Are Parallel Proliferation Pathways Redundant?

Are the receptor tyrosine kinase (RTK) and JAK-STAT-driven proliferation pathways 'parallel' or 'redundant'? And what about those of K-Ras4B versus N-Ras? 'Parallel' proliferation pathways accomplish a similar drug resistance outcome. Thus, are they 'redundant'? In this paper, it is argued that there is a fundamental distinction between 'parallel' and 'redundant'. Cellular proliferation pathways are influenced by the genome sequence, 3D organization and chromatin accessibility, and determined by protein availability prior to cancer emergence. In the opinion presented, if they operate the same downstream protein families, they are redundant; if evolutionary-independent, they are parallel. Thus, RTK and JAK-STAT-driven proliferation pathways are parallel; those of Ras isoforms are redundant. Our Precision Medicine Call to map cancer proliferation pathways is vastly important since it can expedite effective therapeutics.

Harnessing Marine Natural Products to Inhibit PAD4 Triple Mutant: A Structure-Based Virtual Screening Approach for Rheumatoid Arthritis Therapy.

Peptidylarginine deiminase type4 (PAD4) is a pivotal pro-inflammatory protein within the human immune system, intricately involved in both inflammatory processes and immune responses. Its role extends to the generation of diverse immune cell types, including T cells, B cells, natural killer cells, and dendritic cells. PAD4 has recently garnered attention due to its association with a spectrum of inflammatory and autoimmune disorders, notably rheumatoid arthritis (RA). Mutations in the PAD4 gene, leading to the conversion of arginine to citrulline, have emerged as significant factors in the pathogenesis of RA and related conditions. As a calcium-dependent enzyme, PAD4 is central to the citrullination process, a crucial post-translational modification implicated in disease pathophysiology. Its critical role in autoimmune disorders and inflammation makes PAD4 a prime candidate for therapeutic intervention in RA. Inhibiting PAD4 presents a promising avenue for mitigating inflammatory responses and curtailing joint degradation and impairment. To explore its therapeutic potential, a structure-based virtual screening (SBVS) approach was employed, harnessing an array of marine natural products (MNPs) sourced from databases such as CMNPD, MNPD, and Seaweed. Notably, MNPD10752, CMNPD12680, and CMNPD2751 emerged as potential hit molecules, exhibiting adherence to essential pharmacokinetic properties and favorable toxicity profiles. Quantum mechanics studies using density functional theory (DFT) calculations revealed the inhibitory potential of these identified natural products. Further structural elucidation through molecular dynamics simulations (MDS) and principal component-based free energy landscape (FEL) analysis shed light on the stability of MNP-bound PAD4 complexes. In conclusion, this computational study serves as a stepping stone for further experimental evaluation, aiming to explore the potential of MNPs in addressing PAD4-related human pathologies.

Understanding the stability and dynamics of influenza a H5N1 polymerase PB2 CAP-Binding domain in complex with natural compounds for antiviral drug discovery.

Influenza A virus, particularly the H5N1 strain, poses a significant threat to public health due to its ability to cause severe respiratory illness and its high mortality rate. Traditional antiviral drugs targeting influenza A virus have faced challenges such as drug resistance and limited efficacy. Therefore, new antiviral compounds are needed to be discovered and developed. This study concentrated on examining the stability and behavior of the H5N1 polymerase PB2 CAP-binding domain when interacting with natural compounds, aiming to identify potential candidates for antiviral drug discovery. Through the virtual screening process, four lead compounds, ZINC000096095464, ZINC000044404209, ZINC000001562130, and ZINC000059779788, were selected, and these compounds showed binding energies -9.6, -9.4, -9.3, and -9.2 kcal/mol, respectively. When complexed with PB2, the ligand showed acceptable binding stability due to significant bond formation. However, during the 200ns MD simulation analysis, three (ZINC000096095464, ZINC000044404209, and ZINC000059779788) showed significant stability, which was proven by the trajectory analysis. The Rg-RMSD-based FEL plot showed significant structural stability due to stable conformers. The free-binding energy calculation also validates the stability of these complexes. This study offers valuable insights into the stability and dynamics of the H5N1 polymerase PB2 CAP-binding domain in complexes with natural compounds. These findings highlight the potential of these natural compounds as antiviral agents against the H5N1 influenza virus. Furthermore, this research contributes to the broader field of influenza virus treatment by demonstrating the effectiveness of computational methods in predicting and evaluating the stability and dynamics of potential drug candidates.

Investigation of small molecules disrupting dengue virus assembly by inhibiting capsid protein and blocking RNA encapsulation.

Dengue fever is a significant global public health concern, causing substantial morbidity and mortality worldwide. The disease can manifest in various forms, from mild fever to potentially life-threatening complications. Developing effective treatments remains a critical challenge to healthcare systems. Despite extensive research, no antiviral drugs have been approved for either the prevention or treatment of dengue. Targeting the virus during its early phase of attachment is essential to inhibit viral replication. The capsid protein plays a crucial role in the virus's structural integrity, assembly, and viral genome release. In the present study, we employed a computational approach focused on the capsid protein to identify possible potent inhibitors against the dengue virus from a library of FDA-approved drugs. We employed high-throughput virtual screening on FDA-approved drugs to identify drug molecules that could potentially combat the disease and save both cost and time. The screening process identified four drug molecules (Nordihydroguaiaretic acid, Ifenprodil tartrate, Lathyrol, and Safinamide Mesylate) based on their highest binding affinity and MM/GBSA scores. Among these, Nordihydroguaiaretic acid showed higher binding affinity than the reference molecule with - 11.66 kcal/mol. In contrast, Ifenprodil tartrate and Lathyrol showed similar results to the reference molecule, with binding energies of - 9.42 kcal/mol and - 9.29 kcal/mol, respectively. Following the screening, molecular dynamic simulations were performed to explore the molecular stability and conformational possibilities. The drug molecules were further supported by post-molecular simulation analysis. Furthermore, binding energies were also computed using the MM/GBSA approach, and the free energy landscape was used to calculate the different transition states, revealing that the drugs exhibited significant transition states. Specifically, Nordihydroguaiaretic acid and Ifenprodil tartrate displayed higher flexibility, while Lathyrol and Safinamide Mesylate showed more predictable and consistent protein folding. This significant breakthrough offers new hope against dengue, highlighting the power of computational drug discovery in identifying potent inhibitors and paving the way for novel treatment approaches.

Analyzing energetics and dynamics of hepatitis C virus polymerase interactions with marine bacterial compounds: a computational study.

Hepatitis C Virus (HCV) is a significant health concern affecting a large portion of the global population and is a major cause of acute liver diseases, including cirrhosis. The variability in the HCV genome mainly results from the rapid replication facilitated by the NS5B polymerase, making it a prime target for anti-HCV drug development. This study explores potential compounds from marine bacteria that could inhibit the HCV NS5B polymerase by virtual screening, analyzing the energetics, and dynamic behavior of target-compound complexes. Virtual screening with the Lipinski filter was employed to select compounds from the marine bacteria database that demonstrated strong binding affinity to NS5B. The top four (CMNPD27216, CMNPD21066, CMNPD21065, and CMNPD27283) compounds, ranked by their re-docking scores, underwent additional evaluation. Molecular dynamics simulations for 200 ns were conducted to assess the dynamic stability of these complexes in a solvent environment. Furthermore, methods such as MM-GBSA, PCA, and free energy landscape analysis were used to analyze the system's energetics and identify stable conformations by locating transition states. The findings suggest that these compounds exhibit promising binding capabilities to HCV polymerase and could be considered for future experimental validation.

Identification of potential NUDT5 inhibitors from marine bacterial natural compounds via molecular dynamics and free energy landscape analysis.

NUDIX hydrolase 5 (NUDT5) is an enzyme involved in the hydrolysis of nucleoside diphosphates linked to other moieties, such as ADP-ribose. This cofactor is vital in redox reactions and is essential for the activity of sirtuins and poly(ADP-ribose) polymerases, which are involved in DNA repair and genomic stability. It has been shown that NUDT5 activity can also influence NAD+ homeostasis, thereby affecting cancer cell metabolism and survival. In this regard, the discovery of NUDT5 inhibitors has emerged as a potential therapeutic approach in cancer treatment. In this study, we conducted a high-throughput virtual screening of marine bacterial compounds against the NUDT5 enzyme and four molecules were selected based on their docking scores. These compounds established strong interactions within the NUDT5 active site, with molecular analysis highlighting the key role of Trp28A and Trp46B residues. Molecular dynamics simulations over 200 ns indicated a stable behavior, in association with root mean square deviation values always below 3 Å, suggesting conformational stability. Free energy landscape analysis further supported their potential as NUDT5 inhibitors, offering avenues for novel therapeutic strategies against NUDT5-associated breast cancer.

Sinefungin analogs targeting VP39 methyltransferase as potential anti-monkeypox therapeutics: a multi-step computational approach.

The increasing spread of the Monkeypox virus (MPXV) presents a significant public health challenge, emphasising the urgent requirement for effective treatments. Our study focuses on the VP39 Methyltransferase enzyme of MPXV as a critical target for therapy. By utilising virtual screening, we investigated natural compounds with structural similarities to sinefungin, a broad-acting MTase inhibitor. From an initial set of 177 compounds, we identified three promising compounds-CNP0346326, CNP0343532, and CNP008361, whose binding scores were notably close to that of sinefungin. These candidates bonded strongly to the VP39 enzyme, hinting at a notable potential to impede the virus. Our rigorous computational assays, including re-docking, extended molecular dynamics simulations, and energetics analyses, validate the robustness of these interactions. The data paint a promising picture of these natural compounds as front-runners in the ongoing race to develop MPXV therapeutics and set the stage for subsequent empirical trials to refine these discoveries into actionable medical interventions.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (- 96.14 kcal/mol), CNP0176690 (- 93.79 kcal/mol), CNP0136537 (- 92.31 kcal/mol), CNP0398701 (- 91.96 kcal/mol), and CNP0043390 (- 91.60 kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease.

Alzheimer's disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051 kcal/mol better than the reference compound (- 11.828 kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100 ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be - 31.39, - 42.8, - 37.04, - 39.01, - 36.5, - 34.16 kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

Employing advanced computational drug discovery techniques to identify novel inhibitors against ML2640c protein: a potential therapeutic approach for combatting leprosy.

Leprosy, caused by Mycobacterium leprae, remains a significant global health challenge, necessitating innovative approaches to therapeutic intervention. This study employs advanced computational drug discovery techniques to identify potential inhibitors against the ML2640c protein, a key factor in the bacterium's ability to infect and persist within host cells. Utilizing a comprehensive methodology, including virtual screening, re-docking, molecular dynamics simulations, and free energy calculations, we screened a library of compounds for their interaction with ML2640c. Four compounds (24349836, 26616083, 26648979, and 26651264) demonstrated promising inhibitory potential, each exhibiting unique binding energies and interaction patterns that suggest a strong likelihood of disrupting the protein function. The study highlights the efficacy of computational methods in identifying potential therapeutic candidates, presenting compound 26616083 as a notably potent inhibitor due to its excellent binding affinity and stability. Our findings offer a foundation for future experimental validation and optimization, marking a significant step forward in the development of new treatments for leprosy. This research not only advances the fight against leprosy but also showcases the broader applicability of computational drug discovery in tackling infectious diseases.

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.

The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were combined with deep learning (DL) to explore the molecular mechanism of the phosphorylation-mediated effect on conformational dynamics of the GTP-bound KRAS. The DL finds that the switch domains are involved in obvious differences in conformation contacts and suggests that the switch domains play a key role in the function of KRAS. The analyses of free energy landscapes (FELs) reveal that the phosphorylation of pY32, pY64, and pY137 leads to more disordered states of the switch domains than the wild-type (WT) KRAS and induces conformational transformations between the closed and open states. The results from principal component analysis (PCA) indicate that principal motions PC1 and PC2 are responsible for the closed and open states of the phosphorylated KRAS. Interaction networks were analyzed and the results verify that the phosphorylation alters interactions of GTP and magnesium ion Mg2+ with the switch domains. It is concluded that the phosphorylation pY32, pY64, and pY137 tune the activity of KRAS through changing conformational dynamics and interactions of the switch domains. We anticipated that this work could provide theoretical aids for deeply understanding the function of KRAS.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives.

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

Nature and Regulation of Protein Folding on the Ribosome.

Co-translational protein folding is an essential process by which cells ensure the safe and efficient production and assembly of new proteins in their functional native states following biosynthesis on the ribosome. In this review, we describe recent progress in probing the changes during protein synthesis of the free energy landscapes that underlie co-translational folding and discuss the critical coupling between these landscapes and the rate of translation that ultimately determines the success or otherwise of the folding process. Recent developments have revealed a variety of mechanisms by which both folding and translation can be modulated or regulated, and we discuss how these effects are utilised by the cell to optimise the outcome of protein biosynthesis.

Large scale peptide screening against main protease of SARS CoV-2.

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach.

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Reexamining the Emergence and Stability of the Square Cylinder Phase in Block Copolymers.

Recently, a few AB-type multiblock copolymers have been successfully designed to form stable square cylinder phase based on self-consistent field theory (SCFT) calculations. The previous works only identify the stability region of the square phase but not analyzing its stability, which is closely related to the free-energy landscape. In this work, we have reexamined the stability of the square phase in B 1 A 1 B 2 A 2 B 3 ${{{\rm B}}_{1}{{\rm A}}_{1}{{\rm B}}_{2}{{\rm A}}_{2}{{\rm B}}_{3}}$ linear pentablock and ( B 1 A B 2 )​ 5 ${({{\rm B}}_{1}{\rm A}{{\rm B}}_{2}{)}_{5}}$ star triblock copolymers by drawing the free-energy landscape with respect to the two dimensions of a rectangular unit cell. Our results demonstrate that the square phase continuously transfers to the rectangular phase as the degree of packing frustration is gradually released. Moreover, the prolate contour lines of the free-energy landscape indicate the weak stability of the square phase in the B 1 A 1 B 2 A 2 B 3 ${{{\rm B}}_{1}{{\rm A}}_{1}{{\rm B}}_{2}{{\rm A}}_{2}{{\rm B}}_{3}}$ copolymer. In contrast, the stability of the square phase is notably improved in the ( B 1 A B 2 )​ 5 ${({{\rm B}}_{1}{\rm A}{{\rm B}}_{2}{)}_{5}}$ copolymer due to its enhanced concentration of bridging configurations. Our work sheds light on the understanding of the stability of the square cylinder phase in block copolymers. Accordingly, we propose some possible strategies for further designing new AB-type block copolymer systems to obtain more stable square phase.

Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models.

Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function.

The NEK6 (NIMA-related kinase 6) serine/threonine kinase is a pivotal player in a multitude of cellular processes, including the regulation of the cell cycle and the response to DNA damage. Its significance extends to disease pathogenesis, as changes in NEK6 activity have been linked to the development of cancer. Non-synonymous single nucleotide polymorphisms (nsSNPs) in NEK6 have been linked to cancer as they alter the protein's native structure and function. The association between NEK6 activity and cancer development has prompted researchers to explore the effects of genetic variations within the NEK6 gene. Therefore, we utilized advanced computational tools to analyze 155 high-confidence nsSNPs in the NEK6 gene. From this analysis, 21 nsSNPs were identified as potentially harmful, raising concerns about their impact on NEK6 activity and cancer risk. These 21 mutations were then examined for structural alterations, and eight of nsSNPs (I51M, V76A, I134N, Y152D, R171Q, V186G, L237R, and C285S) were found to destabilize the protein. Among the destabilizing mutations screened, a specific mutation, R171Q, stood out due to its conserved nature. To understand its impact on the protein and conformation, all-atom molecular dynamics simulations (MDS) for 100 ns were performed for both Wildtype NEK6 (WT-NEK6) and R171Q. The simulations revealed that the R171Q variant was unstable and led to significant conformational changes in NEK6. This study provides valuable insights into NEK6 dysfunction caused by single amino acid alterations, offering a novel understanding of the molecular mechanisms underlying NEK6-related cancer progression.

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape.

Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔGTOTAL = - 30.78 kcal/mol) and 74977521 (ΔGTOTAL = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.