Modulation of interaction of BRCA1-RAD51 and BRCA1-AURKA protein complexes by natural metabolites using as possible therapeutic intervention toward cardiotoxic effects of cancer drugs: an in-silico approach

Abstract Breast cancer type 1 susceptibility protein (BRCA1) plays an important role in maintaining genome stability and is known to interact with several proteins involved in cellular pathways, gene transcription regulation and DNA damage response. More than 40% of inherited breast cancer cases are due to BRCA1 mutation. It is also a prognostic marker in non-small cell lung cancer patients as well as a gatekeeper of cardiac function. Interaction of mutant BRCA1 with other proteins is known to disrupt the tumor suppression mechanism. Two directly interacting proteins with BRCA1 namely, DNA repair protein RAD51 (RAD51) and Aurora kinase A (AURKA), known to regulate homologous recombination (HR) and G/M cell cycle transition, respectively, form protein complex with both wild and mutant BRCA1. To analyze the interactions, protein–protein complexes were generated for each pair of proteins. In order to combat the cardiotoxic effects of cancer drugs, pharmacokinetically screened natural metabolites derived from plant, marine and bacterial sources and along with FDA-approved cancer drugs as control, were subjected to molecular docking. Piperoleine B and dihydrocircumin were the best docked natural metabolites in both RAD51 and AURKA complexes, respectively. Molecular dynamics simulation (MDS) analysis and binding free energy calculations for the best docked natural metabolite and drug for both the mutant BRCA1 complexes suggested better stability for the natural metabolites piperolein B and dihydrocurcumin as compared to drug. Thus, both natural metabolites could be further analyzed for their role against the cardiotoxic effects of cancer drugs through wet lab experiments. Communicated by Ramaswamy H. Sarma


Introduction
The BRCA1 gene encodes the breast cancer type 1 susceptibility protein (BRCA1). It is an 1863 amino acid protein consisting of significant motifs, a RING-finger domain near the N-terminus and a BRCT domain at the C-terminus. BRCA1 plays a role in transcription, DNA repair of double-stranded breaks, and recombination having an overall role in preserving genome stability and cell cycle progression. Mutations in the BRCA1 gene account for about 40% of inherited breast cancer cases and 80% of families susceptible to both breast and ovarian cancer (Zhang et al., 1998;Zhou et al., 2019). It has been reported that mutation in BRCA1 results in defects in DNA repair and transcription, abnormal centrosome duplication, defective G2/M cell cycle checkpoint regulation, impaired spindle checkpoint and chromosome damage (Petrucelli et al., 2010). Along with breast cancer, BRCA1 is also suggested as a prognostic marker in patients with non-small-cell lung cancer (NSCLC) (Hu et al., 2019;Reguart et al., 2008). A cardioprotective role of wild type BRCA1 has also been reported by Shukla et al. (2011) where it has been shown to protect cardiomyocytes from DNA damage, apoptosis and endothelial dysfunction. Furthermore, BRCA1 has also been suggested as a gatekeeper for cardiac function (van Westerop et al., 2016). A mutant form of BRCA1 has been reported to cause susceptibility to cardiac damage (Singh et al., 2013).
With regards to protein-protein interaction, a number of proteins have been reported to interact with BRCA1 and such interactions have been suggested to safeguard the integrity of the genome and other cellular responses (Christou & Kyriacou, 2013). Therefore, analysis of the protein-protein interaction with mutant BRCA1 and its participating protein partners will be helpful in getting a deeper insight into its role in disease progression. In this context, one of the directly interacting protein with BRCA1 namely, RAD51 which plays a major role in homologous recombination (HR) of DNA during double-strand break (DSB) repair (Baumann & West, 1997;Martin et al., 2007) is worth investigating. Formation of a complex between BRCA1 and RAD51 upon DNA damage, directs their mobilization at sites of DNA damage where BRCA1 promote RAD51 nucleoprotein filament formation and stabilize RAD51 for the HR process (Christou & Kyriacou, 2013;Scully et al., 1997). The C-terminal region of BRCA1 has been reported to participate in interaction with RAD51 (Christou & Kyriacou, 2013;Leung & Glover, 2011;Rodriguez & Songyang, 2008).
Another directly interacting protein with BRCA1 is AURKA, an oncoprotein that is overexpressed in 62% of breast cancers (Miyoshi et al., 2001). The interaction of BRCA1 with AURKA and phosphorylation of BRCA1 have been demonstrated to regulate G2/M cell cycle transition (Hirota et al., 2003;Marumoto et al., 2002;Ouchi et al., 2004). Thus, both the proteins, BRCA1 and AURKA, have been suggested as part of the same regulatory pathway that is responsible for the control of centrosome function and play an important role in tumor suppression (Sankaran et al., 2007). AURKA binds to C-terminal domain of BRCA1 and phosphorylates it. This BRCA1 phosphorylation plays a role in G2/M transition (Ouchi et al., 2004). In mutant condition, BRCA1 is dephosphorylated by AURKA leading to overexpression of AURKA in cancers carrying BRCA1 mutation (Anand et al., 2003;Couch et al., 2007).
Understanding the interaction of target network proteins and their modulation is of utmost significance (Fink et al., 2009). However, determining the 3D structure of a protein-protein complex experimentally, using classical methods, like X-ray and NMR crystallography, in contrast to that of a single protein structure, is a relatively difficult task to perform due to the high cost and technical difficulties (Brender & Zhang, 2015). As the number of biomolecular complexes increase it will eventually become more difficult to study such complexes using such methods. Due to availability of a number of computational methods this task becomes easily feasible. Furthermore, in case where the crystal structure of a protein is not available, it is possible to model a protein using computational homology modeling methods (Russell et al., 2004). The protein-protein docking provides an effective approach to study and analyze the structural basis of a complex (Kastritis et al., 2011). Thus, protein-protein docking comprises prediction of structure of complex given the structure of individual proteins. Its core lies in the physicochemical complementarity at the interface (Vakser, 2014). There is a wide variety of docking programs available for protein-protein docking comprising different sampling algorithms, scoring functions and computational efficiencies. Fast Fourier transform (FFT), spherical Fourier transform (SFT) based correlation algorithms, randomized search are few such examples (Huang, 2015(Huang, , 2014Ritchie, 2008).
A number of drugs used for chemotherapy, such as doxorubicin, epirubicin, valrubicin, etc., belonging to category of synthetic drugs, have been known to cause adverse side-effects including those of cardiotoxicity leading to cardiovascular diseases (CVD) in cancer patients. Thus, synthetic drugs like anthracyclines, beta-adrenoceptor blocking agents, immunomodulators, calcium channel blocking agents, anti-depressants, non-steroidal anti-inflammatory molecules (NSAIDs), anesthetics, anti-arrhythmics are reported to induce cardiotoxicity (Giordano et al., 2016). In view of these drawbacks of synthetic drugs, use of natural products (natural metabolites) as therapeutic molecules offer great potential. Natural products isolated from plant, marine and microorganisms serve as lead compounds against various diseases (Gordaliza, 2007;Koneru et al., 2017).
Thus, from the point of view of exploitation for therapeutic application of these protein-protein interactions, it is pertinent to look for small molecules which could modulate these interactions in a beneficial way. Thus, in this article, pharmacokinetically screened natural metabolites along with FDA-approved cancer drugs (as known inhibitors), have been analyzed for inhibition of the interaction of wild type and mutant BRCA1-RAD51 and BRCA1-AURKA complexes using molecular docking and dynamics simulation analyses.

Materials and methods
Protein-protein complex generation using protein-protein docking approach In order to investigate the interaction between the component proteins of a target protein complex, several docking algorithms are used. In this study, 3D structure of target proteins namely wild type BRCA1 (PDB ID: 1Y98) and mutant BRCA1 (PDB ID: 2ING) and ligand proteins namely, RAD51 (PDB ID: 1N0W) and AURKA (PDB ID: 1MQ4), were retrieved from RCSB protein databank database, for the generation of protein complexes through docking using ZDOCK module of Discovery Studio (DS) version 4.5 (BIOVIA Discovery Studio, San Diego, California). ZDOCK is a fast, rigid-body, initial stage protein-protein docking algorithm that applies ligand rotation and a pair-wise shape complementarity method that takes advantage of FFT. The FFT algorithm (Chen et al., 2003) based docking program can generate thousands of docking poses in a fraction of time. The clusters were selected based on ZDock and ZRank score. ZDock score is the shape complementarity score which includes electrostatic and desolvation energy terms. The predictions from ZDOCK are evaluated by calculating the root mean square deviation (RMSD) between the bound and unbound interface alpha carbon (Ca) atoms. Z-rank score represents energy of docked pose. It utilizes a scoring function that is a linear weighted sum of van der Waals attractive and repulsive energies, electrostatics short and long-range attractive and repulsive energies and desolvation. Thus, the advantage of ZDOCK is that it lets one rapidly dock and refine the component proteins to form an assembly without prior knowledge of the interface residues (Chen et al., 2003;Pierce et al., 2014). The clusters obtained from the above were further refined using the RDOCK method in ZDOCK module. RDOCK optimizes the ZDOCK complexes by running CHARMm optimization. RDOCK output includes the refined receptor and ligand complexes ranked according to their Zdock and Z-rank score and best complex was selected based on the score.

Molecular dynamics simulation analyses of protein-protein complex
In order to check the overall stability, both wild type and mutant BRCA1-RAD51 and BRCA1-AURKA complexes were analyzed through 50 ns of MD simulation (MDS) using GROMACS version 4.5.5 package (University of Groningen, Netherlands) with GROMOS96 43a1 force field (Sterling & Irwin, 2015).

Pharmacokinetic screening of natural metabolites
In this study, pharmacokinetic analyses which consist of ADME, toxicity and drug likeness prediction of 2082 natural metabolites, obtained from ZINC database, were done using DS 4.5 small molecules module. ZINC is a free database of commercially available compounds for virtual screening. It is used for ligand discovery that connects biological activities by gene product, drugs and natural products with commercial availability (Lipinski et al., 1997). Toxicity predictions of selected natural metabolites were done using weight of evidence (WOE) parameter of carcinogenicity of TOPKAT module of DS 4.5. WOE predictions are in accordance with animal model guidelines of U.S. Food and Drug Administration (FDA) and National Toxicology Program (NTP), USA. The analysis cardiotoxic nature of metabolites on the basis of hERG potassium channel blockage was done by admetSAR online server (Zhang et al., 2016). Drug-like properties of selected natural metabolites were analyzed, on the basis of Lipinski's rule of five, using DS version 4.5 by assessing four properties, namely H-Donor, H-Acceptor, molecular weight and AlogP (partition coefficient) (Alam & Khan, 2018).
Docking of pharmacokinetically screened natural metabolites along with FDA approved cancer drugs with wild type and mutant BRCA1-RAD51 and BRCA1-AURKA complexes Pharmacokinetically screened natural metabolites along with FDA-approved cancer drugs retrieved from National Cancer Institute (NCI) repository, were subjected to docking at the binding site of interacting complexes namely, wild type and mutant BRCA1-RAD51 and BRCA1-AURKA using Libdock module of DS 4.5. LibDock uses protein site features, consisting of hot-spots and two types states (polar and apolar). The ligand poses are placed into the polar and apolar receptor interactions site. A polar hotspot is preferred by a polar ligand atom (e.g. a hydrogen bond donor or acceptor), and an apolar hotspot is preferred by an apolar atom (e.g. a carbon atom) (Kumari et al., 2014). For identifying binding site residues at protein-protein interaction zone of both BRCA1-RAD51 and BRCA1-AURKA complexes, define and edit binding site module of DS 4.5 was used which derives binding sites from cavities in the structure of the receptor as well as by using PDB SITE records.
Regiospecificity and ligand interaction analysis of mutant BRCA1-RAD51 and BRCA1-AURKA complexes docked with natural metabolites and drug The complexes of mutant BRCA1-RAD51 and BRCA1-AURKA with their best docked natural metabolites and cancer drugs were subjected to regiospecific interaction analysis within 6 Å region of binding interface followed by ligand interaction analysis which included hydrogen bond interaction as well as non-covalent hydrophobic interactions in order to get a better understanding of stability of ligand at the binding site, using DS 4.5.
Molecular dynamics simulation analysis and binding free energy calculation of mutant BRCA1-RAD51 and BRCA1-AURKA complexes docked with natural metabolites and drug Interactions of best docked natural metabolite and drug with both mutant BRCA1-RAD51 and mutant BRCA1-AURKA complexes were investigated through 100 ns of MDS using GROMACS version 4.5.5 package with GROMOS96 43a1 force field (Sterling & Irwin, 2015). The docking poses of the complexes with the ligand were prepared for MDS through mild minimization and solvation within a water-filled three-dimensional cube (box) of 1 Å spacing. System was neutralized and further minimized. The complex structure was heated to 300 K and equilibrated for 100 ps in NVT ensemble and another 100 ps in NPT ensemble. After heating and equilibration, the complex structure with its ligands was subjected to production run of 100 ns in NPT ensemble. PRODRG web server was used to generate topologies and coordinates of ligand (Sch€ uttelkopf & van Aalten, 2004). Default values of GROMACS were assigned for determination of hydrogen-bonding interactions between protein-protein complex and ligand. The binding free energies of the complexes docked with natural metabolites along with drugs, during the equilibrium phase of the MDS of each trajectory of MDS, were computed using g_mmpbsa tool of GROMACS (Sterling & Irwin, 2015) using the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method (Kollman et al., 2000).

Results
Protein-protein complex generation for BRCA1 and TP53 through docking with their respective interacting proteins In order to generate 3D structure of protein complexes, the structure of proteins BRCA1 wild (1Y98) and mutant (2ING) were retrieved from RCSB protein data bank. The mutant BRCA1 (2ING) structure consisted of four mutations namely, M1775K, T1700A, W1837R and S1841N. Along with these the 3D structures of directly interacting proteins RAD51 (1N0W) & AURKA (1MQ4) and RPA1 (2B29), respectively, were also retrieved from RCSB protein data bank. The retrieved protein structures were subjected to preprocessing in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, etc. (Madhavi Sastry et al., 2013). The protein-protein complex generation for both BRCA1 (wild and mutant) with respective interacting proteins RAD51 and AURKA was done using Z-dock module of DS 4.5. RAD51 and AURKA were docked with both mutant and wild type BRCA1. In this process, out of 2000 clusters generated by Z-dock, for each complex, the best cluster based on highest Z-dock score and Z-rank score was selected. The selected cluster was further subjected to refinement using R-Dock module of DS 4.5 for obtaining the final Z-dock and Z-rank scores for each generated protein-protein complex. The final Z-dock and Z-rank scores along with 3D structures of each complex are depicted in Table 1 and Figure 1(a,b), respectively. Thus, based on the Z-dock and Z-rank scores, the mutant complexes exhibited higher Z-dock and Z-rank scores as compared to that of wild type complex in each case. Among the two complexes of BRCA1, BRCA1-RAD51 complex had better scores as compared to that of BRCA1-AURKA.
3D structures of the wild type and mutant complexes, of BRCA1-RAD51 revealed a more compact structure for the mutant as compared to that of the wild type (Figure 1(a)) concomitant with a significant change in the conformation of both the proteins. However, in case of the of the wild type and mutant complexes, of BRCA1-AURKA, both the complexes exhibited almost similar compactness (Figure 1(b)) concomitant with a significant change in the conformation of both the proteins. It was also noteworthy that BRCA1 formed complex with both RAD51 and AURKA via C-terminus region in both mutant and wild type.  Molecular dynamics simulation analysis of wild and mutant BRCA1 and their complexes with RAD51 and AURKA Molecular dynamics simulation (MDS) analyses of wild and mutant BRCA1 and their complexes with RAD51 and AURKA were performed. The results in terms of RMSD for complex of wild and mutant BRCA1 with RAD51 and AURKA, respectively, are presented in Figure 2. It is evident that all the four complexes reached the equilibrium state within 50 ns of MDS analyses.
The wild type BRCA1-RAD51 complex exhibited an average RMSD of 1.05 ± 0.02 whereas the mutant complex exhibited an average RMSD of 0.77 ± 0.03. Similarly, in case of BRCA1-AURKA, the wild type complex exhibited an average RMSD of 1.20 ± 0.06 whereas the mutant complex exhibited an average RMSD of 0.81 ± 0.06. It is evident that in both BRCA1-RAD51 and BRCA1-AURKA, the RMSD of mutant complex was better than the wild complex, thus suggesting the better compactness and stability for the mutant complex as compared to the wild one.

Pharmacokinetic screening of natural metabolites
Out of 2082 natural metabolites subjected to ADMET analyses, only 317 passed the ADMET criteria, the area encompassed by the ellipse (Figure 3).   (ALogP98) showing the 95% and 99% confidence limit ellipses using ADMET. (Red and green ellipses depict 95% and 99% HIA, respectively; pink and sky-blue ellipses depict 95% and 99% BBB (cell membrane permeability), respectively; dark blue dots depict metabolites).
Only 317 metabolites fulfill the set criteria at both 95 and 99% confidence limit ellipses. The human ether-a-go-gorelated gene (hERG) inhibition analysis for identifying cardiotoxic nature of compounds revealed that out of 317, 41 metabolites showed positive hERG inhibition (suggesting possible cardiotoxic nature), whereas remaining 276 showed negative hERG inhibition (suggesting possible no-cardiotoxic nature). The detail of the ADME and toxicity analysis has been presented in detail in Table 2.
The drugs like properties of the 317 screened metabolites were analyzed based on Lipinski's Rule of Five. It was found that out of 317 metabolites, only 182 successfully fulfilled   Lipinski's rule of five criteria. Among these 182 potential therapeutic molecules, 176 were found to be from plant, 3 from marine and 2 from microbial sources. Thus, plant metabolites were found to have a major share (Supplementary Table 1). Among the 182 screened metabolites, a major share belonged to plant metabolites which have been reported for their therapeutic roles such as antioxidant, anti-inflammatory, immunomodulatory, anti-asthmatic, anti-convulsant, anti-mutagenic, antimycobacterial, anti-amoebic and anticancer agent. All the 182 natural metabolites successfully fulfilling ADMET and drug likeness criteria, were further subjected to screening using molecular docking.
Docking of pharmacokinetically screened natural metabolites along with FDA approved cancer drugs with wild type and mutant BRCA1-RAD51 and BRCA1-AURKA complexes Both wild type and mutant BRCA1-RAD51 and BRCA1-AURKA complexes were subjected to molecular docking with 182 pharmacokinetically screened natural metabolites along with 147 FDA approved cancer drugs. The results of the docking are presented with respect to those pertaining to mutant complexes. For comparison, the data for wild type complexes are also included. The results of the docking of top 10 natural metabolites and a set of well-known inhibitors (drugs), based on their Libdock score, for mutant BRCA1-RAD51 and BRCA1-AURKA complexes along with that of wild type (in parenthesis) are presented in Table  3a,b , respectively.
From Table 3a,b, in mutant BRCA1-RAD51 complex, piperolein B had the highest Libdock score while in case of mutant BRCA1-AURKA complex dihydrocurcumin had the highest Libdock score. In mutant BRCA1-RAD51 complex, valrubicin was the best docked drug while in case of mutant BRCA1-AURKA complex, it was mitoxantrone. The overall analysis of Libdock scores of wild type and mutant complexes of BRCA1-RAD51 and BRCA1-AURKA, reflected better Libdock scores for mutant complex as compared to wild type.

Regiospecificity and ligand interaction analysis of mutant BRCA1-RAD51 and BRCA1-AURKA complexes docked with natural metabolites and drug
The docked complexes of mutant BRCA1-RAD51 and BRCA1-AURKA with their best docked natural metabolites (piperolein B and dihydrocurcumin, respectively) and cancer drugs (valrubicin and mitoxantrane, respectively) were subjected to analysis for regiospecificity and various non-covalent interactions. The regiospecific interacting residues within the 6 Å region of binding site of mutant BRCA1-RAD51 and BRCA1-AURKA complexes and various types of non-covalent interactions of ligand with the binding site residues were also analyzed which have been depicted in Table 4.
From the data presented in Table 3, it is noteworthy that both the natural metabolites piperolein B formed one conventional hydrogen bond having 2.48 Å distance and dihydrocurcumin formed three conventional hydrogen bonds having 1.88, 2.50 and 2.98 Å distance with the residues. Whereas among the drugs only mitoxantrane formed four conventional hydrogen bonds having 1.82, 2.09, 2.12 and 2.61 Å distance with the residues, while no conventional hydrogen bonds were observed for valrubicn. Other than this several non-covalent and hydrophobic interactions like carbon-hydrogen bonds, pi-alkyl, pi-anion, halogen and pi-pi T-shaped interactions were also observed in all the ligands. As the stability of the ligand is attributed to these interactions, in the above table all four ligands formed such interactions with pi-alkyl, pi-anion and pi-pi interactions having a major share. These interactions are known for intercalating the ligand at the receptor-binding site and are therefore crucial for interactions. Here among the four ligands, both natural metabolites, piperloein B and dihydrocurcumin form three pi-alkyl and pi-anion interactions whereas valrubicin and mitoxantrone have these 4 and 5 in number.     Molecular dynamics simulation analysis and binding free energy calculation of mutant BRCA1 complex docked with natural metabolites and drug MDS analyses of both BRCA1-RAD51 and BRCA1-AURKA complexes were done with best docked ligands for 100 ns in order to check the overall stability of the complexes. The results were analyzed in terms of root mean square deviation (Figure 4(a,c)), root mean square fluctuation (Figure 4(a,c)) and hydrogen bond interaction (Figure 4(b,d)).
The details of the RMSD and H-bonds per time frame for both the complexes have been presented in Table 5.
In case of BRCA1-RAD51, from the RMSF plot (Figure 4(a)), it is evident that, barring a few residues, RMSF of BRCA1-RAD51 complex with both piperolein B and valrubicin were almost similar. In BRCA1-AURKA complex the RMSF plot (Figure 4(c)) of both the complexes i.e. with dihydrocurcumin and mitoxantrone, was found to be almost similar.
To validate the results of the MDS analyses and stability of the complexes, the binding free energies of both BRCA1- For cardiotoxic analysis against hERG only, the admetSAR server was used. a ALogP 98 score of < 5 and PSA score of < 140 indicates good absorption and cell permeability; HIA level of 0 indicates good absorption; BBB levels of 0-3 indicate high, medium and low penetrants, respectively; PPB score of À2.226 or less reflect highly bound (!90%) to plasma protein; CYP2D6 score of < 0.162 indicate non inhibitor of CYP2D6; solubility levels of 2 and 3 indicates low and good, respectively; NC depicts non carcinogenic; Àve depicts negative herG inhibition; þve depicts positive herG inhibition. RAD51 and BRCA1-AURKA complexes docked with their respective ligands were analyzed. The details are presented in Table 6. As the binding free energy represents protein ligand-binding affinities, lower the binding energy, greater the affinity. Here in both BRCA1-RAD51 and BRCA1-AURKA mutant complexes, both natural metabolites possessed lower binding energies compared to cancer drug thereby suggesting significant stability of complexes with natural metabolites as compared to those with respective drugs. Therefore, for negative modulation (i.e. inhibition) of both BRCA1-RAD51 and BRCA1-AURKA mutant complexes, natural metabolites piperolein B and dihydrocurcumin, respectively could be effective therapeutic molecules.

Discussion
The present article focuses on exploiting natural metabolites as therapeutic agents through modulation of protein-protein interaction in a positive way. The vast diversity of natural metabolites isolated from plants, marine flora and microorganisms has been known to serve as lead compounds for improvement of their therapeutic potential against a variety of diseases including cancer (Gordaliza, 2007). Thus, more than 60% of cancer drugs are derived from natural products (Newman & Cragg, 2007). Use of natural metabolites, derived from plants, in treating critical diseases is known from ancient times (Dias et al., 2012). Furthermore, a number of clinical studies revealed the beneficial effects of natural metabolites against cardiotoxicity (Koneru et al., 2017). Therefore, natural products could also be a promising alternative for prevention against cardiotoxic effects of drugs. Cardiotoxicity appears at early or late stage of cancer therapy leading to myocardial dysfunction or irreversible heart failure. Breast cancer responsible for 1.6% of annual deaths of women worldwide, has an increased risk of cardiotoxicity for the patients receiving chemo and radiation (Clark et al., 2017;Florescu et al., 2013). It has been demonstrated that BRCA1 mutations amplify anthracycline-induced cardiotoxicity in breast cancer treatment leading to cardiac failure (Singh et al., 2013). Thus, in view of the therapeutic importance of natural metabolites in a number of diseases including cancer and cardiotoxicity, in this article 182 pharmacokinetically screened natural metabolites along with 147 approved cancer drugs (for comparison) were analyzed using molecular docking and dynamics simulation analysis approaches against the two mutant complexes namely, BRCA1-RAD51 and BRCA1-AURKA. Accordingly, out of 182 pharmacokinetically screened docked metabolites, the maximum number of docked natural metabolites was found to belong to plant sources. Furthermore, these docked metabolites were found to be more efficient inhibitors of mutant complexes than that of wild type.
From literature, it was evident that among the top 10 natural metabolites, docked with both the mutant complexes, possessed anti-cancerous, antioxidant, anti-inflammatory and cardioprotective properties. Thus, natural metabolite arctigenin, a natural lignan isolated from Arctium lappa, has been reported to have anti-cancerous properties in stomach, lung, liver, breast and colon cancer cells (He et al., 2018;Maxwell et al., 2018). Similarly, lirioresinol extracted from the Magnolia fargesii seeds has been reported to possess antinflammatory and anti-cancer properties in liver cancer (Shehzad et al., 2020).
In case of mutant BRCA1-RAD51 complex, the best docked natural metabolite, among all, was found to be piperolein B. In agreement to our finding (Ngo et al. 2018) have reported piperolein B as anticancer natural metabolite against human cervical and breast cancer cell lines as well as in leukemia. Also, they have reported cytotoxic properties of alkaloids present in Piper nigrum (black pepper) in general. Even derivatives of piperine such as, pipernonaline and piperanine have also been reported to have chemo preventive role against invasive cancer (Rather & Bhagat, 2018). In addition to this, Wang et al. (2020) have reported the role of black pepper in regulation of lipid metabolism, inflammation and oxidation status in CVD like atherosclerosis.
In case of mutant BRCA1-AURKA complex, the best docked natural metabolite namely, dihydrocurcumin, a curcumin derivative obtained from curcuma longa plant, has been reported for its anti-cancerous, antioxidant and anti-inflammatory properties in breast, lung, colorectal and hepatocellular carcinoma (Nagahama et al., 2016;Venkatesan, 1998). Curcumin and its derivatives have been reported to reduce adriamycin-induced cardiotoxicity through its antioxidant role (Wongcharoen & Phrommintikul, 2009). There are several reports for curcumin and its derivative having a prominent role against many cardiovascular diseases like myocardial infarction, cardiac hypertrophy, atherosclerosis and heart failure (Cookson et al., 2014;Li et al., 2020).
When the results of docking of natural metabolites with the two complexes were compared with those of corresponding drugs, it was found that in case of BRCA1-RAD51 complex, the best docked drug valrubicin while in case of BRCA1-AURKA complex the best docked drug mitoxantrone was comparable to that of best docked natural metabolite piperolein B and dihydrocurcumin, respectively.
Valrubicin which is an analog of anthracycline doxorubicin has been reported as a potent chemotherapeutic agent  against bladder cancer (Guan et al., 2020). Similarly, mitoxantrone (an anthraquinolone) is one of the oldest chemotherapy drugs used as first-line treatment against breast cancer (Kennedy et al., 2004). Chemotherapy drugs are known to function by damaging DNA directly or indirectly (Minotti et al., 2004). However, anthracyclines like valrubicin, doxorubicin, epirubicin, etc., are potent chemotherapeutic agents but their utilization is limited due to dose-dependent cardiotoxicity which causes CVD (Vasu & Hundley, 2013). Similarly, mitoxantrone has also been reported to cause myocardial infarction and arrythmia (Keskin et al., 2016). The regiospecificity and non-covalent interaction analysis of mutant BRCA1-RAD51 and BRCA1-AURKA complexes, revealed the stability of both natural metabolites and drugs at the binding site.
In order to further validate the results of molecular docking and to parameterize the electrostatic interactions to observe the actual behavior of real molecules in motion and to fit quantum-mechanical calculations and experimental data (Durrant & McCammon, 2011), the BRCA1-RAD51 and BRCA1-AURKA mutant complexes with best docked natural metabolites and drugs namely, piperolein B, dihydrocurcumin, valrubicin and mitoxantrone, respectively, were when subjected to MDS, it was found that in mutant BRCA1-RAD51 complex with piperolein B and valrubicin, natural metabolite gave a better RMSD as compared to that of drug. The binding free energy calculations also corroborated the MDS results as natural metabolite piperolein B had significantly better binding energy as compared to that of drug valrubicin. Similarly, in BRCA1-AURKA complex results with respect to RMSD as well as binding free energy were in favor of natural metabolite dihydrocurcumin, which was significantly more stable than the drug mitoxantrone.
The results of MDS analysis also revealed that both the natural metabolites were present at the interacting interface of BRCA1 and RAD51/AURKA complexes, during the equilibrium phase of MDS thereby suggesting its role in modulation of the protein-protein interaction. Thus, the negative modulation of mutant BRCA1-RAD51 and BRCA1-AURKA complexes with natural metabolites pipeorlein B and dihydrocurcumin could be further analyzed for their role against cardiotoxic effects of cancer drugs through wet lab experiments.

Conclusion
Modulation of interaction of mutant BRCA1 and RAD51 as well as mutant BRCA1 and AURKA was done using molecular docking and dynamics simulation analysis. The therapeutic importance of natural metabolites has been observed in cancer and also for their cardioprotective roles combating cardiotoxicity caused by chemo and radiotherapy. A total of 182 pharmacokinetically screened metabolites and along with 147 FDA-approved cancer drugs as control, were subjected to molecular docking. Alkaloid piperolein B was the best docked natural metabolite in case of mutant BRCA1 and RAD51 whereas dihydrocurcumin, a derivative of curcumin, was the best docked natural metabolite in mutant BRCA1 and AURKA complex. Along with these arctigenin, liriosinol, kaempferol, quercetine, bufalin, etc., were other best performing natural metabolites in both the complexes. Anthracyclin, valrubicin and anthraquinolone, mitoxantrone were the best docked cancer drugs in both the complexes, respectively. The overall analysis of Libdock scores of wild type and mutant complexes of BRCA1-RAD51 and BRCA1-AURKA, reflected better Libdock scores for mutant complex as compared to wild type. The data suggested the better binding of ligand in the binding pocket of mutant complex compared to the wild type. Molecular dynamics simulation analysis and binding free energy calculations for the best docked natural metabolite and drug for both the mutant BRCA1 complexes suggested better stability for the natural metabolites piperolein B and dihydrocurcumin as compared to drug.