On the Uses of PCA to Characterise Molecular Dynamics Simulations of Biological Macromolecules: Basics and Tips for an Effective Use

Repurposing of dipeptidyl peptidase FDA-approved drugs in Alzheimer's disease using network pharmacology and in-silico approaches

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) have similar clinical characteristics in the brain and islet, as well as an increased incidence with ageing and familial susceptibility. Therefore, in recent years there has been a great desire for research that elucidates how anti-diabetic drugs affect AD. This work attempts to first elucidate the possible mechanism of action of DPP-IV inhibitors in the treatment of AD by employing techniques from network pharmacology, molecular docking, molecular dynamic simulation, principal component analysis, and MM/PBSA. A total of 463 targets were identified from the SwissTargetPrediction and 784 targets were identified from the SuperPred databases. 79 common targets were screened using the PPI network. The GO and KEGG analyses indicated that the activity of DPP-IV against AD potentially involves the hsa04080 neuroactive ligand-receptor interaction signalling pathway, which contains 17 proteins, including CHRM2, CHRM3, CHRNB1, CHRNB4, CHRM1, PTGER2, CHRM4, CHRM5, TACR2, HTR2C, TACR1, F2, GABRG2, MC4R, HTR7, CHRNG, and DRD3. Molecular docking demonstrated that sitagliptin had the greatest binding affinity of -10.7 kcal/mol and established hydrogen bonds with the Asp103, Ser107, and Asn404 residues in the active site of the CHRM2 protein. Molecular dynamic simulation, PCA, and MM/PBSA were performed for the complex of sitagliptin with the above-mentioned proteins, which revealed a stable complex throughout the simulation. The work identifies the active component and possible molecular mechanism of sitagliptin in the treatment of AD and provides a theoretical foundation for future fundamental research and practical implementation.

In silico studies to understand the interactions of flavonoid inhibitor with nsp12-RNA dependent RNA polymerase of SARS-CoV-2 and its homologs

Aim

COVID 19 continues to be a major health concern. RNA dependent RNA polymerase of SARS-CoV-2 which is crucial for replication is therefore a potential drug target.

Methodology

Based on experimental structures of RdRp from SARS-CoV-2, computational models were generated of its homologs from SARS-C o V-1, MERS and Bat. SARS CoV-2 RdRp was used for virtual screening at nucleotide binding site with molecule from COCONUT Natural Products database using Glide. Complexes with the top inhibitor molecule were modelled using Discovery Studio and Desmond suite of programs.

Results

SARS-CoV-2 RdRp has a minimum of 80 % sequence similarity with its homologs, with the secondary structural elements, catalytic residues and metal binding residues being conserved. Certain residue variations in SARS-CoV-2 RdRp seems to be responsible for the stability of the enzyme. Docking and simulation studies showed that a flavonoid molecule with Coconut ID: CNP0127177.0 (HHF318) has binding affinity in low nano-molar range against RdRp from SARS-CoV-2 which was comparable or better than currently used inhibitors. This affinity stems from cationic-π with Arg555, and π-stacking interactions with a nucleobase of RNA. Molecule also engages with other residues that are crucial for its functions. This flavonoid molecule has similar physio-chemical properties like ATP towards SARS-CoV-2 RdRp, and has low potency for human ATP binding proteins.

Conclusion

HHF318 is a potential inhibitor of SARS-CoV-2 RdRp with good potency, specificity and pharmacokinetic properties for it to be developed as a drug candidate for COVID19.

Bridging Dimensionality Reduction and Stochastic Sampling: The DA2-MC Algorithm for Protein Dynamics

Elucidating protein dynamics and conformational changes is crucial for understanding their biological functions. This work introduces a data-driven accelerated conformational searching algorithm incorporating a Monte Carlo strategy, termed the DA2-MC method, which integrates dimensionality reduction techniques with Monte Carlo strategies to efficiently explore unknown protein conformations. The DA2-MC method was applied to investigate the folding mechanisms of two miniproteins, chignolin and WW domain, revealing their dynamic behavior in different conformational states at a reasonable computational cost. A Markov state model-based analysis of chignolin's folding pathway corroborated the dynamic insights obtained from the DA2-MC method. Moreover, free energy calculations initiated with the intermediate structures identified by DA2-MC yielded results consistent with published literature, affirming the method's reliability in accelerating conformational searches and reconstructing equilibrium properties. Collectively, the DA2-MC method emerges as an effective tool for efficiently exploring protein conformations, facilitating the identification of potential functional conformations on complex energy landscapes.

Effects of F53 hotspot mutations on the molecular dynamics of MEK1 protein and the binding of its FDA-approved inhibitors

MEK1 (MAP2K1) could emerge as an oncogenic protein in the presence of certain mutations, and could be inhibited by FDA-approved drugs (trametinib, cobimetinib, binimetinib and selumetinib). However, how the mutations on MEK1's hotspot residue F53 affect the bindings of these therapeutic molecules remained largely unexplored. We performed molecular dynamics (MD) simulations with wild-type and mutated (F53L/V/C/I/Y) MEK1 structures in the presence and absence of these drugs and observed changes on the motion of MEK1. A longer duration in the lowest energy state conformation was observed during the simulations in the presence of F53 mutations. This was complemented by cross-correlated motions of amino acids of MEK1. More importantly, the binding affinities of inhibitors were affected. There was a marked reduction in the calculated binding affinity of trametinib in the presence of F53C mutation. On the other hand, the binding affinities of cobimetinib and selumetinib could overcome F53 mutations on MEK1. Binimetinib interestingly exhibited an increased binding affinity when F53C/I mutations were present. Taken together, our results provide a comprehensive perspective on the structural and drug-binding effects of observed mutations on the hotspot residue F53 within MEK1; warranting further research in stratifying F53 hotspot mutations for effective drug binding.

Identification of phytochemicals from genus Potentilla as estrogen receptor-α inhibitors through molecular docking, molecular dynamic simulation and DFT calculations

Breast cancer is among the most prevalent causes of death in women worldwide. About 70-75% of these cancers are hormone-dependent, expressing estrogen receptors (ERs), mainly ER-α, making it an essential target for managing breast cancer. Potentilla genus has been traditionally used worldwide for its diverse biological activities, including antidiabetic, anti-inflammatory, antioxidant, etc. In the present study, phytochemicals isolated from various species of the Potentilla species were evaluated for their in silico ER-α inhibitory activity through molecular docking, molecular dynamic simulation, Density Functional Theory calculations and free energy calculations. Four hundred seventy-one molecules were used through ligand preparation and docked inside the generated grid on ER-α protein cavity and the standard drug tamoxifen. Fourteen molecules have shown better dock (-14.42 to -12.57 kcal/mol) scores than tamoxifen (-10.71 kcal/mol). Most of the molecules belong to the category of flavonoid glycosides. Molecules with good binding free energy (-78.81 to -12.94 kcal/mol) indicate stability inside the binding pocket. Further, based on dock score, pharmacokinetic parameters, and binding free energy, two hit molecules, 1 and 2, were selected for their molecular dynamic simulation, MM/PBSA and DFT calculations for assessing their stability and structural dynamics inside the binding cavity as well as their reactivity. Through MD simulation analysis, it was evaluated that Compound 1 could distort the protein to a greater extent. In contrast, compound 2 was stable throughout the simulation time of 150 ns and can be further explored in vitro and in vivo studies as ER-α inhibitors in breast cancer.

In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis

BACKGROUND AND OBJECTIVE

In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in colorectal cancer and its metastasis. Again, exceeding genomic arrangements of AKT2 can be held responsible for HGSC (High-grade serous ovarian cancer) and breast cancer cell metastasis. FDA-approved capivasertib, a potential drug targeting the AKT signaling pathway, has a few side effects such as plausible alterations of liver function and gastrointestinal issues. Hence, this research aims to detect compounds with higher drug potency for selective AKT2 inhibition to encounter the incidence of different types of cancer cell metastasis.

METHODS

Eight machine-learning models were engaged to classify active and inactive drug candidates among 1148 collected compounds from the CHEMBL database. Potential drug candidates with greater IC50 value and no Lipinski violations were then addressed to molecular docking and molecular dynamics simulation using PyRx, AutoDock Vina and Desmond package.

RESULTS

From docking studies, three of the initial drug candidates provided greater binding affinities within a range from -10.9 to -9.8 kcal/mol, comparable to that of Capivasertib and backed up by post-docking MM/GBSA analysis. Again, the prediction of pharmacokinetic properties and bioactivity scores of drug candidates revealed their drug-likeliness and safer ADMET profiles for future clinical trials. Finally, 100 ns MD simulation computation for these lead compounds exhibited greater stability and drug potency during interactions with AKT2 protein, followed by PCA and DCCM analysis.

CONCLUSION

However, future in-vivo research can ascertain whether our proposed drug candidates can pass the standard clinical trials as publicly accessible novel drug targets.

Collective Variables and Facilitated Conformational Opening during Translocation of Human Mitochondrial RNA Polymerase (POLRMT) from Atomic Simulations

Collective variable (CV) identification is challenging in complex dynamical systems. To study the translocation of a single-subunit RNA polymerase (RNAP) during human mitochondrial transcription, we employed all-atom molecular dynamics (MD) as a vehicle to illustrate CV refinement in conformational samplings and dimension reduction analyses. RNAP translocation is an essential mechanical step of transcription elongation that dictates gene expression. The translocation generally follows from polymerization product release and proceeds to initial binding or preinsertion of incoming nucleotides. The human mitochondrial DNA-dependent RNAP (or POLRMT) plays a critical role in cellular metabolism and can be a key molecular off-target in the design of nucleotide analogue antiviral and antitumor drugs due to its structural similarities with many viral RNAPs or RNA-dependent RNA polymerases (RdRps). While POLRMT shares particularly high structural similarity with bacteriophage T7 RNAP, previous experimental studies and our current simulations suggest that POLRMT's mechanochemical coupling mechanisms may be distinct. In the current work, we modeled POLRMT elongation complexes and performed equilibrium MD simulations on the pre- and post-translocation models, with extensive samplings around two potential translocation paths (with or without coupling to the fingers subdomain conformational change). We then compared time-lagged independent component analysis (tICA) and the neural network implementation of the variational approach for Markov processes (VAMPnets) as dimensional reduction methods on selected atomic coordinate sets to best represent the sampled features from the MD simulations. Our results indicate that POLRMT translocation is likely coupled with NTP binding to enable fingers subdomain opening at post-translocation which would otherwise be nonstabilized, or the translocations may proceed futilely without the fingers opening for incoming NTP initial binding or incorporation. The time scale of the coupled translocation reaches over hundreds of microseconds, as predicted by the VAMPnets analyses. Such a time scale seems to match a last postcatalytic kinetic step suggested for the POLRMT elongation cycle by previous experimental measurements. Our MD simulation studies combining atomic coordinate refinements and dimension reduction analyses on top of extensive conformational samplings thus suggest a variation of Brownian ratcheting in POLRMT translocation, as if the Brownian motions of translocation are coupled with NTP binding, which captures transient fingers subdomain opening to couple the translocation with a sustained fingers opening.

Identification of novel amides and alkaloids as putative inhibitors of dopamine transporter for schizophrenia using computer-aided virtual screening

Schizophrenia is a complex psychiatric disorder marked by delusions, memory impairments, hallucinations, disorganized behavior, and severe cognitive deficits. Targeting the dopamine transporter (DAT) protein is promising for treating cognitive symptoms, especially in patients resistant to antipsychotic treatments. In this study, phytochemicals from six medicinal plants underwent virtual screening, and molecular dynamics simulation to identify potential agents targeting DAT. Key drug-like properties, safety, and biological activity were evaluated for identified hits. Pharmacokinetic simulation and pharmacophoric analysis were also performed. Among 990 screened phytochemicals, three alkaloids and six amides, predominantly from Piper retrofractum, and one diterpene were identified as potential antischizophrenic agents based on their stronger binding affinities and favorable docking scores compared to the standard (Lumateperone). Amides showed more potential for DAT than alkaloids. The dynamic behavior and stability of the top three amides, namely, Chenoalbicin, Dipiperamide G, and Lyciumamide C, were evaluated using various molecular dynamics analyses. RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), Rg (Radius of Gyration), and SASA (Solvent Accessible Surface Area) analyses demonstrated favorable characteristics for all three ligands. However, binding free energy, cross-correlation, PCA (Principal Component Analysis) and FEL (Free Energy Landscape) analyses indicated that Lyciumamide C exhibited the highest stability and binding affinity in dynamic environments, Pharmacophoric features highlighted the distinct interacting components for the top three amides. Pharmacokinetic simulations revealed significant peak concentrations and sustained levels can be indicated as Lyciumamide C > Chenoalbicin > Dipiperamide G. The higher and more sustained brain concentrations of Lyciumamide C suggest its most promising pharmacokinetic profile for targeting DAT. Overall, our screened metabolites followed drug-ability criteria and require further experimental validation.

Exploring Holy Basil's Bioactive Compounds for T2DM Treatment: Docking and Molecular Dynamics Simulations with Human Omentin-1

Type 2 Diabetes Mellitus (T2DM) presents a substantial health concern on a global scale, driving the search for innovative therapeutic strategies. Phytochemicals from medicinal plants, particularly Ocimum tenuiflorum (Holy Basil), have garnered attention for their potential in T2DM management. The increased focus on plant-based treatments stems from their perceived safety profile, lower risk of adverse effects, and the diverse range of bioactive molecules they offer, which can target multiple pathways involved in T2DM. Computational techniques explored the binding interactions between O. tenuiflorum phytochemicals and Human Omentin-1, a potential T2DM target. ADMET evaluation and targeted docking identified lead compounds: Luteolin (-4.84 kcal/mol), Madecassic acid (-4.12 kcal/mol), Ursolic acid (-5.91 kcal/mol), Stenocereol (-5.59 kcal/mol), and Apigenin (-4.64 kcal/mol), to have a better binding affinity to target protein compared to the control drug, Metformin (-2.01 kcal/mol). Subsequent molecular dynamics simulations evaluated the stability of Stenocereol, Luteolin, and Metformin complexes for 200 nanoseconds, analysing RMSD, RMSF, RG, SASA, PCA, FEL, and MM-PBSA parameters. Results indicated Stenocereol's strong binding affinity with Omentin-1, suggesting its potential as a potent therapeutic agent for T2DM management. These findings lay the groundwork for further experimental validation and drug discovery endeavours.

Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study

Visceral Leishmaniasis (VL), the second neglected tropical disease caused by various Leishmania species, presents a significant public health challenge due to limited treatment options and the absence of vaccines. The agent responsible for visceral leishmaniasis, also referred to as "black fever" in India, is Leishmania donovani. This study focuses on L. donovani Minichromosome maintenance 10 (LdMcm10), a crucial protein in the DNA replication machinery, as a potential therapeutic target in Leishmania therapy using in silico and in vitro approaches. We employed bioinformatics tools, molecular docking, and molecular dynamics simulations to predict potential inhibitors against the target protein. The research revealed that the target protein lacks homologues in the host, emphasizing its potential as a drug target. Ligands from the DrugBank database were screened against LdMcm10 using PyRx software. The top three compounds, namely suramin, vapreotide, and pasireotide, exhibiting the best docking scores, underwent further investigation through molecular dynamic simulation and in vitro analysis. The observed structural dynamics suggested that LdMcm10-ligand complexes maintained consistent binding throughout the 300 ns simulation period, with minimal variations in their backbone. These findings suggest that these three compounds hold promise as potential lead compounds for developing new drugs against leishmaniasis. In vitro experiments also demonstrated a dose-dependent reduction in L. donovani viability for suramin, vapreotide, and pasireotide, with computed IC50 values providing quantitative metrics of their anti-leishmanial efficacy. The research offers a comprehensive understanding of LdMcm10 as a drug target and provides a foundation for further investigations and clinical exploration, ultimately advancing drug discovery strategies for leishmaniasis treatment.

Molecular dynamics of transferrin receptor binder peptides: unlocking blood-brain barrier for enhanced CNS drug delivery

A cystine-dense peptide (CDP) named TfRB1 was identified for its ability to bind to the transferrin receptor (TfR). CDPs are stabilized by their disulfide bonds, and variants of TfRB1 - specifically TfRB1G1, TfRB1G2, and TfRB1G3 - are explored for their potential to transport molecules across the blood-brain barrier (BBB) into the central nervous system (CNS). This study employed molecular modeling and dynamics simulations to characterize the interactions between these TfRB1 variants and TfR. Binding free energy calculations showed a strong correlation with experimental binding affinities of -10.99 kcal/mol for TfRB1G2 and -13.18 kcal/mol for TfRB1G3, with a relative error of 1.98%. The key forces driving these interactions include electrostatic and van der Waals forces, with mutations in TfRB1G3 (T9M and A13D) enhancing its binding affinity through improved interactions with residues such as Arg633. The free energy landscape analysis revealed that TfRB1G3 maintains the N-terminal residues of TfR in an α-helical conformation, unlike TfRB1G2. Per-residue free energy decomposition identified key residues - Leu619, Arg629, Tyr643, and Phe650 - as crucial for TfR binding, underscoring their competitive nature with transferrin. Additionally, Glu612, which is favorable for binding in TfRB1G2, becomes unfavorable in TfRB1G3. Conversely, Arg633 shifts from unfavorable in TfRB1G2 to favorable in TfRB1G3, compensating for the loss of favorable interaction with Glu612. These findings provide valuable molecular insights into the TfRB1 peptides' potential as drug carriers, highlighting their capability to deliver molecules to the CNS and compete with transferrin for BBB transport.

A review on description dynamics and conformational changes of proteins using combination of principal component analysis and molecular dynamics simulation

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems.

Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate

BACKGROUND

Tuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette-Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects.

METHODS

Computer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics (MD), and immunological characteristics of the MEV.

RESULTS

We constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epitopes, 8 B-cell epitopes, as well as Toll-like receptor (TLR) agonists and helper peptides. Bioinformatics analyses revealed that ZL12138L should exhibit excellent immunogenicity and antigenicity, with no toxicity or allergenicity, and had potential to induce robust immune responses and high solubility, the immunogenicity score was 4.14449, the antigenicity score was 0.8843, and the immunological score was 0.470. Moreover, ZL12138L showed high population coverage for human leukocyte antigen class I and II alleles, reaching 92.41% and 90.17%, respectively, globally. Molecular docking analysis indicated favorable binding affinity of ZL12138L with TLR-2 and TLR-4, with binding energies of -1173.4 and -1360.5 kcal/mol, respectively. Normal mode analysis and MD simulations indicated the stability and dynamic properties of the vaccine construct. Immune simulation predictions suggested that ZL12138L could effectively activate innate and adaptive immune cells, inducing high levels of Type 1 T helper cell cytokines.

CONCLUSIONS

This study provides compelling evidence for ZL12138L as a promising TB vaccine candidate. Future research will focus on experimental validation and further optimization of the vaccine design.

Deciphering the toxicological and computational assessment of Verbascum yemenese.: Integrating phytochemistry and bioinformatics tools

This study explores the phytochemical composition and biological activities of Verbascum yemenense, a plant known for its medicinal properties. The plant extract revealed a rich presence of bioactive compounds that exhibited significant antioxidant properties against free radicals. The enzyme inhibition potential was particularly notable against cholinesterases (AChE: 2.56 mg GALAE/g; BChE: 1.98 mg GALAE/g), and tyrosinase (87.94 mg KAE/g), α-glucosidase suggesting potential therapeutic applications in neurodegenerative diseases, skin disorders and diabetes. Molecular docking studies and Molecular Dynamics simulations, providing insights into the interaction mechanisms of the identified compounds with the target proteins. Molecular docking studies revealed high binding affinities of the phytoconstituents, with compounds like VY4 and phyllanthusol-A (VY15) showing substantial docking scores against AChE (-9.840 kcal/mol) and BChE (-9.853 kcal/mol), respectively. For instance, the RMSD values during the MD simulations for compound VY17 in the AML complex showed a stable conformation, fluctuating within a range of 0.75 Å to 1.75 Å, indicating a strong and consistent interaction with the enzyme. MESP studies highlighted VY17's distinctive electrostatic features, notably a pronounced electronegative region, which might contribute to its binding efficiency. These findings suggest that V. yemenense is a promising candidate for developing novel therapeutic agents.

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP

The highly pathogenic Marburg virus (MARV) is a member of the Filoviridae family, a non-segmented negative-strand RNA virus. This article represents the computer-aided drug design (CADD) approach for identifying drug-like compounds that prevent the MARV virus disease by inhibiting nucleoprotein, which is responsible for their replication. This study used a wide range of in silico drug design techniques to identify potential drugs. Out of 368 natural compounds, 202 compounds passed ADMET, and molecular docking identified the top two molecules (CID: 1804018 and 5280520) with a high binding affinity of -6.77 and -6.672 kcal/mol, respectively. Both compounds showed interactions with the common amino acid residues SER_216, ARG_215, TYR_135, CYS_195, and ILE_108, which indicates that lead compounds and control ligands interact in the common active site/catalytic site of the protein. The negative binding free energies of CID: 1804018 and 5280520 were -66.01 and -31.29 kcal/mol, respectively. Two lead compounds were re-evaluated using MD modeling techniques, which confirmed CID: 1804018 as the most stable when complexed with the target protein. PC3 of the (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) was 8.74 %, whereas PC3 of the 2'-Hydroxydaidzein (CID: 5280520) was 11.25 %. In this study, (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) unveiled the significant stability of the proteins' binding site in ADMET, Molecular docking, MM-GBSA and MD simulation analysis studies, which also showed a high negative binding free energy value, confirming as the best drug candidate which is found in Angelica archangelica which may potentially inhibit the replication of MARV nucleoprotein.

Identification of novel natural compounds against CFTR p.Gly628Arg pathogenic variant

Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of -6.4, -5.1, -6.6, -5.1, and - 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

A computational study to assess the pathogenicity of single or combinations of missense variants on respiratory complex I

Variants found in the respiratory complex I (CI) subunit genes encoded by mitochondrial DNA can cause severe genetic diseases. However, it is difficult to establish a priori whether a single or a combination of CI variants may impact oxidative phosphorylation. Here we propose a computational approach based on coarse-grained molecular dynamics simulations aimed at investigating new CI variants. One of the primary CI variants associated with the Leber hereditary optic neuropathy (m.14484T>C/MT-ND6) was used as a test case and was investigated alone or in combination with two additional rare CI variants whose role remains uncertain. We found that the primary variant positioned in the E-channel region, which is fundamental for CI function, stiffens the enzyme dynamics. Moreover, a new mechanism for the transition between π- and α-conformation in the helix carrying the primary variant is proposed. This may have implications for the E-channel opening/closing mechanism. Finally, our findings show that one of the rare variants, located next to the primary one, further worsens the stiffening, while the other rare variant does not affect CI function. This approach may be extended to other variants candidate to exert a pathogenic impact on CI dynamics, or to investigate the interaction of multiple variants.

Fortuitous Correlations in Molecular Dynamics Simulations: Their Harmful Influence on the Probability Distributions of the Main Principal Components

Nonsense correlations frequently develop between independent random variables that evolve with time. Therefore, it is not surprising that they appear between the components of vectors carrying out multidimensional random walks, such as those describing the trajectories of biomolecules in molecular dynamics simulations. The existence of these correlations does not imply in itself a problem. Still, it can present a problem when the trajectories are analyzed with an algorithm such as the Principal Component Analysis (PCA) because it seeks to maximize correlations without discriminating whether they have physical origin or not. In this Article, we employ random walks occurring on multidimensional harmonic potentials to evaluate the influence of fortuitous correlations in PCA. We demonstrate that, because of them, this algorithm affords misleading results when applied to a single trajectory. The errors do not only affect the directions of the first eigenvectors and their eigenvalues, but the very definition of the molecule's "essential space" may be wrong. Additionally, the main principal component's probability distributions present artificial structures which do not correspond with the shape of the potential energy surface. Finally, we show that the PCA of two realistic protein models, human serum albumin and lysozyme, behave similarly to the simple harmonic models. In all cases, the problems can be mitigated and eventually eliminated by doing PCA on concatenated trajectories formed from a large enough number of individual simulations.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

Antidiabetes and antioxidant potential of Schiff bases derived from 2-naphthaldehye and substituted aromatic amines: Synthesis, crystal structure, Hirshfeld surface analysis, computational, and invitro studies

Three Schiff bases were synthesised by the condensation reaction between 2-napthaldehyde and aromatic amines to afford (E)-N-mesityl-1-(naphthalen-2-yl)methanimine (L1), (E)-N-(2,6-dimethylphenyl)-1-(naphthalen-2-yl)methanimine (L2) and (E)-N-(2,6-diisopropylphenyl)-1-(naphthalen-2-yl)methanimine (L3). The synthesised compounds were characterised using UV-visible, NMR (13C & 1H), and Fourier transform infrared spectroscopic methods while their purity was ascertained by elemental analysis. Structural analysis revealed that the naphthalene ring is almost coplanar with the imine functional group as evident by C1-C10-C11-N1 torsion angles of 176.4(2)° and 179.4(1)° in L2 and L3, respectively. Of all the various intermolecular contacts, H⋯H interactions contributed mostly towards the Hirshfeld surfaces of both L2 (58.7 %) and L3 (69.7 %). Quantum chemical descriptors of L1 - L3 were determined using Density Functional Theory (DFT) and the results obtained showed that the energy band gap (ΔE) for L1, L2 and L3 are 3.872, 4.023 and 4.004 eV respectively. The antidiabetic potential of the three compounds were studied using α-amylase and α-glucosidase assay. Compound L1 showed very promising antidiabetic activities with IC50 values of 58.85 μg/mL and 57.60 μg/mL while the reference drug (Acarbose) had 405.84 μg/mL and 35.69 μg/mL for α-amylase and α-glucosidase respectively. In-silico studies showed that L1 docking score as well as binding energies are higher than that of acarbose, which are recognized inhibitors of α-amylase together with α-glucosidase. Further insight from the RMSF, RMSD and RoG analysis predicted that, throughout the simulation L1 showcased evident influence on the structural stability of α-amylase. The antioxidant potential of the compounds was carried out using nitric oxide (NO), ferric reducing ability power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. The compounds exhibited good to fairly antioxidant properties with L1 as well as L3 having IC50 values of 70.91 and 91.21 μg/mL respectively for NO scavenging activities assay, which comparatively outshined acarbose (reference drug) with IC50 value of 109.95 μg/mL. Pharmacology and pharmacokinetics approximations of L1 - L3 showed minimal violation of Lipinski's Ro5 and this projects them to be less toxic and orally bioavailable as potential templates for the design of therapeutics with antioxidant and antidiabetic activities.

Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites

A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.