Fragment based G-QSAR and molecular dynamics based mechanistic simulations into hydroxamic-based HDAC inhibitors against spinocerebellar ataxia

Inhibitors against New Delhi metallo-betalactamase-1 (NDM-1) and its variants endemic in Indian settings along with the laboratory functional gain mutant of NDM-1

PURPOSE

The emergence of NDM-1 producing bacteria has become common in both hospital and community settings, but no inhibitor has yet been available for clinical treatment. Hence, demanding the urgent need of NDM-1 inhibitors, we initiated to screen broad spectrum inhibitors against NDM natural variants and laboratory mutant.

METHODS

We used docking and molecular dynamics simulations, in silico pharmacokinetic investigations, and density functional theory calculation to characterize molecules. Furthermore, an in vitro study, including MIC, kinetics, and fluorescence study were carried out to confirm the efficacies of the selected compounds.

RESULTS

According to the findings of the computational studies, three compounds were effective against NDM variants. Fourfold reduction in MIC of imipenem and meropenem was observed when combined with inhibitors (D2573, D2148, and D63) against blaNDM-1, blaNDM-4, blaNDM-6, and blaNDM-1Q123A, while twofold reduction in MIC of imipenem and meropenem was observed against blaNDM-5 and blaNDM-7. Similarly in the presence of inhibitors (D2573, D2148, and D63) the efficiency of nitrocefin hydrolysis by NDM-4, NDM-6, and Q123A decreases to much more extent as compared to NDM-5 and NDM-7. These results showed that the efficacy of these broad spectrum inhibitors decreases with increasing resistance of NDM variants.

CONCLUSION

This is the first time inhibitors were tested against different NDM natural variants which are endemic in Indian settings. Moreover, a functional gain laboratory mutant was also checked for their efficacies. We may propose these molecules for the pre-clinical trial to further translate.

Repurposing FDA approved drug molecules against A B C classes of β-lactamases: a computational biology and molecular dynamics simulations study

β-lactamase are the main resistance factor for β-lactam antibiotics in Gram-negative bacteria. Since β-lactam antibiotics are being utilised as an antimicrobial agents extensively for the past 70 years, a large number of β-lactam-inactivating β-lactamases have been produced by bacteria. Here, we employed a structure-based drug discovery approach to identify and assess the efficacy of a potential medication that might block the β-lactamases which hydrolyse antibiotics. The FDA-approved medications were subjected to virtual screening, molecular docking, molecular dynamics simulations, density functional theory, and covalent docking against the β-lactamases. We identified diosmin, hidrosmin, monoxuritin and solasulfone as β-lactamase inhibitors which are authorised for therapeutic use in humans. These medications interact in a remarkable variety of non-covalent ways with the conserved residues in the substrate-binding pocket of the β-lactamases. Diosmin has been identified as an inhibitor that binds covalently to the NDM-1 a class B metallo-betalactamase. After experimental validation and clinical demonstration, this study offers adequate evidence for the therapeutic use of these drugs for controlling multidrug resistance.Communicated by Ramaswamy H. Sarma.

Discovery of novel HDAC8 inhibitors from natural compounds by in silico high throughput screening

A class I histone deacetylase HDAC8 is associated with several diseases, including cancer, intellectual impairment and parasite infection. Most of the HDAC inhibitors that have so far been found to inhibit HDAC8 limit their efficacy in the clinic by producing toxicities. It is therefore very desirable to develop specific HDAC8 inhibitors. The emergence of HDAC inhibitors derived from natural sources has become quite popular. In recent decades, it has been shown that naturally occurring HDAC inhibitors have strong anticancer properties. A total of 0.2 million natural compounds were screened against HDAC8 from the Universal Natural Product Database (UNPD). Molecular docking was performed for these natural compounds and the top six hits were obtained. In addition, molecular dynamics (MD) simulations were used to evaluate the structural stability and binding affinity of the inhibitors, which showed that the protein-ligand complexes remained stable throughout the 100 ns simulation. MM-PBSA method demonstrated that the selected compounds have high affinity towards HDAC8. We infer from our findings that Hit-1 (-29.35 kcal mol-1), Hit-2 (-29.15 kcal mol-1) and Hit-6 (-30.28 kcal mol-1) have better binding affinity and adhesion to ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics against HDAC8. To compare our discussions and result in an effective way. We performed molecular docking, MD and MM-PBSA analysis for the FDA-approved drug romidepsin. The above results show that our hits show better binding affinity than the compound romidepsin (-12.03 ± 4.66 kcal mol-1). The important hotspot residues Asp29, Ile34, Trp141, Phe152, Asp267, Met274 and Tyr306 have significantly contributed to the protein-ligand interaction. These findings suggest that in vitro testing and additional optimization may lead to the development of HDAC8 inhibitors.Communicated by Ramaswamy H. Sarma.

Broad-Spectrum Inhibitors against Class A, B, and C Type β-Lactamases to Block the Hydrolysis against Antibiotics: Kinetics and Structural Characterization

The emergence of antibiotic resistance has led to a global crisis for the physician to handle infection control issues. All antibiotics, including colistin, have lost efficiency against emerging drug-resistant bacterial strains due to the production of metallo-β-lactamases (MBLs) and serine-β-lactamases (SBLs). Therefore, it is of the utmost importance to design inhibitors against these enzymes to block the hydrolytic action against antibiotics being used. Although various novel β-lactamase inhibitors are being authorized or are under clinical studies, the coverage of their activity spectrum does not include MDR organisms expressing multiple classes of β-lactamases at a single time. This study reports three novel broad-spectrum inhibitors effective against both SBLs and MBLs. Virtual screening, molecular docking, molecular dynamics simulations, and an in silico pharmacokinetic study were performed to identify the lead molecules with broad-spectrum ability to inhibit the hydrolysis of β-lactam. The selected compounds were further assessed by in vitro cell assays (MIC, 50% inhibitory concentration [IC₅₀], kinetics, and fluorescence against class A, B, and C type β-lactamases) to confirm their efficacies. A 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay was performed to check the toxicity of screened lead molecules. All three selected inhibitors were found to reduce MIC and showed good affinity against all the SBLs and MBLs produced by class A, B, and C type β-lactamases. These nontoxic novel non-β-lactam broad-spectrum inhibitors bind to the active site residues of selected β-lactamases, which are crucial for β-lactam antibiotic hydrolysis. These inhibitors may be proposed as a future drug candidate in combination with antibiotics as a single formulation to control infection caused by resistant strains. Hence, this study plays a significant role in the cure of infections caused by antibiotic-resistant bacteria.

IMPORTANCE Several inhibitors for usage in conjunction with antibiotics have been developed. However, to date, there is no commercially available broad-spectrum β-lactamase inhibitor that targets both MBLs and SBLs. Here, we showed three novel broad-spectrum inhibitors with promising results through computational techniques and in vitro studies. These inhibitors are effective against both SBLs and MBLs and hence could be used as future drug candidates to treat infections caused by multidrug-resistant bacteria producing both types of enzymes (SBLs and MBLs).

Repurposing drug molecule against SARS-Cov-2 (COVID-19) through molecular docking and dynamics: a quick approach to pick FDA-approved drugs

A novel coronavirus known as severe acute respiratory syndrome is rapidly spreading worldwide. The international health authorities are putting all their efforts on quick diagnosis and placing the patients in quarantine. Although different vaccines have come for quick use as prophylactics, drug repurposing seems to be of paramount importance because of inefficient therapeutic options and clinical trial limitations. Here, we used structure-based drug designing approach to find and check the efficacy of the possible drug that can inhibit coronavirus main protease which is involved in polypeptide processing to functional protein. We performed virtual screening, molecular docking and molecular dynamics simulations of the FDA-approved drugs against the main protease of SARS-CoV-2. Using well-defined computational methods, we identified amprenavir, cefoperazone, riboflavin, diosmin, nadide and troxerutin approved for human therapeutic uses, as COVID-19 main protease inhibitors. These drugs bind to the SARS-CoV-2 main protease conserved residues of substrate-binding pocket and formed a remarkable number of non-covalent interactions. We have found diosmin as an inhibitor which binds covalently to the COVID-19 main protease. This study provides enough evidences for therapeutic use of these drugs in controlling COVID-19 after experimental validation and clinical demonstration.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Structure-Based Screening of Non-β-Lactam Inhibitors against Class D β-Lactamases: An Approach of Docking and Molecular Dynamics

The manifestation of class D β-lactamases in the community raises significant concern as they can hydrolyze carbapenem antibiotics. Hence, it is exceptionally alluring to design novel inhibitors. Structure-based virtual screening using docking programs and molecular dynamics simulations was employed to identify two novel non-β-lactam compounds that possess the ability to block different OXA variants. Furthermore, the presence of a nonpolar aliphatic amino acid, valine, near the active site serine, was identified in all OXA variants that can be accounted to block the catalytic activity of OXA enzymes.

Unravelling the interaction of glipizide with human serum albumin using various spectroscopic techniques and molecular dynamics studies

Glipizide is known to stimulate insulin secretion by β-cells of the pancreas. It is a second-generation sulfonylurea drug used in the management of type 2 diabetes. The shorter biological half-life makes it a suitable candidate to be designed as a controlled release formulation. Human serum albumin (HSA), a major plasma protein plays a crucial role in the transportation of drugs, hormones, fatty acids, and many other molecules and determines their physiological fate and biodistribution. In this study, the interaction of glipizide with HSA was investigated under physiological conditions using multi-spectroscopic techniques corroborated with molecular docking and dynamics approach. It was found that glipizide integrates to HSA with a binding constant in the order of 10⁵ M^-1. The mode of fluorescence quenching by glipizide is static in nature with one binding site. Glipizide preferentially interacts with sub-domain IIA of HSA and their complexion is thermodynamically favorable. This interaction results in the loss of α-helical content of HSA. The energy transfer efficiency from HSA to glipizide was found to be 26.72%. In silico molecular docking and simulation studies ratified in vitro findings and revealed that hydrogen bonds and hydrophobic interactions are accountable for glipizide-HSA complex formation.Communicated by Ramaswamy H. Sarma.

Pyrazinamide drug resistance in RpsA mutant (∆438A) of Mycobacterium tuberculosis: Dynamics of essential motions and free-energy landscape analysis

Pyrazinamide is an essential first-line antitubercular drug which plays pivotal role in tuberculosis treatment. It is a prodrug that requires amide hydrolysis by mycobacterial pyrazinamidase enzyme for conversion into pyrazinoic acid (POA). POA is known to target ribosomal protein S1 (RpsA), aspartate decarboxylase (PanD), and some other mycobacterial proteins. Spontaneous chromosomal mutations in RpsA have been reported for phenotypic resistance against pyrazinamide. We have constructed and validated 3D models of the native and Δ438A mutant form of RpsA protein. RpsA protein variants were then docked to POA and long range molecular dynamics simulations were carried out. Per residue binding free-energy calculations, free-energy landscape analysis, and essential dynamics analysis were performed to outline the mechanism underlying the high-level PZA resistance conferred by the most frequently occurring deletion mutant of RpsA. Our study revealed the conformational modulation of POA binding site due to the disruptive collective modes of motions and increased conformational flexibility in the mutant than the native form. Residue wise MMPBSA decomposition and protein-drug interaction pattern revealed the difference of energetically favorable binding site in the wild-type (WT) protein in comparison with the mutant. Analysis of size and shape of minimal energy landscape area delineated higher stability of the WT complex than the mutant form. Our study provides mechanistic insights into pyrazinamide resistance in Δ438A RpsA mutant, and the results arising out of this study will pave way for design of novel and effective inhibitors targeting the resistant strains of Mycobacterium tuberculosis.

Conformal prediction of HDAC inhibitors

The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Insight into the inhibitor discrimination by FLT3 F691L

Fms-like tyrosine kinase 3 (FLT3) belongs to the receptor tyrosine kinase family and expressed in hematopoietic progenitor cells. FLT3 gene mutations are reported in ~30% of acute myeloid leukemia cases. FLT3 kinase domain mutation F691L is one of the common causes of acquired resistance to the FLT3 inhibitors including quizartinib. MZH29 and crenolanib were previously reported to inhibit FLT3 F691L. However, crenolanib was reported for the moderate inhibition. We found that Glu661and Asp829 were the most significant residues to target the FLT3 F691L which contribute most significantly to the binding energy with MZH29 and crenolanib. These interactions were found absent with quizartinib. Further free energy landscape analysis revealed that FLT3 F691L bound to MZH29 and crenolanib was more stable as compared to quizartinib.

Structural investigations on mechanism of lapatinib resistance caused by HER-2 mutants

HER-2 belongs to the human epidermal growth factor receptor (HER) family. Via different signal transduction pathways, HER-2 regulates normal cell proliferation, survival, and differentiation. Recently, it was reported that MCF10A, BT474, and MDA-MB-231 cells bearing the HER2 K753E mutation were resistant to lapatinib. Present study revealed that HER-2 mutant K753E showed some contrasting behaviour as compared to wild, L768S and V773L HER-2 in complex with lapatinib while similar to previously known lapatinib resistant L755S HER-2 mutant. Lapatinib showed stable but reverse orientation in binding site of K753E and the highest binding energy among studied HER2-lapatinib complexes but slightly lesser than L755S mutant. Results indicate that K753E has similar profile as L755S mutant for lapatinib. The interacting residues were also found different from other three studied forms as revealed by free energy decomposition and ligplot analysis.

Role of pncA gene mutations W68R and W68G in pyrazinamide resistance

Mycobacterium tuberculosis (Mtb) resistance toward anti-tuberculosis drugs is a widespread problem. Pyrazinamide (PZA) is a first line antitubercular drug that kills semi-dormant bacilli when converted into its activated form, that is, pyrazinoic acid (POA) by Pyrazinamidase (PZase) enzyme coded by pncA gene. In this study, we conducted several analyses on native and mutant structures (W68R, W68G) of PZase before and after docking with the PZA drug to explore the molecular mechanism behind PZA resistance caused due to pncA mutations. Structural changes caused by mutations were studied with respect to their effects on functionality of protein. Docking was performed to analyze the protein-drug binding and comparative analysis was done to observe how the mutations affect drug binding affinity and binding site on protein. Native PZase protein was observed to have the maximum binding affinity in terms of docking score as well as shape complementarity in comparison to the mutant forms. Molecular dynamics simulation analyses showed that mutation in the 68th residue of protein results in a structural change at its active site which further affects the biological function of protein, that is, conversion of PZA to POA. Mutations in the protein thereby led to PZA resistance in the bacterium due to the inefficient binding.

Simulation Based Investigation of Deleterious nsSNPs in ATXN2 Gene and Its Structural Consequence Toward Spinocerebellar Ataxia

Spinocerebellar degeneration, termed as ataxia is a neurological disorder of central nervous system, characterized by limb in-coordination and a progressive gait. The patient also demonstrates specific symptoms of muscle weakness, slurring of speech, and decreased vibration senses. Expansion of polyglutamine trinucleotide (CAG) within ATXN2 gene with 35 or more repeats, results in spinocerebellar ataxia type-2. Protein ataxin-2 coded by ATXN2 gene has been reported to have a crucial role in translation of the genetic information through sequestering the histone acetyl transferases (HAT) resulting in a state of hypo-acetylation. In the present study, we have evaluated the outcome for 122 non synonymous single nucleotide polymorphisms (nsSNPs) reported within ATXN2 gene through computational tools such as SIFT, PolyPhen 2.0, PANTHER, I-mutant 2.0, Phd-SNP, Pmut, MutPred. The apo and mutant (L305V and Q339L) form of structures for the ataxin-2 protein were modeled for gaining insights toward 3D spatial arrangement. Further, molecular dynamics simulations and structural analysis were performed to observe the brunt of disease associated nsSNPs toward the strength and secondary properties of ataxin-2 protein structure. Our results showed that, L305V is a highly deleterious and disease causing point substitution. Analysis based on RMSD, RMSF, Rg, SASA, number of hydrogen bonds (NH bonds), covariance matrix trace, projection analysis for eigen vector demonstrated a significant instability and conformation along with rise in mutant flexibility values in comparison to the apo form of ataxin-2 protein. The study provides a blue print of computational methodologies to examine the ataxin-blend SNPs. J. Cell. Biochem. 119: 499-510, 2018. © 2017 Wiley Periodicals, Inc.

In silico binding mechanism prediction of benzimidazole based corticotropin releasing factor-1 receptor antagonists by quantitative structure activity relationship, molecular docking and pharmacokinetic parameters calculation

Despite the various research efforts toward the treatment of stress-related disorders, the drug has not yet launched last 20 years. Corticotropin releasing factor-1 receptor antagonists have been point of great interest in stress-related disorders. In the present study, we have selected benzazole scaffold-based compounds as corticotropin releasing factor-1 antagonists and performed 2D and 3D QSAR studies to identify the structural features to elucidating the binding mechanism prediction. The best 2D QSAR model was obtained through multiple linear regression method with r² value of .7390, q² value of .5136 and pred_r² (predicted square correlation coefficient) value of .88. The contribution of 2D descriptor, T_2_C_1 was 60% (negative contribution) and 4pathClusterCount was 40.24% (positive contribution) in enhancing the activity. Also 3D QSAR model was statistically significant with q² value of .9419 and q²_se (standard error of internal validation) value of .19. Statistical parameters results prove the robustness and significance of both models. Further, molecular docking and pharmacokinetic analysis was performed to explore the scope of investigation. Docking results revealed that the all benzazole compounds show hydrogen bonding with residue Asn283 and having same hydrophobic pocket (Phe286, Leu213, Ile290, Leu287, Phe207, Arg165, Leu323, Tyr327, Phe284, and Met206). Compound B14 has higher activity compare to reference molecules. Most of the compounds were found within acceptable range for pharmacokinetic parameters. This work provides the extremely useful leads for structural substituents essential for benzimidazole moiety to exhibit antagonistic activity against corticotropin releasing factor-1 receptors.

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r²) value of 0.6774, cross-validated correlation coefficient (q²) of 0.6157 and co-relation coefficient for external test set (pred_r²) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

Background

Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer’s is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer’s towards development of effective AD therapeutics.

Results

In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings.

Conclusions

To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3108-1) contains supplementary material, which is available to authorized users.