lower toxicity.

Application of the weighted histogram method for calculating the thermodynamic parameters of the formation of oligodeoxyribonucleotide duplexes

To date, many derivatives and analogs of nucleic acids (NAs) have been developed. Some of them have found uses in scientific research and biomedical applications. Their effective use is based on the data about their properties. Some of the most important physicochemical properties of oligonucleotides are thermodynamic parameters of the formation of their duplexes with DNA and RNA. These parameters can be calculated only for a few NA derivatives: locked NAs, bridged oligonucleotides, and peptide NAs. Existing predictive approaches are based on an analysis of experimental data and the consequent construction of predictive models. The ongoing pilot studies aimed at devising methods for predicting the properties of NAs by computational modeling techniques are based only on knowledge about the structure of oligonucleotides. In this work, we studied the applicability of the weighted histogram analysis method (WHAM) in combination with umbrella sampling to the calculation of thermodynamic parameters of DNA duplex formation (changes in enthalpy ΔH°, entropy ΔS°, and Gibbs free energy ΔG° 37). A procedure was designed involving WHAM for calculating the hybridization properties of oligodeoxyribonucleotides. Optimal parameters for modeling and calculation of thermodynamic parameters were determined. The feasibility of calculation of ΔH°, ΔS°, and ΔG° 37 was demonstrated using a representative sample of 21 oligonucleotides 4-16 nucleotides long with a GC content of 14-100 %. Error of the calculation of the thermodynamic parameters was 11.4, 12.9, and 11.8 % for ΔH°, ΔS°, and ΔG° 37, respectively, and the melting temperature was predicted with an average error of 5.5 °C. Such high accuracy of computations is comparable with the accuracy of the experimental approach and of other methods for calculating the energy of NA duplex formation. In this paper, the use of WHAM for computation of the energy of DNA duplex formation was systematically investigated for the first time. Our results show that a reliable calculation of the hybridization parameters of new NA derivatives is possible, including derivatives not yet synthesized. This work opens up new horizons for a rational design of constructs based on NAs for solving problems in biomedicine and biotechnology.

Computational identification of potential inhibitors targeting cdk1 in colorectal cancer

Introduction: Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Methods: Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Results: Four hit compounds, namely, spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. Discussion: The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.

Differentiable rotamer sampling with molecular force fields

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

In vitro generated antibodies guide thermostable ADDomer nanoparticle design for nasal vaccination and passive immunization against SARS-CoV-2

Background

Due to COVID-19, pandemic preparedness emerges as a key imperative, necessitating new approaches to accelerate development of reagents against infectious pathogens.

Methods

Here, we developed an integrated approach combining synthetic, computational and structural methods with in vitro antibody selection and in vivo immunization to design, produce and validate nature-inspired nanoparticle-based reagents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Results

Our approach resulted in two innovations: (i) a thermostable nasal vaccine called ADDoCoV, displaying multiple copies of a SARS-CoV-2 receptor binding motif derived epitope and (ii) a multivalent nanoparticle superbinder, called Gigabody, against SARS-CoV-2 including immune-evasive variants of concern (VOCs). In vitro generated neutralizing nanobodies and electron cryo-microscopy established authenticity and accessibility of epitopes displayed by ADDoCoV. Gigabody comprising multimerized nanobodies prevented SARS-CoV-2 virion attachment with picomolar EC50. Vaccinating mice resulted in antibodies cross-reacting with VOCs including Delta and Omicron.

Conclusion

Our study elucidates Adenovirus-derived dodecamer (ADDomer)-based nanoparticles for use in active and passive immunization and provides a blueprint for crafting reagents to combat respiratory viral infections.

Combined Antibodies Evusheld against the SARS-CoV-2 Omicron Variants BA.1.1 and BA.5: Immune Escape Mechanism from Molecular Simulation

The Omicron lineage of SARS-CoV-2, which was first reported in November 2021, has spread globally and become dominant, splitting into several sublineages. Experiments have shown that Omicron lineage has escaped or reduced the activity of existing monoclonal antibodies, but the origin of escape mechanism caused by mutation is still unknown. This work uses molecular dynamics and umbrella sampling methods to reveal the escape mechanism of BA.1.1 to monoclonal antibody (mAb) Tixagevimab (AZD1061) and BA.5 to mAb Cilgavimab (AZD8895), both mAbs were combined to form antibody cocktail, Evusheld (AZD7442). The binding free energy of BA.1.1-AZD1061 and BA.5-AZD8895 has been severely reduced due to multiple-site mutated Omicron variants. Our results show that the two Omicron variants, which introduce a substantial number of positively charged residues, can weaken the electrostatic attraction between the receptor binding domain (RBD) and AZD7442, thus leading to a decrease in affinity. Additionally, using umbrella sampling along dissociation pathway, we found that the two Omicron variants severely impaired the interaction between the RBD of SARS-CoV-2's spike glycoprotein (S protein) and complementary determining regions (CDRs) of mAbs, especially in CDR3H. Although mAbs AZD8895 and AZD1061 are knocked out by BA.5 and BA.1.1, respectively, our results confirm that the antibody cocktail AZD7442 retains activity against BA.1.1 and BA.5 because another antibody is still on guard. The study provides theoretical insights for mAbs interacting with BA.1.1 and BA.5 from both energetic and dynamic perspectives, and we hope this will help in developing new monoclonals and combinations to protect those unable to mount adequate vaccine responses.

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

Lysosomal dysfunction in Down syndrome and Alzheimer mouse models is caused by v-ATPase inhibition by Tyr682-phosphorylated APP βCTF

Lysosome dysfunction arises early and propels Alzheimer's disease (AD). Herein, we show that amyloid precursor protein (APP), linked to early-onset AD in Down syndrome (DS), acts directly via its β-C-terminal fragment (βCTF) to disrupt lysosomal vacuolar (H+)-adenosine triphosphatase (v-ATPase) and acidification. In human DS fibroblasts, the phosphorylated 682YENPTY internalization motif of APP-βCTF binds selectively within a pocket of the v-ATPase V0a1 subunit cytoplasmic domain and competitively inhibits association of the V1 subcomplex of v-ATPase, thereby reducing its activity. Lowering APP-βCTF Tyr682 phosphorylation restores v-ATPase and lysosome function in DS fibroblasts and in vivo in brains of DS model mice. Notably, lowering APP-βCTF Tyr682 phosphorylation below normal constitutive levels boosts v-ATPase assembly and activity, suggesting that v-ATPase may also be modulated tonically by phospho-APP-βCTF. Elevated APP-βCTF Tyr682 phosphorylation in two mouse AD models similarly disrupts v-ATPase function. These findings offer previously unknown insight into the pathogenic mechanism underlying faulty lysosomes in all forms of AD.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Inhibition of Human Cholinesterases and in vitro β-Amyloid Aggregation by Rationally Designed Peptides

The multifactorial nature of Alzheimer's disease (AD) is now widely recognized, which has increased the interest in compounds that can address more than one AD-associated targets. Herein, we report the inhibitory activity on the human cholinesterases (acetylcholinesterase, hAChE and butyrylcholinesterase, hBChE) and on the AChE-induced β-amyloid peptide (Aβ) aggregation by a series of peptide derivatives designed by mutating aliphatic residues for aromatic ones. We identified peptide W3 (LGWVSKGKLL-NH2 ) as an interesting scaffold for the development of new anti-AD multitarget-directed drugs. It showed the lowest IC50 value against hAChE reported for a peptide (0.99±0.02 μM) and inhibited 94.2 %±1.2 of AChE-induced Aβ aggregation at 10 μM. Furthermore, it inhibited hBChE (IC50 , 15.44±0.91 μM), showed no in vivo toxicity in brine shrimp and had shown moderated radical scavenging and Fe2+ chelating capabilities in previous studies. The results are in line with multiple reports showing the utility of the indole moiety for the development of cholinesterase inhibitors.

Binding Mechanism of Inhibitors to Heat Shock Protein 90 Investigated by Multiple Independent Molecular Dynamics Simulations and Prediction of Binding Free Energy

The heat shock protein (HSP90) has been an import target of drug design in the treatment of human disease. An exploration of the conformational changes in HSP90 can provide useful information for the development of efficient inhibitors targeting HSP90. In this work, multiple independent all-atom molecular dynamics (AAMD) simulations followed by calculations of the molecular mechanics generalized Born surface area (MM-GBSA) were performed to explore the binding mechanism of three inhibitors (W8Y, W8V, and W8S) to HSP90. The dynamics analyses verified that the presence of inhibitors impacts the structural flexibility, correlated movements, and dynamics behavior of HSP90. The results of the MM-GBSA calculations suggest that the selection of GB models and empirical parameters has important influences on the predicted results and verify that van der Waals interactions are the main forces that determine inhibitor-HSP90 binding. The contributions of separate residues to the inhibitor-HSP90 binding process indicate that hydrogen-bonding interactions (HBIs) and hydrophobic interactions play important roles in HSP90-inhibitor identifications. Moreover, residues L34, N37, D40, A41, D79, I82, G83, M84, F124, and T171 are recognized as hot spots of inhibitor-HSP90 binding and provide significant target sites of for the design of drugs related to HSP90. This study aims to contribute to the development of efficient inhibitors that target HSP90 by providing an energy-based and theoretical foundation.

Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies

β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.

Cargo-selective and adaptive delivery of nucleic acid therapeutics by bola-amphiphilic dendrimers

Nucleic acid therapeutics are becoming an important drug modality, offering the unique opportunity to address "undruggable" targets, respond rapidly to evolving pathogens, and treat diseases at the gene level for precision medicine. However, nucleic acid therapeutics have poor bioavailability and are chemolabile and enzymolabile, imposing the need for delivery vectors. Dendrimers, by virtue of their well-defined structure and cooperative multivalence, represent precision delivery systems. We synthesized and studied bola-amphiphilic dendrimers for cargo-selective and on-demand delivery of DNA and small interfering RNA (siRNA), both important nucleic acid therapeutics. Remarkably, superior performances were achieved for siRNA delivery with the second-generation dendrimer, yet for DNA delivery with the third generation. We systematically studied these dendrimers with regard to cargo binding, cellular uptake, endosomal release, and in vivo delivery. Differences in size both of the dendrimers and their nucleic acid cargos impacted the cooperative multivalent interactions for cargo binding and release, leading to cargo-adaptive and selective delivery. Moreover, both dendrimers harnessed the advantages of lipid and polymer vectors, while offering nanotechnology-based tumor targeting and redox-responsive cargo release. Notably, they allowed tumor- and cancer cell-specific delivery of siRNA and DNA therapeutics for effective treatment in different cancer models, including aggressive and metastatic malignancies, outperforming the currently available vectors. This study provides avenues to engineer tailor-made vectors for nucleic acid delivery and precision medicine.

Deciphering the binding site and mechanism of new methylene blue with serum albumins: A multispectroscopic and computational investigation

Herein, the interaction mechanism of new methylene blue (NMB) with human serum albumin (HSA) and bovine serum albumin (BSA) was carefully investigated both experimentally and conceptually, employing experimental and insilico analysis. The steady-state emission spectral studies showed that the emission intensity of HSA and BSA was quenched significantly by NMB. The findings of the Stern-Volmer and double logarithmic plot revealed that the observed emission quenching process was through a static quenching mechanism and the measured binding constant values (K_b) for HSA-NMB and BSA-NMB are 2.766 and 1.187 × 10⁵ dm³ mol^-1 respectively. The time-resolved fluorescence lifetime measurement and UV-vis absorption investigation further verify the complex formation between NMB and HSA/BSA. The assessment of thermodynamic parameters disclosed the binding process was spontaneous driven by hydrogen bonds (H-bond) and van der Waals interactions, which contributed a significant role in the complexation. Moreover, the secondary structural conformation and microenvironment of HSA/BSA were modified in the presence of NMB, as evidenced by circular dichroism and synchronous fluorescence data. Molecular docking study predicted a plausible binding mode of NMB inside the binding pocket of HSA and BSA. These results demonstrated that the stabilized NMB is found at the Subdomain IIA (site I) of both the proteins and the results were correlated well with the competitive binding assay. Additionally, the principal components analysis revealed less variation of docked poses for HSA, while, more dispersed docked poses were observed for the BSA model. This also highlights the effects of docking towards a modeled protein (BSA). Molecular dynamic (MD) simulation based binding free energy (ΔG_mmgbsa) estimation obtained at 298, 303, 308 and 313 K, were in good agreement with our experimental (ΔG_bind) values.

Autochthonous Peruvian Natural Plants as Potential SARS-CoV-2 Mpro Main Protease Inhibitors

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 M^pro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against M^pro, with a K_i value lower than 20 µM, presumably through allosteric modulation.

Simplified Derivatives of Tetrandrine as Potent and Specific P-gp Inhibitors to Reverse Multidrug Resistance in Cancer Chemotherapy

Targeted inhibition of a drug efflux transporter P-glycoprotein (P-gp) is an important strategy to reverse multidrug resistance in cancer chemotherapy. In this study, a rationally structural simplification to natural tetrandrine was performed based on molecular dynamics simulation and fragment growth, leading to an easily prepared, novel, and simplified compound OY-101 with high reversal activity and low cytotoxicity. Its excellent synergistic anti-cancer effect with vincristine (VCR) against drug-resistant cells Eca109/VCR was confirmed by reversal activity assay, flow cytometry, plate clone formation assay, and drug synergism analysis (IC50 = 9.9 nM, RF = 690). Further mechanism study confirmed that the OY-101 was a specific and efficient P-gp inhibitor. Importantly, OY-101 increased VCR sensitization in vivo without obvious toxicity. Overall, our findings may provide an alternative strategy for the design of novel specific P-gp inhibitor as an anti-tumor chemotherapy sensitizer.

Conformationally Restricted σ1 Receptor Antagonists from (-)-Isopulegol

Antagonists at σ₁ receptors have great potential for the treatment of neuropathic pain. Starting from monoterpene (-)-isopulegol (1), aminodiols 8-11 were obtained and transformed into bicyclic 13-16 and tricyclic ligands 19-22. Aminodiols 8-11 showed higher σ₁ affinity than the corresponding bicyclic 13-16 and tricyclic derivatives 19-22. (R)-configuration in the side chain of aminodiols (8 and 10) led to higher σ₁ affinity than (S)-configuration (9 and 11). 4-Benzylpiperidines (b-series) revealed higher σ₁ affinity than 4-phenylbutylamines (a-series). Aminodiol 8b showed very high σ₁ affinity (K_i = 1.2 nM), excellent selectivity over σ₂ receptors, and promising logD_7.4 (3.05) and lipophilic ligand efficiency (5.87) values. Molecular dynamics simulations were conducted to analyze the σ₁ affinity and selectivity on an atomistic level. In the capsaicin assay, 8b exhibited similar antiallodynic activity to the prototypical σ₁ antagonist S1RA. The antiallodynic activity of 8b was removed by co-application of the σ₁ agonist PRE-084, proving σ₁ antagonism being involved in the antiallodynic effect.

The Intrinsic Radius as a Key Parameter in the Generalized Born Model to Adjust Protein-Protein Electrostatic Interaction

The generalized Born (GB) model is an extension of the continuum dielectric theory of Born solvation energy and is a powerful method for accelerating the molecular dynamic (MD) simulations of charged biological molecules in water. While the effective dielectric constant of water that varies as a function of the separation distance between solute molecules is incorporated into the GB model, adjustment of the parameters is indispensable for accurate calculation of the Coulomb (electrostatic) energy. One of the key parameters is the lower limit of the spatial integral of the energy density of the electric field around a charged atom, known as the intrinsic radius ρ. Although ad hoc adjustment of ρ has been conducted to improve the Coulombic (ionic) bond stability, the physical mechanism by which ρ affects the Coulomb energy remains unclear. Via energetic analysis of three differently sized systems, here, we clarify that the Coulomb bond stability increases with increasing ρ and that the increased stability is caused by the interaction energy term, not by the self-energy (desolvation energy) term, as was supposed previously. Our results suggest that the use of larger values for the intrinsic radii of hydrogen and oxygen atoms, together with the use of a relatively small value for the spatial integration cutoff in the GB model, can better reproduce the Coulombic attraction between protein molecules.

Differentiable rotamer sampling with molecular force fields

Molecular dynamics is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to molecular dynamics, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD samples rare events at a higher rate than traditional MD, however critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Understanding Drug Resistance of Wild-Type and L38HL Insertion Mutant of HIV-1 C Protease to Saquinavir

Acquired immunodeficiency syndrome (AIDS) is one of the most challenging infectious diseases to treat on a global scale. Understanding the mechanisms underlying the development of drug resistance is necessary for novel therapeutics. HIV subtype C is known to harbor mutations at critical positions of HIV aspartic protease compared to HIV subtype B, which affects the binding affinity. Recently, a novel double-insertion mutation at codon 38 (L38HL) was characterized in HIV subtype C protease, whose effects on the interaction with protease inhibitors are hitherto unknown. In this study, the potential of L38HL double-insertion in HIV subtype C protease to induce a drug resistance phenotype towards the protease inhibitor, Saquinavir (SQV), was probed using various computational techniques, such as molecular dynamics simulations, binding free energy calculations, local conformational changes and principal component analysis. The results indicate that the L38HL mutation exhibits an increase in flexibility at the hinge and flap regions with a decrease in the binding affinity of SQV in comparison with wild-type HIV protease C. Further, we observed a wide opening at the binding site in the L38HL variant due to an alteration in flap dynamics, leading to a decrease in interactions with the binding site of the mutant protease. It is supported by an altered direction of motion of flap residues in the L38HL variant compared with the wild-type. These results provide deep insights into understanding the potential drug resistance phenotype in infected individuals.

An Attempt of Seeking Favorable Binding Free Energy Prediction Schemes Considering the Entropic Effect on Fis-DNA Binding

Protein-DNA binding mechanisms in a complex manner are essential for understanding many biological processes. Over the past decades, numerous experiments and calculations have analyzed the specificity of protein-DNA binding. However, the accuracy of binding free energy prediction for multi-base DNA systems still needs to be improved. Fis is a DNA-binding protein that regulates various transcription and recombination reactions. In the present work, we tested several methods of predict binding free energy based on this system to find a favorable prediction scheme and explore the binding mechanism of Fis protein and DNA. Two solvent models (explicit and implicit solvent models) were chosen for the dynamics process, and the predicted binding free energy was more accurate under the explicit solvent model. When different Poisson-Boltzmann/Generalized Born (PB/GB) models were tested for DNA force fields (BSC1 and OL15), it was found that the binding free energy predicted by the selected OL15 force field performed better and the correlation between predicted and experimental values was improved with the increasing interior dielectric constant (Dk). Finally, using Dk = 8, the GB^OBC1 model combined with interaction entropy (IE), which was calculated for entropic contribution (GB^OBC1_IE_8), was screened out for the binding free energy prediction and analysis of the Fis-DNA system, and the validity of the method was further verified by testing the Cren7-DNA system. By performing conformational analysis of the minor groove, it was found that mutation of the DNA central sequence A/T to C/G and deletion of the guanine 2-amino group would change the minor groove width and thus affect the formation of the major groove, altering the interaction and atomic contact between the protein and the major groove, thus changing the binding affinity of Fis and DNA. Hopefully, the series of tests in this work can shed some light on the related studies of protein and DNA systems.

Role of Dynamics and Mutations in Interactions of a Zinc Finger Antiviral Protein with CG-rich Viral RNA

Zinc finger antiviral protein (ZAP) is a host antiviral factor that selectively inhibits the replication of a variety of viruses. ZAP recognizes the CG-enriched RNA sequences and activates the viral RNA degradation machinery. In this work, we investigated the dynamics of a ZAP/RNA complex and computed the energetics of mutations in ZAP that affect its binding to the viral RNA. The crystal structure of a mouse-ZAP/RNA complex showed that RNA interacts with the zinc finger 2 (ZF2) and ZF3 domains. However, we found that due to the dynamic behavior of the single-stranded RNA, the terminal nucleotides C1 and G2 of RNA change their positions from the ZF3 to the ZF1 domain. Moreover, the electrostatic interactions between the zinc ions and the viral RNA provide further stability to the ZAP/RNA complex. We also provide structural and thermodynamic evidence for seven residue pairs (C1-Arg74, C1-Arg179, G2-Arg74, U3-Lys76, C4-Lys76, G5-Arg95, and U6-Glu204) that show favorable ZAP/RNA interactions, although these interactions were not observed in the ZAP/RNA crystal structure. Consistent with the observations from the mouse-ZAP/RNA crystal structure, we found that four residue pairs (C4-Lys89, C4-Leu90, C4-Tyr108, and G5-Lys107) maintained stable interactions in MD simulations. Based on experimental mutagenesis studies and our residue-level interaction analysis, we chose seven residues (Arg74, Lys76, Lys89, Arg95, Lys107, Tyr108, and Arg179) for individual alanine mutations. In addition, we studied mutations in those residues that are only observed in the crystal structures as interacting with RNA (Tyr98, Glu148, and Arg170). Out of these 10 mutations, we found that the Ala mutation in each of the five residues Arg74, Lys76, Lys89, Lys107, and Glu148 significantly reduced the binding affinity of ZAP to RNA.

A Combined in Silico and Structural Study Opens New Perspectives on Aliphatic Sulfonamides, a Still Poorly Investigated Class of CA Inhibitors

Aliphatic sulfonamides are an interesting class of carbonic anhydrase inhibitors (CAIs) proven to be effective for several carbonic anhydrase (CA) isoforms involved in pathologic states. Here we report the crystallographic structures of hCA II in complex with two aliphatic sulfonamides incorporating coumarin rings, which showed a good inhibition and selectivity for this isoform. Although these two molecules have a very similar chemical structure, differing only in the substitution of the two aliphatic hydrogen atoms with two fluorine atoms, they adopt a significantly different binding mode within the enzyme active site. Theoretical binding free energy calculations, performed to rationalize these data, showed that a delicate balance of electrostatic and steric effects modulate the protein-ligand interactions. Data presented here can be fruitfully used for the rational design of novel and effective isozyme-specific inhibitor molecules.

Crystal structure of a covalently linked Aurora-A-MYCN complex

Formation of the Aurora-A-MYCN complex increases levels of the oncogenic transcription factor MYCN in neuroblastoma cells by abrogating its degradation through the ubiquitin proteasome system. While some small-molecule inhibitors of Aurora-A were shown to destabilize MYCN, clinical trials have not been satisfactory to date. MYCN itself is considered to be `undruggable' due to its large intrinsically disordered regions. Targeting the Aurora-A-MYCN complex rather than Aurora-A or MYCN alone will open new possibilities for drug development and screening campaigns. To overcome the challenges that a ternary system composed of Aurora-A, MYCN and a small molecule entails, a covalently cross-linked construct of the Aurora-A-MYCN complex was designed, expressed and characterized, thus enabling screening and design campaigns to identify selective binders.

Inhibition of VMAT2 by β2-adrenergic agonists, antagonists, and the atypical antipsychotic ziprasidone

Vesicular monoamine transporter 2 (VMAT2) is responsible for packing monoamine neurotransmitters into synaptic vesicles for storage and subsequent neurotransmission. VMAT2 inhibitors are approved for symptomatic treatment of tardive dyskinesia and Huntington's chorea, but despite being much-studied inhibitors their exact binding site and mechanism behind binding and inhibition of monoamine transport are not known. Here we report the identification of several approved drugs, notably β2-adrenergic agonists salmeterol, vilanterol and formoterol, β2-adrenergic antagonist carvedilol and the atypical antipsychotic ziprasidone as inhibitors of rat VMAT2. Further, plausible binding modes of the established VMAT2 inhibitors reserpine and tetrabenazine and hit compounds salmeterol and ziprasidone were identified using molecular dynamics simulations and functional assays using VMAT2 wild-type and mutants. Our findings show VMAT2 as a potential off-target of treatments with several approved drugs in use today and can also provide important first steps in both drug repurposing and therapy development targeting VMAT2 function.

In Silico Study of the Acquired Resistance Caused by the Secondary Mutations of KRAS G12C Protein Using Long Time Molecular Dynamics Simulation and Markov State Model Analysis

Kirsten rat sarcoma viral oncogene homolog (KRAS) is a small GTPase protein which plays an important role in the treatment of KRAS mutant cancers. The FDA-approved AMG510 and MRTX849 (phase III clinical trials) are two potent KRASG12C-selective inhibitors that target KRAS G12C. However, the drug resistance caused by the second-site mutation in KRAS has emerged, and the mechanisms of drug resistance at atom level are still unclear. To clarify the mechanisms of drug resistance, we conducted long time molecular dynamics simulations (75 μs in total) to study the structural and energetic features of KRAS G12C and its four drug resistant variants to inhibitors. The combined binding free energy calculation and protein-ligand interaction fingerprint revealed that these second-site mutations indeed caused KRAS to produce different degrees of resistance to AMG510 and MRTX849. Furthermore, Markov State Models and 2D-free energy landscapes analysis revealed the difference in conformational changes of mutated KRAS bound with and without inhibitors. Furthermore, the comparative analysis of these systems showed that there were differences in their allosteric signal pathways. These findings provide the molecular mechanism of drug resistance, which helps to guide novel KRAS G12C inhibitor design to overcome drug resistance.

CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.

Shedding light on the binding mechanism of kinase inhibitors BI-2536, Volasetib and Ro-3280 with their pharmacological target PLK1

In the present work, the interactions of the novel kinase inhibitors BI-2536, Volasetib (BI-6727) and Ro-3280 with the pharmacological target PLK1 have been studied by fluorescence spectroscopy and molecular dynamics calculations. High Stern-Volmer constants were found in fluorescence experiments suggesting the formation of stable protein-ligand complexes. In addition, it was observed that the binding constant between BI-2536 and PLK1 increases about 100-fold in presence of the phosphopeptide Cdc25C-p that docks to the polo box domain of the protein and releases the kinase domain. All the determined binding constants are higher for the kinase inhibitors than for their competitor for the active center (ATP) being BI-2536 and Volasertib the inhibitors that showed more affinity for PLK1. Calculated binding free energies confirmed the higher affinity of PLK1 for BI-2536 and Volasertib than for ATP. The higher affinity of the inhibitors to PLK1 compared to ATP was mainly attributed to stronger van der Waals interactions. Results may help with the challenge of designing and developing new kinase inhibitors more effective in clinical cancer therapy.

Neglected N-Truncated Amyloid-β Peptide and Its Mixed Cu-Zn Complexes

Amyloid-β (Aβ) peptides are involved in Alzheimer's disease (AD) development. The interactions of these peptides with copper and zinc ions also seem to be crucial for this pathology. Although Cu(II) and Zn(II) ions binding by Aβ peptides has been scrupulously investigated, surprisingly, this phenomenon has not been so thoroughly elucidated for N-truncated Aβ_4-x-probably the most common version of this biomolecule. This negligence also applies to mixed Cu-Zn complexes. From the structural in silico analysis presented in this work, it appears that there are two possible mixed Cu-Zn(Aβ_4-x) complexes with different stoichiometries and, consequently, distinct properties. The Cu-Zn(Aβ_4-x) complex with 1:1:1 stoichiometry may have a neuroprotective superoxide dismutase-like activity. On the other hand, another mixed 2:1:2 Cu-Zn(Aβ_4-x) complex is perhaps a seed for toxic oligomers. Hence, this work proposes a novel research direction for our better understanding of AD development.

Identification of a Novel Osteogenetic Oligodeoxynucleotide (osteoDN) That Promotes Osteoblast Differentiation in a TLR9-Independent Manner

Dysfunction of bone-forming cells, osteoblasts, is one of the causes of osteoporosis. Accumulating evidence has indicated that oligodeoxynucleotides (ODNs) designed from genome sequences have the potential to regulate osteogenic cell fate. Such osteogenetic ODNs (osteoDNs) targeting and activating osteoblasts can be the candidates of nucleic acid drugs for osteoporosis. In this study, the ODN library derived from the Lacticaseibacillus rhamnosus GG genome was screened to determine its osteogenetic effect on murine osteoblast cell line MC3T3-E1. An 18-base ODN, iSN40, was identified to enhance alkaline phosphatase activity of osteoblasts within 48 h. iSN40 also induced the expression of osteogenic genes such as Msx2, osterix, collagen type 1α, osteopontin, and osteocalcin. Eventually, iSN40 facilitated calcium deposition on osteoblasts at the late stage of differentiation. Intriguingly, the CpG motif within iSN40 was not required for its osteogenetic activity, indicating that iSN40 functions in a TLR9-independent manner. These data demonstrate that iSN40 serves as a novel osteogenetic ODN (osteoDN) that promotes osteoblast differentiation. iSN40 provides a potential seed of the nucleic acid drug that activating osteoblasts for osteoporosis therapy.

Prediction of COMT Inhibitors Using Machine Learning and Molecular Dynamics Methods

Catechol O-methyltransferase (COMT) plays a vital role in deactivating neurotransmitters like dopamine, norepinephrine, etc., by methylating those compounds. However, the deactivation of an excess amount of neurotransmitters leads to serious mental ailments such as Parkinson's disease. Molecules that bind inside the enzyme's active site inhibit this methylation mechanism by methylating themselves, termed COMT inhibitors. Our study is focused on designing these inhibitors by various machine learning methods. First, we have developed a classification model with experimentally available COMT inhibitors, which helped us generate a new data set of small inhibitor-like molecules. Then, to predict the activity of the new molecules, we have applied regression techniques such as Random Forest, AdaBoost, gradient boosting, and support vector machines. Each of the regression models yielded an R² value > 70% for both training and test data sets. Finally, to validate our models, 200 ns long molecular dynamics (MD) simulations of the two known inhibitors with known IC₅₀ values and the resultant inhibitors were performed inside the binding pockets to check their stability within. The free energy barrier of the methyl transfer from S-adenosyl-l-methionine (SAM) to each inhibitor was determined by combining steered molecular dynamics (SMD) and umbrella sampling using the quantum mechanics/molecular mechanics (QM/MM) method.