A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

A novel homozygous FAM92A gene (CIBAR1) variant further confirms its association with non-syndromic postaxial polydactyly type A9 (PAPA9).

Polydactyly is a very common digit anomaly, having extra digits in hands and/or toes. Non-syndromic polydactyly in both autosomal dominant and autosomal recessive forms are caused by disease-causing variants in several genes, including GLI1, GLI3, ZNF141, FAM92A, IQCE, KIAA0825, MIPOL1, STKLD1, PITX1, and DACH1. Whole exome sequencing (WES) followed by bi-directional Sanger sequencing was performed for the single affected individual (II-1) of the family to reveal the disease causative variant/gene. 3D protein modeling and structural molecular docking was performed to determine the effect of the identified mutation on the overall protein structure. WES revealed a novel biallelic missense variant (c.472G>C; p.Ala158Pro) in exon 6 of the FAM92A gene. The identified variant segregated perfectly with the disease phenotype using Sanger sequencing. Furthermore, Insilco analysis revealed that the variant significantly changes the protein secondary structure, and substantially impact the stability of FAM92A. We report the second FAM92A disease-causing mutation associated with recessive non-syndromic postaxial polydactyly. The data further confirms the contribution of FAM92A in limb development and patterning.

Genotypes and phenotypes of DNM1 encephalopathy.

BACKGROUND: Variants in the dynamin-1 (DNM1) gene typically cause synaptopathy, leading to developmental and epileptic encephalopathy (DEE). We aimed to determine the genotypic and phenotypic spectrum of DNM1 encephalopathy beyond DEE. METHODS: Electroclinical phenotyping and genotyping of patients with a DNM1 variant were conducted for patients undergoing next-generation sequencing at our centre, followed by a systematic review. RESULTS: Six patients with heterozygous DNM1 variants were identified in our cohort. Three had a typical DEE phenotype characterised by epileptic spasms, tonic seizures and severe-to-profound intellectual disability with pathogenic variants located in the GTPase or middle domain. The other three patients had atypical phenotypes of milder cognitive impairment and focal epilepsy. Genotypically, two patients with atypical phenotypes had variants located in the GTPase domain, while the third patient had a novel variant (p.M648R) in the linker region between pleckstrin homology and GTPase effector domains. The third patient with an atypical phenotype showed normal development until he developed febrile status epilepticus. Our systematic review on 55 reported cases revealed that those with GTPase or middle domain variants had more severe intellectual disability (p<0.001) and lower functional levels of ambulation (p=0.001) or speech and language (p<0.001) than the rest. CONCLUSION: DNM1-related phenotypes encompass a wide spectrum of epilepsy and neurodevelopmental disorders, with specific variants underlying different phenotypes.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

MOTIVATION: Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. RESULTS: A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. AVAILABILITYAND IMPLEMENTATION: The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a -7 kcal/mol binding energy score, the top-2 docked complex with a -6.9 kcal/mol binding energy score, and the top-3 docked complex with a -6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were -24.23 kcal/mol, -26.38 kcal/mol, and -25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as -17.23 kcal/mol (top-1 complex), -24.75 kcal/mol (top-2 complex), and -24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.

Towards a novel peptide vaccine for Middle East respiratory syndrome coronavirus and its possible use against pandemic COVID-19.

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging health concern due to its high mortality rate of 35%. At present, no vaccine is available to protect against MERS-CoV infections. Therefore, an in silico search for potential antigenic epitopes in the non-redundant proteome of MERS-CoV was performed herein. First, a subtractive proteome-based approach was employed to look for the surface exposed and host non-homologous proteins. Following, immunoinformatics analysis was performed to predict antigenic B and T cell epitopes that were used in the design of a multi-epitopes peptide. Molecular docking study was carried out to predict vaccine construct affinity of binding to Toll-like receptor 3 (TLR3) and understand its binding conformation to extract ideas about its processing by the host immune system. We identified membrane protein, envelope small membrane protein, non-structural protein ORF3, non-structural protein ORF5, and spike glycoprotein as potential candidates for subunit vaccine designing. The designed multi-epitope peptide then linked to ß-defensin adjuvant is showing high antigenicity. Further, the sequence of the designed vaccine construct is optimized for maximum expression in the Escherichia coli expression system. A rich pattern of hydrogen and hydrophobic interactions of the construct was observed with the TLR3 allowing stable binding of the construct at the docked site as predicted by the molecular dynamics simulation and MM-PBSA binding energies. We expect that the panel of subunit vaccine candidates and the designed vaccine construct could be highly effective in immunizing populations from infections caused by MERS-CoV and could possible applied on the current pandemic COVID-19.

Identification of Novel Genes and Pathways of Ovarian Cancer Using a Comprehensive Bioinformatic Framework.

Ovarian cancer (OC) is a significant contributor to gynecological cancer-related deaths worldwide, with a high mortality rate. Despite several advances in understanding the pathogenesis of OC, the molecular mechanisms underlying its development and prognosis remain poorly understood. Therefore, the current research study aimed to identify hub genes involved in the pathogenesis of OC that could serve as selective diagnostic and therapeutic targets. To achieve this, the dataset GEO2R was used to retrieve differentially expressed genes. The study identified a total of five genes (CDKN1A, DKK1, CYP1B1, NTS, and GDF15) that were differentially expressed in OC. Subsequently, a network analysis was performed using the STRING database, followed by the construction of a network using Cytoscape. The network analyzer tool in Cytoscape predicted 276 upregulated and 269 downregulated genes. Furthermore, KEGG analysis was conducted to identify different pathways related to OC. Subsequently, survival analysis was performed to validate gene expression alterations and predict hub genes, using a p-value of 0.05 as a threshold. Four genes (CDKN1A, DKK1, CYP1B1, and NTS) were predicted as significant hub genes, while one gene (GDF15) was predicted as non-significant. The adjusted P values of said predicted genes are 2.85E - 07, 5.49E - 06, 4.28E - 07, 1.43E - 07, and 3.70E - 07 for CDKN1A, DKK1, NTS, GDF15, and CYP1B1 respectively; additionally 6.08, 5.76, 5.74, 5.01, and 4.9 LogFc values of the said genes were predicted in GEO data set. In a boxplot analysis, the expression of these genes was analyzed in normal and tumor cells. The study found that three genes were highly expressed in tumor cells, while two genes (CDKN1A and DKK1) were more elevated in normal cells. According to the boxplot analysis for CDKN1A, 50% of tumor cells ranged between approx 3.8 and 5, while 50% of normal cells ranged between approx 6.9 and 7.9, which is greater than tumor cells. This shows that in normal cells, the CYP1B1 has a high expression level according to the GEPIA boxplot; addtionally the boxplot for DKK1 indicated that 50% of tumor cells ranged between approx 0 and 0.5, which was less than that of normal cells which ranged between approx 0.3 and 0.9. It shows that DKK1 is highly expressed in normal genes. Overall, the current study provides novel insights into the molecular mechanisms underlying OC. The identified hub genes and drug candidate targets could potentially serve as alternative diagnostic and therapeutic options for OC patients. Further research is needed to investigate the clinical significance of these findings and develop effective interventions that can improve the prognosis of patients with OC.

An integrative reverse vaccinology, immunoinformatic, docking and simulation approaches towards designing of multi-epitopes based vaccine against monkeypox virus.

Monkeypox is a viral zoonotic disease that is caused by the monkeypox virus (MPXV) and is mainly transmitted to human through close contact with an infected person, animal, or fomites which is contaminated by the virus. In the present research work, reverse vaccinology and several other bioinformatics and immunoinformatics tools were utilized to design multi-epitopes-based vaccine against MPXV by exploring three probable antigenic extracellular proteins: cupin domain-containing protein, ABC transporter ATP-binding protein and DUF192 domain-containing protein. Both cellular and humoral immunity induction were the main concerning qualities of the vaccine construct, hence from selected proteins both B and T-cells epitopes were predicted. Antigenicity, allergenicity, toxicity, and water solubility of the predicted epitopes were assessed and only probable antigenic, non-allergic, non-toxic and good water-soluble epitopes were used in the multi-epitopes vaccine construct. The developed vaccine was found to be potentially effective against MPXV and to be highly immunogenic, cytokine-producing, antigenic, non-toxic, non-allergenic, and stable. Additionally, to increase stability and expression efficiency in the host E. coli, disulfide engineering, codon adaptation, and in silico cloning were employed. Molecular docking and other biophysical approaches were utilized to evaluate the binding mode and dynamic behavior of the vaccine construct with TLR-2, TLR-4, and TLR-8. The outcomes of the immune simulation demonstrated that both B and T cells responded more strongly to the vaccination component. The detailed in silico analysis concludes that the proposed vaccine will induce a strong immune response against MPXV infection, making it a promising target for additional experimental trials.Communicated by Ramaswamy H. Sarma.

Computational Design of a Multi-Epitope Vaccine Against Porphyromonas gingivalis.

Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine-TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.

Design of a Multi-Epitope Vaccine against Tropheryma whipplei Using Immunoinformatics and Molecular Dynamics Simulation Techniques.

Whipple's disease is caused by T. whipplei, a Gram-positive pathogenic bacterium. It is considered a persistent infection affecting various organs, more likely to infect males. There is currently no licensed vaccination available for Whipple's disease; thus, the development of a chimeric peptide-based vaccine against T. whipplei has the potential to be tremendously beneficial in preventing Whipple's disease in the future. The present study aimed to apply modern computational approaches to generate a multi-epitope-based vaccine that expresses antigenic determinants prioritized from the core proteome of two T. whipplei whole proteomes. Using an integrated computational approach, four immunodominant epitopes were found from two extracellular proteins. Combined, these epitopes covered 89.03% of the global population. The shortlisted epitopes exhibited a strong binding affinity for the B- and T-cell reference set of alleles, high antigenicity score, nonallergenic nature, high solubility, nontoxicity, and excellent binders of DRB1*0101. Through the use of appropriate linkers and adjuvation with a suitable adjuvant molecule, the epitopes were designed into a chimeric vaccine. An adjuvant was linked to the connected epitopes to boost immunogenicity and efficiently engage both innate and adaptive immunity. The physiochemical properties of the vaccine were observed favorable, leading toward the 3D modeling of the construct. Furthermore, the vaccine's binding confirmation to the TLR-4 critical innate immune receptor was also determined using molecular docking and molecular dynamics (MD) simulations, which shows that the vaccine has a strong binding affinity for TLR4 (-29.4452 kcal/mol in MM-GBSA and -42.3229 kcal/mol in MM-PBSA). Overall, the vaccine described here has a promising potential for eliciting protective and targeted immunogenicity, subject to further experimental testing.

Whole Proteome-Based Therapeutic Targets Annotation and Designing of Multi-Epitope-Based Vaccines against the Gram-Negative XDR-Alcaligenes faecalis Bacterium.

This study involved therapeutic targets mining for the extremely drug-resistant bacterial species called Alcaligenes faecalis, which is known to infect humans. The infections caused by this species in different parts of the human body have been linked with a higher degree of resistance to several classes of antibiotics. Meanwhile, alternate therapeutic options are needed to treat these bacterial infections in clinical settings. In the current study, a subtractive proteomics approach was adapted to annotate the whole proteome of Alcaligenes faecalis and prioritize target proteins for vaccine-related therapeutics design. This was followed by targeted protein-specific immune epitope prediction and prioritization. The shortlisted epitopes were further subjected to structural design and in silico validation of putative vaccines against Alcaligenes faecalis. The final vaccine designs were also evaluated for potential interaction analysis with human TLR-2 through molecular docking. Finally, the putative vaccines were subjected to in silico cloning and immune simulation approaches to ensure the feasibility of the target-specific vaccine constructs in further experimental designs.

Vaccinomics to Design a Multi-Epitopes Vaccine for Acinetobacter baumannii.

Antibiotic resistance (AR) is the result of microbes' natural evolution to withstand the action of antibiotics used against them. AR is rising to a high level across the globe, and novel resistant strains are emerging and spreading very fast. Acinetobacter baumannii is a multidrug resistant Gram-negative bacteria, responsible for causing severe nosocomial infections that are treated with several broad spectrum antibiotics: carbapenems, ß-lactam, aminoglycosides, tetracycline, gentamicin, impanel, piperacillin, and amikacin. The A. baumannii genome is superplastic to acquire new resistant mechanisms and, as there is no vaccine in the development process for this pathogen, the situation is more worrisome. This study was conducted to identify protective antigens from the core genome of the pathogen. Genomic data of fully sequenced strains of A. baumannii were retrieved from the national center for biotechnological information (NCBI) database and subjected to various genomics, immunoinformatics, proteomics, and biophysical analyses to identify potential vaccine antigens against A. baumannii. By doing so, four outer membrane proteins were prioritized: TonB-dependent siderphore receptor, OmpA family protein, type IV pilus biogenesis stability protein, and OprD family outer membrane porin. Immuoinformatics predicted B-cell and T-cell epitopes from all four proteins. The antigenic epitopes were linked to design a multi-epitopes vaccine construct using GPGPG linkers and adjuvant cholera toxin B subunit to boost the immune responses. A 3D model of the vaccine construct was built, loop refined, and considered for extensive error examination. Disulfide engineering was performed for the stability of the vaccine construct. Blind docking of the vaccine was conducted with host MHC-I, MHC-II, and toll-like receptors 4 (TLR-4) molecules. Molecular dynamic simulation was carried out to understand the vaccine-receptors dynamics and binding stability, as well as to evaluate the presentation of epitopes to the host immune system. Binding energies estimation was achieved to understand intermolecular interaction energies and validate docking and simulation studies. The results suggested that the designed vaccine construct has high potential to induce protective host immune responses and can be a good vaccine candidate for experimental in vivo and in vitro studies.

In silico methods and tools for drug discovery.

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Rational Drug Design for Pseudomonas aeruginosa PqsA Enzyme: An in silico Guided Study to Block Biofilm Formation.

Pseudomonas aeruginosa is an opportunistic gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Such infections occur owing to biofilm formation that confers multidrug resistance and enhanced pathogenesis to the bacterium. In this study, we used a rational drug design strategy to inhibit the quorum signaling system of P. aeruginosa by designing potent inhibitory lead molecules against anthranilate-CoA ligase enzyme encoded by the pqsA gene. This enzyme produces autoinducers for cell-to-cell communication, which result in biofilm formation, and thus plays a pivotal role in the virulence of P. aeruginosa. A library of potential drug molecules was prepared by performing ligand-based screening using an available set of enzyme inhibitors. Subsequently, structure-based virtual screening was performed to identify compounds showing the best binding conformation with the target enzyme and forming a stable complex. The two hit compounds interact with the binding site of the enzyme through multiple short-range hydrophilic and hydrophobic interactions. Molecular dynamic simulation and MM-PBSA/GBSA results to calculate the affinity and stability of the hit compounds with the PqsA enzyme further confirmed their strong interactions. The hit compounds might be useful in tackling the resistant phenotypes of this pathogen.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation.

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Moleculer dynamics simulaiton revealed reciever domain of Acinetobacter baumannii BfmR enzyme as the hot spot for future antibiotics designing.

Acinetobacter baumannii is an alarming nosocomial pathogen that is resistant to multiple drugs. The pathogen is forefront of scientific attention because of high mortality and morbidity found for its complications in the past decade. As a consequence, identification of novel drug candidates and subsequent designing of novel chemical scaffolds is an imperative need of time. In the present study, we used a recently reported structure of BfmR enzyme and performed structure based virtual screening, MD simulation and binding free energies calculations. MD simulation revealed a profound movement of the best-characterized inhibitor towards the α4-ß5-α5 face of the enzyme receiver domain, thus indicating its high affinity for this site compared to phosphorylation. Furthermore, it was observed that the enzyme and enzyme-inhibitor complex have high structure stability with mean RMSD of 1.2 and 1.1 Å, respectively. Binding free energy calculations for the complex unraveled high stability with MMGBSA score of -26.21 kcal/mol and MMPBSA score of -1.47 kcal/mol. Van der Waal energy was found highly favorable with value of -30.25 kcal/mol and dominated significantly the overall binding energy. Furthermore, a novel WaterSwap assay was used to circumvent the limitations of MMGB/PBSA that complements the inhibitor affinity for enzyme active pocket as depicted by the low convergence of Bennett, TI and FEP algorithms. Results yielded from this study will not only give insight into the phenomena of inhibitor movement towards the enzyme receiver domain, but will also provide a useful baseline for designing derivatives with improved biological and pharmacokinetics profiles. Communicated by Ramaswamy H. Sarma.