Investigation of novel indole-based HIV-1 protease inhibitors using virtual screening and text mining

Kader Sahin*

Abstract

Human immunodeficiency virus type 1 protease (HIV-1 PR) inhibitors have been used as possible therapeutic agents for HIV-1 infection in clinical study. Most of the HIV therapyrelated problems usually stem from long-term opioid usage. The rapid development of drug-resistant variants limits the long-term effectiveness of current inhibitors as therapeutic agents. In addition, different side effects were reported. Further drug development is required to design new compounds which have similar efficacy as the drugs currently used in HIV infection but without having undesirable side effects. Indole derivatives were considered as one of the effective HIV inhibitors. Indole is an important fragment used in many FDA-approved medicines and used in various diseases. For this purpose, in this study the molecules containing ”indole” keywords in their fragments are taken from the Specs-SC database which includes 212520 small molecules. 5194 molecules that include indole keywords are selected. These selected molecules are then screened against HIV-1 PR target protein using molecular docking simulations. Then the molecules are ranked according to the their docking scores. Top docking poses of 10 ligands and FDA approved drug Amprenavir are subjected to 100 ns Molecular Dynamics (MD) simulations. Thus, by using combination of text mining and integrated molecular modeling approaches, we identified novel indole-based hits against HIV-1 PR.

Key words: HIV protease, virtual screening, text mining, molecular docking simulations, molecular dynamics simulations, indoles

1 Introduction

The human immunodeficiency virus (HIV) encodes three enzymes that are necessary for a productive life cycle. Today, in the Acquired Immune Deficiency Syndrome (AIDS) pandemic, about 33 million people around the world are believed to be diagnosed with HIV (for HIV/AIDS, 2019). The disease can not be completely eradicated despite efficacy of highly active antiretroviral therapy (HAART) (Tozser, 2001). In addition, vaccine development has been extremely challenging (Walker & Burton, 2008). The main challenge to restrict current treatments is the rapid development of drug resistance (Condra et al., 1995). A variety of anti-retroviral drugs are currently available for medical use and are either reverse transcriptase inhibitors or viral protease inhibitors. Some excellent reviews on HIV drug design (Bourinbaiar, 2009), anti-HIV therapy (Agrawal, Lu, Jin, & Alkhatib, 2006), HIV-1 reverse transcriptase inhibitors (Campiani et al., 2002; MenendezArias, Matamoros, & Cases-Gonzalez, 2006), and HIV-1 integrase inhibitors are also available (Deng, Dayam, Al-Mawsawi, & Neamati, 2007). HAART is known as the most effective form of treatment for AIDS, and protease inhibitors in HAART play a very significant role. HIV-1 PR is the enzyme which cleaves the viral polyproteins Gag and Gag-Pol into mature. Protease is a valuable drug target because Protease inhibition leads to immature noninfectious virions (Gottlinger, Sodroski, & Haseltine, 1989; Louis, Ishima, Torchia, & Weber, 2007). The HIV-1 PR may recognize the sequence of PhePro or Tyr-Pro as the cleavage site unique to retroviruses. In 1989, the HIV-1 PR was first characterized structurally by X-ray crystallography (Wlodawer & Erickson, 1993). The HIV-1 PR is a symmetric homodimer of C2. The homodimeric protein consists of two similar subunits of 99 amino acids, each containing one catalytic acid Asp25, 125. Activity of HIV-1 PR enzymes may be inhibited by blocking the protease’s active site. After the HIV diagnosis, the US Food and Drug Administration (FDA) has approved 26 anti-HIV drugs. Ten of these drugs are inhibitors of the HIV PR. These inhibitors include: amprenavir, indinavir, nelfinavir, saquinavir, ritonavir, fosamprenavir, lopinavir, tipranavir, atazanavir and darunavir. Unfortunately, most of these inhibitors have side effects in the long-term treatments. The most common side effects are metabolic syndromes caused by the HIV PR, such as insulin-resistance, dyslipidemia, lipodystrophy/lipoatrophy, cardiovascular and cerebrovascular diseases (Bozzette, Ake, Tam, Chang, & Louis, 2003; Hruz, 2008; Kotler, 2008; Soontornniyomkij et al., 2014). HIV PR plays a key role in the virus’ life cycle, its inhibition stops the viral particles from maturing and renders them non-infectious. HIV PR remains one of the key goals for the production of novel HIV therapies more than 25 years after its discovery. Recently, allosteric control of HIV PR activity was recognized as a novel way of limiting drug-resistance growth (Zhang, Chen, & Yang, 2015).
The HIV PR inhibitors approved by the FDA share the same structural similarities and a similar binding pattern, which may cause some of the common side effects of the protease-containing regimens. The development of new, effective, efficient and safe HIV inhibitors is therefore still an urgent need and possesses a high priority for medical research. Indole has always been of concern for medicinal and organic chemists because of the unique composition. Indole has always been of concern to medicinal and organic chemists because of the unique composition. Indole’s heterocyclic ring system combines five-membered pyrrole and six-membered benzene ring.
The chemistry of this bicyclic arene is well known in various research articles (Bandini, 2013; Taber & Tirunahari, 2011). There are many indole-based FDA approved drug molecules. Despite extensive research, these compounds which include indole derivatives have not yet been studied in detail in terms of molecular mechanism. Recent progress shows that an increasing number of small-molecule HIV drug candidates for clinical development are based on high-throughput assay and driven by a crystal structure (Klumpp & Mirzadegan, 2006; Yeung et al., 2013). Docking is a common virtual structurebased approach used in the design of molecules that are of biological interest (Kumar, Rathi, & Kini, 2020; Panda, Saxena, & Guruprasad, 2020; Saikia & Bordoloi, 2019). This enables predicting the complementary stereoelectronic compatibility of a possible bioactive ligand with its biomolecular target. Molecular docking studies that provide an initial insight into protein-ligand interactions, understanding the maintenance of these interactions and performing dynamic studies such as molecular dynamics (MD) simulations are often crucial (Bathula et al., 2020; Rather et al., 2020) . This research paper focuses strongly on small molecule libraries to investigate new HIV-1 PR inhibitors using text mining approaches and virtual screening. To this end, 212520 molecules obtained from Specs-SC small molecule database are prepared here by using the Marvinsketch software in the text file format of IUPAC. This text file is screened and 5194 molecules are extracted to identify the compounds that include the word ”indol” by utilizing Python-based text mining.

2 Materials and Methods

Binary QSAR models

Computational approaches such as ligand and structure based would decrease the times required for the identification of novel inhibitors for specific target with the advantage of also being cost-efficient (Cutinho et al., 2020). Successful drug design requires molecular-level understanding of the drug’s impact on the disease. MetaCore is an innovative data analysis program based on protein-protein interactions, protein-DNA interactions, infection and toxicity, etc. MetaDrug is a platform designed to investigate the effects on the human body of small compounds. MetaDrug is also used to solve issues such as toxicity, drug action mechanism and off-target effects. MetaCore/MetaDrug predicts therapeutic efficacy, pharmacokinetics and ADME/toxicity using different QSAR models of examined compounds. Tanimoto Prioritization (TP) indicating similarities between the studied molecule and the compounds in the training set were determined to seek similarity in the QSAR models for all structures. The molecules’ therapeutic activities are measured using binary
QSAR models for 25 common diseases and constructed 26 different toxicity QSAR models can be used to investigate the pharmacokinetic profiles of screened compounds. The quality of binary classifications was evaluated using the Matthews Correlation Coefficient (MCC) (Matthews, 1975). MCC takes a value from -1 to 1. If the MCC value is close to 1, the prediction is good if not, it is weak. RMSE measures the amount of errors by comparing the predicted and observed values for the molecules evaluated (Todeschini, Ballabio, & Grisoni, 2016). High R2 and low RMSE values point out the accuracy of the constructed models. Indole fragments are oftenly used fingerprints for different diseases. Thus, in the current research, a text mining study is considered for identifying the indole derivatives from a large database. For this aim, 212520 molecules obtained from NCI database have been prepared using Marvinsketch code in IUPAC text file format (MarvinSketch 18.30.0, Chemaxon, 2018). To find the compounds that include ”indol” phrase, using Python-based text mining, this text file is screened and 5194 molecules are obtained. Text mining helps to find molecules of interest by screening a large database with keywords quickly (Krallinger & Valencia, 2005). On the MetaCore/MetaDrug system, selected indole-based 5194 molecules have been translated to .sdf file format to predict therapeutic activity values. The molecules with higher values than 0.5 are considered for HIV therapeutic activity prediction and then evaluated in the MetaCore/MetaDrug system for 26 specific QSAR toxicity models. For structure-based analyses, 476 potent and non-toxic compounds are identified.

Data collection

In this study, 212520 molecules obtained from Specs-SC are prepared in IUPAC text file format using Marvinsketch software (MarvinSketch 18.30.0, Chemaxon, 2018). This text file is screened to find indole-based molecules using Python-based text mining and we obtain 5194 molecules. Text mining helps to find interested molecules by quickly screening interested compounds from large database using keywords (Krallinger & Valencia, 2005).

Ligand Preparation

Ligand preparation consists of the analysis of a 3D structure of a ligand, generation and optimization steps. 5194 compounds are prepared here using the OPLS-2005 force field (Banks et al., 2005) LigPrep module (Kakarala, Jamil, & Devaraji, 2014) of the Maestro Molecular Modeling Suit. A problem to be addressed is the ionization of the ligand in physiological conditions. Epik module (Shelley et al., 2007) has been used at the physiological pH of 7.4 for potential ionization states. All possible stereoisomers and tautomers are also produced and upto 32 structures per ligand are considered.

Protein Preparation

The protein data bank (PDB) provides 3D structures of various nucleic acids, proteins, protein fragments and protein ligand complexes. HIV-1 protease target is taken from the PDB (3NU3) (Shen, Wang, Kovalevsky, Harrison, & Weber, 2010a). We work with this protein because Amprenavir was the first

PR inhibitor and 3NU3 is a wild Type HIV-1 PR with Antiviral Drug

Amprenavir. The missing side chains, backbone atoms and loops are fixed with Prime (Jacobson et al., 2004a). The effect of dissolution, depending on the water molecules surrounding the binding pocket(¡ 5.0 Å), should be evaluated. For protonation states, structural optimization and minimization, the PROPKA and OPLS-2005 force field are used, respectively. (Protein Preparation, Version 2.5, Schrodinger, LLC, New York, 2011).

Molecular Docking Simulations

Molecular docking studies investigate interactions occurring in protein-protein or ligand-protein complexes and rank candidate poses according to affinity scoring functions (Sledz & Caflisch, 2018). Doking processes predict ligands to bind with the most appropriate conformation in the binding pocket of the target protein using different algorithms. Glide is used to carry out molecular docking between the investigated ligands and HIV-1 PR to obtain high docking scores (Friesner et al., 2004). The conformations obtained during the docking are ranked using the Glide score function (Raha & Merz, 2004). The binding pocket is set by residues within 10 Åvicinity of co-crystallized ligand. All ligands are initially docked into the binding pocket of HIV-1 PR using a grid-based docking program Glide standard precision (SP) of Maestro Molecular Modeling pocket (Friesner et al., 2006; Jacobson et al., 2004a) to obtain the best docking poses and 10 docking poses are requested for each ligand (Roy, Kumar, Baig, Masajik, & Provaznik, 2015; Subhani, Jayaraman, & Jamil, 2015).

Molecular Dynamics (MD) Simulations

There is evidence that some systems require MD simulations to discover proper binding fit (Hou, Wang, Li, & Wang, 2011; Zhao et al., 2020). Long MD simulations can find more and energetically favorable configurations. We implement MD simulations up to 100 ns using Desmond V 4.9 to investigate conformational stability of the complexes of selected hit molecules with HIV-1 PR (Desmond, Version 4.9, Schrodinger, LLC, New York, 2011). The complex structures are solvated in the orthorhombic simple point charge (SPC) water model (Berendsen, Postma, Gunsteren, & Hermans, 1981). The systems are neutralized with counter ions (0.15 M NaCI solution). The system is set as Lennard-Jones interactions cut off of 10 Åon periodic boundary conditions (Friesner et al., 2006). 2.0 fs time step is used in the integration steps. Nose Hoover thermostat (Hoover, 1985) and Martyna-Tobias Klein protocols (Ma & Tuckerman, 2010) are used to control the temperature and pressure of the systems at 310 K and 1 bar, respectively.

Molecular Mechanics/Generalized Born Surface Area (MM/GBSA)

Protein-ligand complexes are also analyzed by MM/GBSA to estimate free binding energies of studied ligands. The MM/GBSA calculations are applied to complex structures using Schrodinger’s Prime module (Jacobson et al., 2004b). The frames of ligand-protein complexes are extracted from the MD trajectory of each complexes at every 10 ps (Tripathi, Muttineni, & Singh, 2013). VSGB solvation model (Li et al., 2011) which is a realistic parametrization of the solvation and OPLS-2005 force field (Banks et al., 2005) were are for protein flexibility.

Shape Base Screening

Shape Base Screening (SBS) aims to detect new compounds with identical structure by comparison to a known compound structure. A molecule’s electrostatic properties and structure are important on how it attaches to the target protein. It is most possible that compounds with similar structure, steric and electrostatic properties with a known compound would bind to the receptor. SBS is an important way to easily superpose a wide number of molecules in SAR studies. SBS can check very many conformers in a very short time and was shown to be performing better than other approaches for virtual screening studies for specific targets (Sastry, Dixon, & Sherman, 2011).
The purpose of this study is to perform a virtual screening of the small molecule Specs-SC database that includes 212520 compounds for HIV-1 PR inhibition and to identify new small hit compounds. Therefore, we screened all Specs-SC database to find indole-based compounds using text mining. Flowchart of all applied procedure in the current study is shown in Figure 1.

3 Results and Discussion

In our group, virtual screening of different ligand databases have been performed vastly in recent years by an in-house script and it is shown that the obtained results by this screening algorithm which is a hybrid algorithm of ligand- and target-driven based screening techniques gave successful results (Sahin, B., F., & S., 2019; Sahin & S., 2020). Thus, in the current study, this hybrid algorithm is applied for the identification of novel inhibitors against HIV1 PR target proteins. In this study, all compounds imported from Specs-SC database in .sdf file format are converted to .name format (IUPAC text file) with MarvinSketch program (MarvinSketch 18.30.0, Chemaxon, 2018). 5194 compounds containing the expression ”indol” in the IUPAC names are detected. Selected 5194 compounds are converted to .sdf file format. Two dimensional molecular structures of selected 5194 molecules are converted to energetically optimized 3D structures using LigPrep module of Schrodinger Maestro Molecular Modeling pocket. Glide/SP software performed the docking procedure to determine the possible ligand binding poses of potential HIV-1 PR inhibitors. Initially for all ligands, the grid-based docking are performed in the HIV-1 PR binding site. Top-10 docking scored compounds are selected and 100-ns MD simulations are performed for each complexes. Table 1 shows the results of molecular docking of selected 5 hit ligands. For further simulations the top-docking pose of screened compounds at the binding pocket is considered. 100-ns MD simulations are performed on the initial structure of HIV-1 PR ligand complexes resulting from the docking calculations to assess structural stability within a nanosecond time scale (Table S1). Figures S5 and S6 show change of MM/GBSA scores of studied 5 compounds throughout the 100 ns MD simulation time. Figures S5 and S6 also show corresponding MM/GBSA scores of approved drug Amprenavir. Results showed that some of the selected hits have close MM/GBSA scores compared to approved drug.
Known molecule amprenavir is taken as a template and for 476 compounds shape based screening is performed. Table 2 shows the 3 hit compounds obtained as a result of shape based screening.
In Figure 2, RMSD plots of selected 5 hits are provided. RMSD-time graphs show that, depending on the initial design, structures of all systems studied have smaller structural changes (¡ 3.0 Å). During simulations based on the graphs shown in Figure 2, the molecule 25 clearly shows the highest increase in RMSD. The average RMSD value is about 2 Åand structurally stable after 20 ns in all MD simulations of complex systems. Table 1 shows the results of molecular docking of ligands. Ligand RMSDs are also examined along with the protein RMSD plot. Figure 3 shows the Lig Fit Prot (i.e., ligand RMSD changes based on protein backbone, thus a translational motion of the ligand throughout the MD simulations) graph of 5 hit molecules and FDA approved drug Amprenavir. Ligand RMSD indicates how stable the ligand is with regard to the protein and the binding pocket. A ligand RMSD of LigFitProt is seen when the protein-ligand complex is first paired with the reference protein backbone and then the ligand heavy atoms RMSD is calculated. If the calculated values are considerably greater than the RMSD of the protein, the ligand would possibly have diffused away from its initial binding location.
The rotational motions of the ligands are also examined using the LigFitLig (i.e., ligand’s RMSD values based on its heavy atoms, thus a rotational motion of the ligand throughout the MD simulations) RMSDs in Figure 4. The average LigFitLig RMSDs of all of the studied molecules (except Mol 315) are less than 2.0 Å, indicating that the molecules did not make a serious rotational change.
In the HIV protease the active site is not completely visible, keeping two filexible β hairpin flaps protected. The flaps have to be opened so that the substrates enable entry to the active site Figure 5.
Many existing HIV-protease inhibitors have been developed to imitate the transition state of the substrate. The inhibitor hydroxyl group associates with the protease-active site residues carboxyl group Asp25 and Asp125 via hydrogen bonds. The HIV protease inhibitor-contacting residues, including Gly27, Asp25 Asp29, Asp30 and Gly48, are fairly conserved (Lv, Chu, & Wang, 2015; Shen, Wang, Kovalevsky, Harrison, & Weber, 2010b).
It must be noted that although we did not use any constraints in the numerical analyses which we perform for the identification of compounds against HIV-1 PR, interestingly we found that selected 3 hits (Compounds 290, 287 and 338) are analogs of each other. Figures S1, S2, S3 and S4 show interactions diagrams of compounds 287 and 338.

4 Conclusions

Providing general principles for inhibitor design is challenging. The structural properties of the compounds are not the only considerations; the ease of chemical synthesis, small molecular weight, bioavailability and stability are also important. In-silico studies involving ligand-based approaches are applied to identify therapeutic agents that act as powerful inhibitors of specific targets. Structure-based methods such as docking and molecular dynamics simulation have been carried out to elucidate the binding mechanisms, which are basically the main role of hydrophobic interactions in ligand binding and the versatility of the active site to respond to different ligands. In this study we carried out advanced text mining, virtual screening and hybrid molecular modeling strategies to identify the structural properties of indole based HIV-1 PR inhibitors and binding mechanism. To this end, Specs-SC database consisting of 212520 molecules is first screened and 5194 compounds including indole are obtained and then obtained molecules are filtered to remove toxic compounds using MetaCore/MetaDrug from Clarivate Analytics. MetaCore database is ideal for databases of manually curated interactions over 90% of human proteins with known function. Docking simulations are carried out thanks to the availability of many crystal structures of HIV-1 PR– ligand complexes. By using molecular docking simulations, the obtained 476 compounds with high HIV therapeutic activity (¿ 0.5) and without any toxicity are investigated against the HIV -1 PR and filtered molecular top-docking poses are then used in long (100 ns) MD simulations. Docking scores and interactions diagrams throughout 100 ns MD simulations of the selected 5 hit compounds (with high therapeutic activity and no toxicity) in HIV-1 PR show that these compounds may inhibit HIV-1 PR target. Results showed that the following residues are crucial for ligand (mol-290) binding: ASP30, ILE150, GLY148 and GLY151. Several water bridges and hydrogen bonding interactions dominate the interactions. The crucial interactions such as constructed hydrogen bonds with ASP30 and ILE150 which are also observed in Amprenavir are also conserved in the identified hit molecule 290. The identified novel HIV PR inhibitor scaffolds may be a potential alternative for the removal of medication side effects. The leading new scaffolds could become the next generation of HIV-1 PR inhibitors with different chemical structures and alternate binding patterns to HIV-1 PR after comprehensive modifications and tests.

References

Agrawal, L., Lu, X., Jin, Q., & Alkhatib, G. (2006). Anti-HIV therapy: Current and future directions. Curr. Pharm. Des., 12(16), 2031–2055.
Bandini, M. (2013, Aug). Electrophilicity: the ”dark-side” of indole chemistry. Org. Biomol. Chem., 11(32), 5206–5212.
Banks, J. L., Beard, H. S., Cao, Y., Cho, A. E., Damm, W., Farid, R., et al. (2005, Dec). Integrated Modeling Program, Applied Chemical Theory (IMPACT). J Comput Chem, 26(16), 1752–1780.
Bathula, R., Lanka, G., Muddagoni, N., Dasari, M., Nakkala, S., Bhargavi, M., et al. (2020, May). Identification of potential Aurora kinase-C protein inhibitors: an amalgamation of energy minimization, virtual screening, prime MMGBSA and AutoDock. J. Biomol. Struct. Dyn., 38(8), 2314–2325.
Berendsen, H. J. C., Postma, J. P. M., Gunsteren, W. F. van, & Hermans, J. (1981). Interaction models for water in relation to protein hydration. In Intermolecular Forces: Proceedings of the Fourteenth Jerusalem Symposium on Quantum Chemistry and Biochemistry Held in Jerusalem, Israel, April 13–16, 1981 (pp. 331–342). Dordrecht: Springer Netherlands. Retrieved from https://doi.org/10.1007/978-94-015-7658-1_21
Bourinbaiar, A. S. (2009). New developments in drug and vaccine discoveries. Curr. Pharm. Des., 15(11), 1157–1158.
Bozzette, S. A., Ake, C. F., Tam, H. K., Chang, S. W., & Louis, T. A. (2003, Feb). Cardiovascular and cerebrovascular events in patients treated for human immunodeficiency virus infection. N. Engl. J. Med., 348(8), 702–710.
Campiani, G., Ramunno, A., Maga, G., Nacci, V., Fattorusso, C., Catalanotti, B., et al. (2002). Non-nucleoside HIV-1 reverse transcriptase (RT) inhibitors: past, present, and future perspectives. Curr. Pharm. Des., 8(8), 615–657.
Condra, J. H., Schleif, W. A., Blahy, O. M., Gabryelski, L. J., Graham, D. J., Quintero, J. C., et al. (1995, Apr). In vivo emergence of HIV-1 variants resistant to multiple protease inhibitors. Nature, 374(6522), 569–571.
Cutinho, P. F., Roy, J., Anand, A., Cheluvaraj, R., Murahari, M., & Chimatapu, H. S. V. (2020, Apr). Design of metronidazole derivatives and flavonoids as potential non-nucleoside reverse transcriptase inhibitors using combined ligand- and structure-based approaches. J. Biomol. Struct. Dyn., 38(6), 1626– 1648.
Deng, J., Dayam, R., Al-Mawsawi, L. Q., & Neamati, N. (2007). Design of second generation HIV-1 integrase inhibitors. Curr. Pharm. Des., 13(2), 129– 141.
Desmond, version 4.9, schrodinger, llc, new york. (2011). for HIV/AIDS, N. C. for. (2019). Estimated hiv incidence and prevalence in the united states, 2010–2016 (Vol. 24, Tech. Rep.). Center for Desease Control and Prevetion.
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., et al. (2004, Mar). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 47(7), 1739–1749.
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., et al. (2006, Oct). Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 49(21), 6177–6196.
Gottlinger, H. G., Sodroski, J. G., & Haseltine, W. A. (1989, Aug). Role of capsid precursor processing and myristoylation in morphogenesis and infectivity of human immunodeficiency virus type 1. Proc. Natl. Acad. Sci. U.S.A., 86(15), 5781–5785.
Hoover, W. G. (1985, Mar). Canonical dynamics: Equilibrium phase-space distributions. Phys Rev A Gen Phys, 31(3), 1695–1697.
Hou, T., Wang, J., Li, Y., & Wang, W. (2011, Jan). Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model, 51(1), 69–82.
Hruz, P. W. (2008, Nov). HIV protease inhibitors and insulin resistance: lessons from in-vitro, rodent and healthy human volunteer models. Curr Opin HIV AIDS, 3(6), 660–665.
Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J., Honig, B., Shaw, D. E., et al. (2004a, May). A hierarchical approach to all-atom protein loop prediction. Proteins, 55(2), 351–367.
Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J., Honig, B., Shaw, D. E., et al. (2004b, May). A hierarchical approach to all-atom protein loop prediction. Proteins, 55(2), 351–367.
Kakarala, K. K., Jamil, K., & Devaraji, V. (2014, Sep). Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) – a promising target for breast cancer. J. Mol. Graph. Model., 53, 179–199.
Klumpp, K., & Mirzadegan, T. (2006). Recent progress in the design of small molecule inhibitors of HIV RNase H. Curr. Pharm. Des., 12(15), 1909–1922.
Kotler, D. P. (2008, Sep). HIV and antiretroviral therapy: lipid abnormalities and associated cardiovascular risk in HIV-infected patients. J. Acquir. Immune Defic. Syndr., 49 Suppl 2, 79–85.
Krallinger, M., & Valencia, A. (2005). Text-mining and information-retrieval services for molecular biology. Genome Biol., 6(7), 224. ([PubMed Central:PMC1175978] [DOI:10.1186/gb-2005-6-7-224] [PubMed:12501816])
Kumar, A., Rathi, E., & Kini, S. G. (2020, Apr). Identification of potential tumour-associated carbonic anhydrase isozyme IX inhibitors: atom-based 3DQSAR modelling, pharmacophore-based virtual screening and molecular docking studies. J. Biomol. Struct. Dyn., 38(7), 2156–2170.
Li, J., Abel, R., Zhu, K., Cao, Y., Zhao, S., & Friesner, R. A. (2011, Oct). The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins, 79(10), 2794–2812.
Louis, J. M., Ishima, R., Torchia, D. A., & Weber, I. T. (2007). HIV-1 protease: structure, dynamics, and inhibition. Adv. Pharmacol., 55, 261–298.
Lv, Z., Chu, Y., & Wang, Y. (2015). HIV protease inhibitors: a review of molecular selectivity and toxicity. HIV AIDS (Auckl), 7, 95–104.
Ma, Z., & Tuckerman, M. (2010, Nov). Constant pressure ab initio molecular dynamics with discrete variable representation basis sets. J Chem Phys, 133(18), 184110. MarvinSketch 18.30.0, chemaxon. (2018).
Matthews, B. W. (1975, Oct). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 405(2), 442–451.
Menendez-Arias, L., Matamoros, T., & Cases-Gonzalez, C. E. (2006). Insertions and deletions in HIV-1 reverse transcriptase: consequences for drug resistance and viral fitness. Curr. Pharm. Des., 12(15), 1811–1825.
Panda, S. K., Saxena, S., & Guruprasad, L. (2020, Apr). Homology modeling, docking and structure-based virtual screening for new inhibitor identification of
Klebsiella pneumoniae heptosyltransferase-III. J. Biomol. Struct. Dyn., 38(7), 1887–1902.
Protein preparation, version 2.5, schrodinger, llc, new york. (2011).
Raha, K., & Merz, K. M. (2004, Feb). A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding. J. Am. Chem. Soc., 126(4), 1020–1021.
Rather, M. A., Dutta, S., Guttula, P. K., Dhandare, B. C., Yusufzai, S. I., & Zafar, M. I. (2020, May). Structural analysis, molecular docking and molecular dynamics simulations of G-protein-coupled receptor (kisspeptin) in fish. J. Biomol. Struct. Dyn., 38(8), 2422–2439.
Roy, S., Kumar, A., Baig, M. H., Masajik, M., & Provaznik, I. (2015, Jul). Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer’s disease. Methods, 83, 105– 110.
Sahin, K., B., Z. K., F., S., & S., D. (2019). Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays. Journal of Biomolecular Structure and Dynamics, https://doi.org/10.1080/07391102.2019.1660218.
Sahin, K., & S., D. (2020). Identifying New Piperazine-based PARP1 Inhibitors Using Text Mining and Integrated Molecular Modeling Approaches. Journal of Biomolecular Structure and Dynamics, https://doi.org/10.1080/07391102.2020.1715262.
Saikia, S., & Bordoloi, M. (2019). Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets, 20(5), 501– 521.
Sastry, G. M., Dixon, S. L., & Sherman, W. (2011, Oct). Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J Chem Inf Model, 51(10), 2455– 2466.
Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007, Dec). Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des., 21(12), 681–691.
Shen, C. H., Wang, Y. F., Kovalevsky, A. Y., Harrison, R. W., & Weber, I. T. (2010a, Sep). Amprenavir complexes with HIV-1 protease and its drugresistant mutants altering hydrophobic clusters. FEBS J., 277(18), 3699– 3714.
Shen, C. H., Wang, Y. F., Kovalevsky, A. Y., Harrison, R. W., & Weber, I. T. (2010b, Sep). Amprenavir complexes with HIV-1 protease and its drugresistant mutants altering hydrophobic clusters. FEBS J., 277(18), 3699– 3714.
Sledz, P., & Caflisch, A. (2018, 02). Protein structure-based drug design: from docking to molecular dynamics. Curr. Opin. Struct. Biol., 48, 93–102.
Soontornniyomkij, V., Umlauf, A., Chung, S. A., Cochran, M. L., Soontornniyomkij, B., Gouaux, B., et al. (2014, Jun). HIV protease inhibitor exposure predicts cerebral small vessel disease. AIDS, 28(9), 1297–1306.
Subhani, S., Jayaraman, A., & Jamil, K. (2015, Apr). Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer. Biomed. Pharmacother., 71, 37–45.
Taber, D. F., & Tirunahari, P. K. (2011, Sep). Indole synthesis: a review and proposed classification. Tetrahedron, 67(38), 7195–7210.
Todeschini, R., Ballabio, D., & Grisoni, F. (2016, 10). Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models. J Chem Inf Model, 56(10), 1905–1913.
Tozser, J. (2001, Nov). HIV inhibitors: problems and reality. Ann. N. Y. Acad. Sci., 946, 145–159.
Tripathi, S. K., Muttineni, R., & Singh, S. K. (2013, Oct). Extra precision docking, free energy calculation and molecular dynamics simulation studies of CDK2 inhibitors. J. Theor. Biol., 334, 87–100.
Walker, B. D., & Burton, D. R. (2008, May). Toward an AIDS vaccine. Science, 320(5877), 760–764.
Wlodawer, A., & Erickson, J. W. (1993). Structure-based inhibitors of HIV-1 protease. Annu. Rev. Biochem., 62, 543–585.
Yeung, K. S., Qiu, Z., Yin, Z., Trehan, A., Fang, H., Pearce, B., et al. (2013, Jan). Inhibitors of HIV-1 attachment. Part 8: the effect of C7-heteroaryl substitution on the potency, and in vitro and in vivo profiles of indole-based inhibitors. Bioorg. Med. Chem. Lett., 23(1), 203–208.
Zhang, M. Z., Chen, Q., & Yang, G. F. (2015, Jan). A review on recent developments of indole-containing antiviral agents. Eur J Med Chem, 89, 421–441.
Zhao, F., Wang, J. L., Ming, H. Y., Zhang, Y. N., Dun, Y. Q., Zhang, J. H., et al. (2020, Apr). Insights into the binding mode and functional components of the analgesic-antitumour peptide from Buthus martensii Karsch to human voltage-gated sodium channel 1.7 based on dynamic simulation analysis. J. Biomol. Struct. Dyn., 38(6), 1868–1879.