Revealing vilazodone's binding mechanism underlying its partial agonism to the 5-HT1A receptor in the treatment of major depressive disorder

Design, synthesis, and biological evaluation of aralkyl piperazine and piperidine derivatives targeting SSRI/5-HT1A/5-HT7

Serotonin reuptake inhibition combined with the action targeting 5-hydroxytryptamine receptor subtypes can serve as a potential target for the development of antidepressant drugs. Herein a series of new aralkyl piperazines and piperidines were designed and synthesized by the structural modifications of the previously discovered aralkyl piperidine compound 1, targeting SSRI/5-HT1A/5-HT7. The results exhibited that compound 5a showed strong binding to 5-HT1A and 5-HT7 (Ki of 0.46 nM, 2.7 nM, respectively) and a high level of serotonin reuptake inhibition (IC50 of 1.9 nM), all of which were significantly elevated compared to 1. In particular, compound 5a showed weaker inhibitory activity against hERG than 1, and demonstrated good stability in liver microsomes in vitro. The preliminary screening using FST indicated that orally administered 5a, at a high dose, could reduce immobility time in mice markedly, indicating potential antidepressant activity.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Multi-state Model-Based Identification of Cryptic Allosteric Sites on Human Serotonin Transporter

Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OO_holo), outward-occluded (OC_holo), and inward-open (IO_holo and IO_holo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Moreover, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.

Quality of life among Iranian major depressive disorder patients: a qualitative study

Background

Research indicates that the prevalence of Major Depressive Disorder (MDD) is high and it reduces patient’s Quality of Life (QoL). Nowadays, in addition to reducing the symptoms of MDD, emphasis is on the concept of QoL as the purpose of treating these patients. However, we still do not know what these patient’s attitudes and perceptions are about QoL. This study aims to clarify the Iranian patient’s attitudes and perceptions of QoL’s meaning through a qualitative paradigm.

Results

During the data analysis, “Agitation factors”, “Destructive effects”, and “Gratifications” were developed as three main themes with 13 main categories, 39 sub-categories, and 879 codes. According to the findings the main priority of patients with MDD was Agitation factors, which plays a remarkable role in the concept of QoL.

Conclusions

MDD leads to deterioration in the QoL of these individuals in different parts. The novelty of this study leads to the creation of a deep and realistic attitude in national and global nurses towards the QoL of majorly depressed patients in order to remove the aggravating factors of the disorder and create a pleasant life based on the adequate and specialized understanding of the consequences of this disorder according to the preferences of patients and provide a suitable quality of life for these patients.

Unbiased molecular dynamics simulation of a first-in-class small molecule inhibitor binds to oncostatin M

Small molecule inhibitors (SMIs) targeting oncostatin M (OSM) signaling pathway represent new therapeutics to combat cancer, inflammatory bowel disease (IBD) and CNS disease. Recently, the first-in-class SMI named SMI-10B that target OSM and block its interaction with receptor (OSMR) were reported. However, the binding pocket and interaction mode of the compound on OSM remain poorly understood, which hampering the rational design of SMIs that target OSM. Here, using SMI-10B as a probe, the multiple pockets on OSM for small molecules binding were extensively explored by unbiased molecular dynamics (MD) simulations. Then, the near-native structure of the complex was identified by molecular mechanics generalized Born surface area (MM/GBSA) binding energy funnel. Moreover, the binding stabilities of the protein-ligand complexes in near- and non-native conformations were verified by additional independent MD runs and absolute free energy perturbation (FEP) calculation. In summary, the unique feature of SMI-10B spontaneously binds to OSM characterized here not only provide detailed information for understanding the molecular mechanism of SMI-10B binding to OSM, but also will facilitate the rational design of novel and more potent SMIs to block OSM signaling.

Identifying the difference in time perception between major depressive disorder and bipolar depression through a temporal bisection task

BACKGROUND

It is difficult to make a precise diagnosis to distinguish patients with Major Depressive Disorder (MDD) from patients with Bipolar Depressive Disorder (current depressive episode, BD). This study will explore the difference in time perception between MDD and BD using a temporal bisection task.

METHODS

In this temporal bisection task, 30 MDD patients, 30 BD patients, and 30 healthy controls (HC) had to categorize a signal duration, between 400 and 1600 milliseconds (ms), as either short or long. A repeated measurement analysis of variance with 3 (subject type) × 7 (time interval) was performed on the long response ratio with Bonferroni correction for multiple comparisons. Origin software was used to calculate the subjective bisection point (BP), difference limen (DL), and Weber ratio (WR). The Hamilton Depression Rating Scale for depression-17 was used to assess depressive symptoms in the patients.

RESULTS

The data showed that the interaction effect between subject type and duration was significant (F (6,498) = 4.656, p <0.001, η2p = 0.101). At 400 ms, and the long response of the MDD group was greater than HC group (p<0.017, Bonferroni-corrected). At 1200, 1400 and 1600 ms, the long response of BD group is smaller than HC group, (p<0.017, Bonferroni-corrected). The one-way ANOVA revealed significant difference among the HC, MDD and BD groups in the BP values WR values, F(2, 81) = 3.462, p = 0.036 vs. F(2, 81) = 3.311, p = 0.042. Post-hoc tests showed that the value of BP in the MDD group was less than BD group (p = 0.027) and the value of BP in the MDD group was less than HC group (p = 0.027), while there was not significant difference of BP values between BD group and HC group. The WR values in MDD group larger than the HC group (p = 0.022).

LIMITATIONS

Severity of depression not divided and analyzed according to the Hamilton Depression Rating Scale score.

CONCLUSION

The time perception of the MDD and BD groups was different from that of the HC group, they overestimated short time periods. Compared with the BD group, the MDD group had a smaller time bisector, and these patients felt that time passed more slowly. The time sensitivity of MDD group and BD group were less than the HC group. However, there was no statistical difference in time sensitivity between the MDD and BD groups.

The clinical utility of newer antidepressant agents: Understanding the role in management of MDD

Whereas MDD is characterized in part by changes in mood, other symptoms can also cause significant impairment, including sexual dysfunction, cognitive impairment, and fatigue. Newer antidepressants are explored with the goal of more optimally treating these non–mood-related symptoms of MDD. The 3 oral antidepressants that have been FDA-approved most recently include vortioxetine, vilazodone, and levomilnacipran. Unique features of these antidepressants are explored through 3 patient cases.

Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors

Group I metabotropic glutamate receptors (mGlu₁ and mGlu₅) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu₁ and mGlu₅ allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu₁ and mGlu₅ crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu₁ and mGlu₅ to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu₁ and mGlu₅ were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.

The miRNA: a small but powerful RNA for COVID-19

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

Profiling the structural determinants of aminoketone derivatives as hNET and hDAT reuptake inhibitors by field-based QSAR based on molecular docking

BACKGROUND

Bupropion, one of the dual norepinephrine and dopamine reuptake inhibitors (NDRIs), is an aminoketone derivative performed effect in improving cognitive function for depression. However, its therapeutic effect is unsatisfactory due to poor clinical response, and there are only few derivatives in pre-clinical settings.

OBJECTIVE

This work attempted to elucidate the essential structural features for the activity and designed a series of novel derivatives with good inhibitive ability, pharmacokinetic and medicinal chemistry properties.

METHODS

The field-based QSAR of aminoketone derivatives of two targets were established based on docking poses, and the essential structural properties for designing novel compounds were supplied by comparing contour maps.

RESULTS

The selected two models performed good predictability and reliability with R2 of 0.8479 and 0.8040 for training set, Q2 of 0.7352 and 0.6266 for test set respectively, and the designed 29 novel derivatives performed no less than the highest active compound with good ADME/T pharmacokinetic properties and medicinal chemistry friendliness.

CONCLUSIONS

Bulky groups in R1, bulky groups with weak hydrophobicity in R3, and potent hydrophobic substituted group with electronegative in R2 from contour maps provided important insights for assessing and designing 29 novel NDRIs, which were considered as candidates for cognitive dysfunction with depression or other related neurodegenerative disorders.

Comparison of the Inhibitory Binding Modes Between the Planar Fascaplysin and Its Nonplanar Tetrahydro-β-carboline Analogs in CDK4

Fascaplysin is a natural marine product originating from sponges, attracting widespread attention due to its potential inhibitory activities against CDK4. However, its clinical application has been largely limited because of serious adverse effects caused by planar skeleton. To reduce the serious adverse effects, 18 tetrahydro-β-carboline analogs (compounds 6a-i and 7a-i) were designed and synthesized via breaking the planarity of fascaplysin, and the biological activities of the synthesized compounds were evaluated by MTT assay and CDK4/CycD3 enzyme inhibition assay. The title compounds showed varying degrees of inhibitory activities, especially the cytotoxicity of compound 6c against HeLa cells (IC₅₀ = 1.03 ± 0.19 μM) with quite weak cytotoxicity toward the normal cells WI-38 (IC₅₀ = 311.51 ± 56.06 μM), and the kinase inhibition test indicated that compound 6c was a potential CDK4 inhibitor. In order to further compare the action mechanisms of planar and nonplanar molecules on CDK4, the studied complexes of CDK4 bound with fascaplysin and three representative compounds (compound 6a-c) with bioactivities gradient were constructed by molecular docking and further verified through molecular dynamic simulation, which identified the key residues contributing to the ligands' binding. By comparing the binding modes of the constructed systems, it could be found that the residues contributing significantly to compound 6c's binding were highly consistent with those contributing significantly to fascaplysin's binding. Through the design, synthesis of the nonplanar fascaplysin derivatives, and binding mechanism analysis, some valuable hints for the discovery of antitumor drug candidates could be provided.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Computational analysis of binding free energies, hotspots and the binding mechanism of Bcl-xL/Bcl-2 binding to Bad/Bax

Hierarchical clustering tree of residues providing contributions to system binding based on the binding free energy of specific residues for (A) Bcl-xL systems (B) Bcl-2 systems.

A2A Adenosine Receptor Partial Agonism Related to Structural Rearrangements in an Activation Microswitch

In drug design, G protein-coupled receptor (GPCR) partial agonists enable one to fine-tune receptor output between basal and maximal signaling levels. Here, we add to the structural basis for rationalizing and monitoring partial agonism. NMR spectroscopy of partial agonist complexes of the A_2A adenosine receptor (A_2AAR) revealed conformations of the P-I-F activation motif that are distinctly different from full agonist complexes. At the intracellular surface, different conformations of helix VI observed for partial and full agonist complexes manifest a correlation between the efficacy-related structural rearrangement of this activation motif and intracellular signaling to partner proteins. While comparisons of A_2AAR in complexes with partial and full agonists with different methods showed close similarity of the global folds, this NMR study now reveals subtle but distinct local structural differences related to partial agonism.

Sub-Acute Treatment of Curcumin Derivative J147 Ameliorates Depression-Like Behavior Through 5-HT1A-Mediated cAMP Signaling

Background

Major depressive disorder (MDD) is a severe mental disorder related to the deficiency of monoamine neurotransmitters, particularly to abnormalities of 5-HT (5-hydroxytryptamine, serotonin) and its receptors. Our previous study suggested that acute treatment with a novel curcumin derivative J147 exhibited antidepressant-like effects by increasing brain derived neurotrophic factor (BDNF) level in the hippocampus of mice. The present study expanded upon our previous findings and investigated the antidepressant-like effects of sub-acute treatment of J147 for 3 days in male ICR mice and its possible relevancy to 5-HT_1A and 5-HT_1B receptors and downstream cAMP-BDNF signaling.

Methods

J147 at doses of 1, 3, and 9 mg/kg (via gavage) was administered for 3 days, and the anti-immobility time in the forced swimming and tail suspension tests (FST and TST) was recorded. The radioligand binding assay was used to determine the affinity of J147 to 5-HT_1A and 5-HT_1B receptor. Moreover, 5-HT_1A or 5-HT_1B agonist or its antagonist was used to determine which 5-HT receptor subtype is involved in the antidepressant-like effects of J147. The downstream signaling molecules such as cAMP, PKA, pCREB, and BDNF were also measured to determine the mechanism of action.

Results

The results demonstrated that sub-acute treatment of J147 remarkably decreased the immobility time in both the FST and TST in a dose-dependent manner. J147 displayed high affinity in vitro to 5-HT_1A receptor prepared from mice cortical tissue and was less potent at 5-HT_1B receptor. These effects of J147 were blocked by pretreatment with a 5-HT_1A antagonist NAD-299 and enhanced by a 5-HT_1A agonist 8-OH-DPAT. However, 5-HT_1B receptor antagonist NAS-181 did not appreciably alter the effects of J147 on depression-like behaviors. Moreover, pretreatment with NAD-299 blocked J147-induced increases in cAMP, PKA, pCREB, and BDNF expression in the hippocampus, while 8-OH-DPAT enhanced the effects of J147 on these proteins’ expression.

Conclusion

The results suggest that J147 induces rapid antidepressant-like effects during a 3-day treatment period without inducing drug tolerance. These effects might be mediated by 5-HT_1A-dependent cAMP/PKA/pCREB/BDNF signaling.

Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters

Background:

The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs.

Objective:

This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted.

Methods:

PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs.

Results:

Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models.

Conclusion:

The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations

Vilazodone is a novel antidepressant used for the treatment of major depressive disorder (MDD) with a primary action mechanism of inhibiting the human serotonin reuptake transporter (hSERT) and acting as a 5-HT_1A receptor partial agonist. The interaction between vilazodone and the 5-HT_1A receptor has been reported, however, the binding mode of vilazodone in the hSERT remains elusive. In the current study, to elucidate the molecular mechanism of vilazodone binding in the hSERT, the drug and its five analogs were docked into the hSERT crystal structure as initial conformations and were sampled by 400 ns molecular dynamics (MD) simulations. Through the analysis of the profiles of protein-ligand binding free energies, interaction fingerprints, and conformational rearrangements, the binding mode of vilazodone in the hSERT was revealed. As a result, unlike the classical antidepressants located in the S1 site of the hSERT, vilazodone adopted a linear pose in the binding pocket. Its arylpiperazine fragment occupies the central site (S1) and interacts with Y95, D98, I172, Y176, F335, F341, S438, and T439, while the indole fragment extends to the allosteric site (S2) via interacting with the ionic switch (R104/E403) between the two sites. The new insights obtained are not only helpful in understanding the binding mode of vilazodone in the hSERT, but also provide valuable guidance to the discovery of novel antidepressant drugs.

In silico design of novel benzohydroxamate-based compounds as inhibitors of histone deacetylase 6 based on 3D-QSAR, molecular docking, and molecular dynamics simulations

In silico design of benzohydroxamate-based selective HDAC6 inhibitors.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

How Does Chirality Determine the Selective Inhibition of Histone Deacetylase 6? A Lesson from Trichostatin A Enantiomers Based on Molecular Dynamics

Histone deacetylase 6 (HDAC6) plays a key role in a variety of neurological disorders, which makes it attractive drug target for the treatment of Alzheimer's disease, Parkinson's disease, and memory/learning impairment. The selectivity of HDAC6 inhibitors (sHDAC6Is) are widely considered to be susceptible to the sizes of their Cap group and the physicochemical properties of their linker or zinc-binding group, which makes the discovery of new sHDAC6Is extremely difficult. With the discovery of the distinct selectivity between Trichostatin A (TSA) enantiomers, the chirality residing in the connective units between TSA's Cap and linker shows a great impact on its selectivity. However, the mechanism underlining ( S)-TSA's selectivity is still elusive, and the way chirality switches the selective ( S)-TSA to nonselective ( R)-TSA is unknown. In this study, multiple computational approaches were collectively applied to explore, validate, and differentiate the binding modes of two TSA enantiomers in HDACs (especially the HDAC6) at atomic level. First, two nonconservative residues (G200/M205 and Y197/F202 in HDAC1/6) in loop3 and four conservative residues deep inside the hydrophobic binding pocket were discovered as the decisive residues of ( S)-TSA's selectivity toward HDAC6. Then, a novel mechanism underlying the selectivity of ( S)-TSA toward HDAC6 was proposed, which was composed of the trigger by two nonconservative residues F202 and M205 in HDAC6 and a subsequently improved fit of ( S)-TSA deep inside HDAC6's hydrophobic binding pocket. TSA enantiomers were used as a molecular probe to explore the mechanism underlying sHDAC6Is' selectivity in this study. Because of their decisive roles in ( S)-TSA's selectivity to HDAC6, both F202 and M205 in HDAC6 should be especially considered in the discovery of novel sHDAC6Is.

Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I1/2 NIK Inhibitors

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I^1/2 inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I^1/2 NIK inhibitors. It is found that the docking accuracy is largely dependent on the selection of docking protocols as well as the crystal structures. The predictions given by the ensemble docking based on multiple receptor conformations (MRCs) and the MM/GB(PB)SA calculations based on MD simulations showed higher linear correlations with the experimental data than those given by conventional rigid receptor docking (RRD) methods (Glide, GOLD, and Autodock Vina), highlighting the importance of incorporating protein flexibility in predicting protein–ligand interactions. Further analysis based on MM/GBSA demonstrates that the hydrophobic interactions play the most essential role in the ligand binding to NIK, and the polar interactions also make an important contribution to the NIK-ligand recognition. A deeper comparison of several pairs of representative derivatives reveals that the hydrophobic interactions are vitally important in the structural optimization of analogs as well. Besides, the H-bond interactions with some key residues and the large desolvation effect in the back pocket devote to the affinity distinction. It is expected that our study could provide valuable insights into the design of novel and potent type I^1/2 NIK inhibitors.

Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H

The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

Signaling transduction regulated by 5-hydroxytryptamine 1A receptor and orexin receptor 2 heterodimers

As G-protein-coupled receptors (GPCRs), 5-hydroxytryptamine 1A receptor (5-HT1AR) and orexin receptor 2 (OX2R) regulate the levels of the cellular downstream molecules. The heterodimers of different GPCRs play important roles in various of neurological diseases. Moreover, 5-HT1AR and OX2R are involved in the pathogenesis of neurological diseases such as depression with deficiency of hippocampus plasticity. However, the direct interaction of the two receptors remains elusive. In the present study, we firstly demonstrated the heterodimer formation of 5-HT1AR and OX2R. Exchange protein directly activated by cAMP (Epac) cAMP bioluminescence resonance energy transfer (BRET) biosensor analysis revealed that the expression levels of cellular cAMP significantly increased in HEK293T cells transfected with the two receptors compared with the 5-HT1AR group. Additionally, the cellular level of calcium was upregulated robustly in HEK293T cells co-transfected with 5-HT1AR and OX2R group after agonist treatment. Furthermore, western blotting data showed that 5-HT1AR and OX2R heterodimer decreased the levels of phosphorylation of extracellular signal-regulated kinase (ERK) and cAMP-response element-binding protein (CREB). These results not only unraveled the formation of 5-HT1AR and OX2R heterodimer but also suggested that the heterodimer affected the downstream signaling pathway, which will provide new insights into the function of the two receptors in the brain.

Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein⁻Peptide Docking and Molecular Dynamics Simulation

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT_1290-1310) remain elusive and the interaction between GluN2B-CT_1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT_1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT_1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT_1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT_1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT_1290-1310 and DAPK1 interface.

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis

AIMS

Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune-relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties.

METHODS

A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well-known pathway-related databases were conducted to explore the function of the identified potential drug target genes.

RESULTS

Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that the enriched terms were primarily focus on immune-relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting.

CONCLUSION

The well-reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases.

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.

Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations

Metabotropic glutamate receptor 5 (mGlu₅) plays a key role in synaptic information storage and memory, which is a well-known target for a variety of psychiatric and neurodegenerative disorders. In recent years, the increasing efforts have been focused on the design of allosteric modulators, and the negative allosteric modulators (NAMs) are the front-runners. Recently, the architecture of the transmembrane (TM) domain of mGlu₅ receptor has been determined by crystallographic experiment. However, it has been not well understood how the pharmacophores of NAMs accommodated into the allosteric binding site. In this study, molecular dynamics (MD) simulations were performed on mGlu₅ receptor bound with NAMs in preclinical or clinical development to shed light on this issue. In order to identify the key residues, the binding free energies as well as per-residue contributions for NAMs binding to mGlu₅ receptor were calculated. Subsequently, the in silico site-directed mutagenesis of the key residues was performed to verify the accuracy of simulation models. As a result, the shared common features of the studied 5 clinically important NAMs (mavoglurant, dipraglurant, basimglurant, STX107, and fenobam) interacting with 11 residues in allosteric site were obtained. This comprehensive study presented a better understanding of mGlu₅ receptor NAMs binding mechanism, which would be further used as a useful framework to assess and discover novel lead scaffolds for NAMs.

What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation

Dual inhibition of serotonin and norepinephrine transporters (hSERT and hNET) gives greatly improved efficacy and tolerability for treating major depressive disorder (MDD) compared with selective reuptake inhibitors. Pioneer studies provided valuable information on structure, function, and pharmacology of drugs targeting both hSERT and hNET (serotonin-norepinephrine reuptake inhibitors, SNRIs), and the differential binding mechanism between SNRIs and selective inhibitors of 5-HT (SSRIs) or NE (sNRIs) to their corresponding targets was expected to be able to facilitate the discovery of a privileged drug-like scaffold with improved efficacy. However, the dual-target mechanism of SNRIs was still elusive, and the binding mode distinguishing SNRIs from SSRIs and sNRIs was also unclear. Herein, an integrated computational strategy was adopted to discover the binding mode shared by all FDA approved SNRIs. The comparative analysis of binding free energy at the per-residue level discovered that residues Phe335, Leu337, Gly338, and Val343 located at the transmembrane domain 6 (TM6) of hSERT (the corresponding residues Phe317, Leu319, Gly320, and Val325 in hNET) were the determinants accounting for SNRIs' dual-acting inhibition, while residues lining TM3 and 8 (Ile172, Ser438, Thr439, and Leu443 in hSERT; Val148, Ser419, Ser420, and Met424 in hNET) contributed less to the binding of SNRIs than that of SSRIs and sNRIs. Based on these results, the distances between an SNRI's centroid and the centroids of its two aromatic rings (measuring the depth of rings stretching into hydrophobic pockets) were discovered as the key to the SNRIs' dual-targeting mechanism. This finding revealed SNRIs' binding mechanism at an atomistic level, which could be further utilized as structural blueprints for the rational design of privileged drug-like scaffolds treating MDD.

Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder

A shared binding mode involving eleven key residues at the S1 site of MATs for the binding of amitifadine is identified.