1 Introduction

Fish In 1986, Ranjan Sen and David Baltimore discovered the transcription factor which binds to the enhancer element of immunoglobulin kappa light chain of activated B cells covering the sequence GGGACTTTCC by using simple gel-electrophoresis-mobility-shift -assay. They named the factor NFkB as this nuclear factor bonded selectively to k enhancers of the tumours B cells (Baltimore, 2009). Years of research has spread light into the mechanism of action of NFkB pathway which revealed that the associated proteins of the pathway are expressed in nearly in all cells and dictate many cellular signaling pathways. These proteins exist as dimeric transcription factor  and  orchestrate various metabolic processes as well as immunological responses (Karlsen et al., 2007; Madonna et al., 2012; Maqbool et al., 2013). NFkB family includes five members, designated as p65 (RelA), RelB, c-Rel, NF-kB1 and NF-kB2. The NF-kB1 and NF-kB2 proteins are synthesized in pre-forms (p105 and p100) and are later processed by proteolytic cleavage into p50 and p52. All members form homo or heterodimers and have some common structural features like Rel homology domain (RHD), which helps in dimerization as well as in DNA binding (Perkins and Gilmore, 2006). In most inactive cells the NFkB dimmers are bound to an inhibitor known as IkB (Inhibitor of kappa B). These factors have characteristic six ankyrin repeat domain which bind the DNA binding domains of the dimmers rendering them inactive. The ankyrin repeats of the pro p50 and p52 are cleaved by auto-proteolytic cleavage, hence making self-inhibition. p50 and p52 unlike other members do-not have transactivation domain hence when these dimmers bind to the kappa B region of DNA they act as repressors of the transcriptional pathway. However, when they bind to any member having transactivation domain they act as transcriptional activator. Bcl-3, a member of the IkB family consists of transactivating domain and makes the p50-p52 dimmer transcriptionally active upon binding (Gilmore, 2006).

 

NFkB is involved in a variety of cellular processes like cell proliferation and apoptosis, neural development, response to infection, inflammation. Malfunctioning of the NFkB results in the onset of chronic inflammatory diseases like cancers and neurodegenerative disorders (Lawrence, 2009). The regulation of the pathway is very essential for treating these diseases. Many upstream and downstream molecules are involved in the cascade, which interact and form a further complicated network. A better understanding of the pathway may provide the missing links and new targets for designing and developing drugs. In this review, we focused on the molecular and computational modeling studies which decipher the cross-talks of the NFkB pathway. This review also focuses on the understanding of function of E3 ligases and their inhibitors and how computational tools can help in finding new potential drug candidates using virtual screening and QSAR studies.

 

2 Pathways for NFkB Activation

The canonical and the non-canonical pathways are the two major pathways involved in the NFkB signaling. In the canonical pathway, signals are received from the receptors like tumour necrosis factor receptor (TNFR), interleukin 1 receptor IL-1R and toll like receptors (TLRs). This pathway is mainly dependent on phosphorylation of IkB by IKK-B and NEMO, which leads to IkB degradation by the ubiquitin-proteosomal pathway (UPS). Following degradation of IkB, the NFkB particles mostly p65 are released. The homo/hetero dimmers then translocate to the nucleus with the help of importing proteins and bind the DNA to carry out the process of transcription. Phosphorylation of two serine residues (Ser 177,181) in the activation loop of IKKB kinase enhances its activity (Gilmore, 2006; Perkins and Gilmore, 2006).

 

The non-canonical pathway regulates specific immunological processes by using several specific IkB family members. In contrast to the IkB degradation in the canonical pathway, the non-canonical pathway depends on the phosphorylation and auto-proteolysis of the NFkB factor p105 and p100. The C-terminal domain of p105 and p100 exhibits structural homology with the IkB and inhibit the nuclear localization of associated NFkB factors by masking their NLS domain. The processing of p100 is suppressed by C-terminal portion. The C-terminal portion of contains a processing inhibitory domain (PID) and an ankyrin repeat domain (ARD). The PID consists of a death domain (DD) structure whereas the ARD is known to mask the nuclear localization signal (NLS) present in the N-terminal Rel-homology domain of p100. p100 is phosphorylated at two C-terminal serine residues by IKK-a homodimer along with NFkB inducing factor (NIK) upon reception of signal(Sun, 2010, 2012). NIK is subjected to continuous degradation by TRAF3 E3 ubiquitin ligase under normal cellular environment. Recent research showed that NIK ubiquitin ligase is composed of TRAF3, TRAF2 and cIAP1, in which TRAF3 functions as a substrate binding subunit for NIK. Upon reception of signal from a specific sub-set of TNF receptor superfamily, TRAF3 degradation is initiated leading to NIK stabilization and hence its accumulation (Sanjo et al., 2010; Sun, 2012). NIK accumulation and its association with IKK-α leads to ubiquitination of p100 which produces p52. p52 is known to prefentially bind RelB, hence UPS degradation of p100 leads to the nuclear localization of p52/RelB complex. There are additional NFkB pathways which do not depend on IKK action (Li et al., 2010). Pathways like UV induced NFkB activation does not appear to use the IKK complex. Phosphorylation of IkB-α at Tyr 42 alone leads to the activation of NFkB pathway (Campbell et al., 2001). NFkB is a complex pathway controlling many cellular processes which involves many factors upstream as well as downstream. The different transcriptional factors have specified binding regions in the DNA. Separate NFkB factors are involved in the activation of different target genes.

 

2.1 Cross-talk of NFkB with other pathways

NFkB pathway regulates a broad spectrum of biological processes in the cell. The question raises that, how a few set of proteins of the NFkB pathway can regulate such diverse number of cellular processes. Research demonstrates that NFkB pathway is not quarantine and our picture of NFkB pathway is mostly one-dimensional. Biological signaling is mostly defined by the feedback circuits which include dynamic circuit networks. The function of its constituents are mostly described not only by its individual function but also by their interactions and cross-talks with the components of other signaling pathways (Oeckinghaus et al., 2011). NFkB pathway has numerous parallel interaction networks and cooperativities with various other pathways. NFkB interacts with PI3/AKT pathway (Hussain et al., 2012)in modulating anti-apoptosis in lymphoma cells, also reactive oxygen species (ROS) which interacts at various points within the signaling pathway to activate or deactivate the NFkB pathway are examples of cross-talk of NFkB with other components in the cell(Morgan and Liu, 2010).  Seeking to the diversity and complexity of the NFkB pathway researchers are now combining computational-mathematical modeling with biochemical experimentation to get a clear picture of the components and their interactions in the pathway. This combined approach not only provides the insights of the molecular interactions but also the kinetic details of the interactions. However, it is important to note that the effect of a signaling molecule on NF-κB often strictly depends on the cell type or the micro-environment and that even opposite effects can occur in distinct cell types.

 

2.2 Computational modeling of NFkB pathway

A mathematical modeling represents a well-coordinated setting which helps to test different predictions based on previous biochemical data for the working of a system. Mathematical modeling often leads to finding missing links in the complex signaling networks. A mathematical model could be simple, describing the features but not the realistic cascade or it could be detailed providing a comprehensive view of the signaling network. Building a model requires three steps – Firstly, selecting the components and getting their detailed interaction overview from previous experimentation data. Secondly, quantifying concentration and strength of the components by selecting appropriate parameters, defined as the rate constants. Lastly, a mathematical formula has to be deduced for simulation of the components (Chaplain, 2011). Reaction-network models assume that each molecule in the cell is uniformly distributed within. This gives rise to ordinary differential equations (ODE) - one equation for each molecule (Eungdamrong, 2004). As NFkB pathway governs enormous cellular signaling pathways, modulation of the pathway is of prime importance in drug research studies. The use of computational language in investigating the complex networking of the NFkB pathway has produced some amazing understanding of its functioning and its interaction with other components within the cell. As IkB plays a central role in regulating the activities of the NFkB pathway, it has been the major focus of in silico studies in recent years. Unified Modeling Language (UML) is being used as a basis for the recent computational model systems. We here have mainly focused on deterministic and stochastic modeling.

 

Hoffman et al., provided a pioneering model with a deep view of the NFkB signaling pathway describing the effect of isoforms of IkB (α, β and Є) on NFkB regulation. The model was comprised of 24 ODEs delineating the concentration fluctuations of NFkB and IkB in cytoplasm and nucleus using genetically reduced systems. It also contained 30 parameters from previous experimentation data. They explicitly modeled the interaction of NFkB with IkB and their translocation, the interaction of IKK with the NFkB-IkB complex, degradation rates of IkB and transcription and translation of IkB isoforms. Their final results demonstrated that the oscillatory dynamics of the NFkB is dictated by the concentration of two variables, the IKK and IkB. O'Dea et al., built a model using Hoffman’s model for IKK-IkB-NFkB signaling modulation. They used MATLAB and Excel with extended equilibration time. Isoforms of IkB were not considered and hence the rate constants for IkBα, IkBβ and IkBЄ were considered to be similar (Dea et al., 2007). Cheong et al., demonstrated by his modeling studies that the combinatory action of the three isoforms of IkB mediates the distinction between long and short term stimuli (Cheong et al., 2008). Kearns et al., demonstrated that during long lasting activity of NFkB, IkBЄ dampens the oscillations mediated by IkBα (Kearns et al., 2006). Basak et al. built a model to introduce p100 and LPS induction of IKK mediated IkB degradation (Basak et al., 2007). Shih et al., demonstrated that IkBδ provides negative feedback to NFkB during sequential signal induction (Shih et al., 2009). Recently, Alves et al. demonstrated that IkBЄ provides negative feedback to cRel and RelA to regulate the B-cell expansion (Alves et al., 2015). Lipniacki et al. modeled IKK, NFkB, IkB and IKK inhibitor in nucleus and cytoplasm using 15 ODEs or differential equations. Lipniacki et al. combined deterministic as well as stochastic modeling to find that single TNF-α molecule can induce a massive NFkB induction (Lipniacki et al., 2007). Choudhary et al. demonstrated that TRAF1 and NIK activity regulates the coupling of canonical and non-canonical NFkB pathway (Choudhary et al., 2013). Ashall et al. used semi-stochastic modeling approach to determine the NFkB stimulation  by TNFα at various time-intervals (Ashall et al., 2009). Tay et al. used various stochastic models to show the heterogeneity of NFkB in single cell level (Tay et al., 2010). Pogson et al., used agent based approach to provide a more in-depth understanding of the NFkB pathway which was not possible by the equation based methods. Their result showed that the actin filament of the cytoskeleton sequesters excess inhibitors to the NFkB pathway regulating the steady state kinetics (Pogson et al., 2008). Most recently Fagerlund et al. used computational modeling to explain that NFkB negative feedback is not only dependent on the induction of NFkB pathway but rather it also depends on the nuclear import and export of IkBα and IkBα-NFkB complex and also on the half life of IkBα (Fagerlund et al., 2015).

 

Initiated by Hoffman, computational modeling has come a long way in solving various complex networks in the NFkB signaling pathway using programs such as MATLAB. Hence, computational modeling studies are a very helpful tool in solving the complex signaling pathways and may provide a better perspective in understanding cancers and other inflammatory diseases.

 

3 Inflammation, NFkB and Disease

Regulation of the NFkB pathway is essential for cells to changing environmental conditions. Inflammation is body’s protective response to fight against any injury or infection. Most of our knowledge on inflammation is derived from studying the signaling pathway mediated by IL-1, TNF (TNFR1 and TNFR2) and Toll Like receptor families. The inflammatory response is mediated by activated macrophages and other immune cells which ultimately results in the transcriptional up regulation of TNF-α, IL-1 and proinflammatory cytokines (Landskron et al., 2014; Popa et al., 2007). Introduced in 2002, a new term “inflammasome” came in the limelight (Martinon et al., 2002). Inflammasomes are multimeric protein complexes which comprise of danger sensing proteins which are activated during microbial attack or free radicals. They assemble in the cytosol after sensing pathogen associated molecular patterns (PAMPs) or damage associated molecular patterns (DAMPs). There are NLRP1, NLRP3, IPAF and AIM2 type of inflammasome known so far which regulates IL-1β maturation. Inflammasome mediated pathway is one in many of the pathways used by activated NFkB to signal the cell to produce proteins related to inflammation. Inflammasome activation is a three step process. Firstly NFkB pathway is initiated in response to incoming signal. Then, NFkB directs the cells to form protein complexes which would function as inflammasomes. Lastly, these activated inflammasomes mediate the response by signaling the production of inflammatory proteins (Guo et al., 2015; Martinon et al., 2002; Rahman et al., 2009). Insufficient inflammation can result into persistant microbial infection whereas excess of inflammation result in chronic inflammatory diseases. Chronic inflammation refers to the prolonged low-level state of inflammation which can last from days to month. Chronic inflammation is associated with almost every chronic disease including cancer and neurodegenerative diseases (Maqbool et al., 2013; Multhoff et al., 2011; Popa et al., 2007; Rakoff-Nahoum, 2006). It contributes to cancer pathogenesis by promoting angiogenesis, invasion and metastasis. It also known to result in resistant to chemotherapy (Koti et al., 2015). During chronic inflammation cells produce a large amount of deleterious free radicals which interact with DNA and cause DNA damage by introducing mutations. As long as the cell is able to escape this condition by either undergoing DNA damage repair or apoptosis cancerous cells do not develop (Mittal et al., 2014). However, during the state of chronic inflammation there is constant NFkB signaling which shuts down the DNA repair mechanism and apoptosis. As a result, a large number of mutated cells aggregate which ultimately transforms into cancer cells. The inflammatory signals block the immune cells to reach the newly formed cancer cells. These signals promote the invasion and proliferation of malignant cells into their surroundings. One of the most lethal actions of these cancer cells is that they turn on their own NFkB pathway therefore taking over the whole tissue repair system of the body. They turn on the similar inflammatory response in the surrounding tissues (Lawrence, 2009; Tak and Firestein, 2001). Chronic inflammation is also known to reduce insulin sensitivity of the cells resulting in high sugar levels which leads to metabolic disorders, diabetes type II and obesity (Luft et al., 2013).  Under normal conditions, NFkB is constitutively expressed in a few cell types such as neurons and inactive in other cells, but in cancer cells it remains constitutively active (Mauro et al., 2009; Nakshatri et al., 1997). A defective TNFR2 system gives rise to many autoimmune diseases. High levels of TNFR2 are associated with rheumatoid arthritis, Crohn’s disease (Sandborn, 2001)and systemic lupus erythematosus (Aringer and Smolen, 2008). Receptor shedding may be a way to decrease the concentration of TNFR2 in the cell which will ultimately lower down the inflammatory response (Deng et al., 2015; Xanthoulea et al., 2004). Inhibition of NFkB pathway in selective cells can prove to be an attractive therapeutic proposal for treating or subliming various diseases resulting from neurodegeneration. (Karlsen et al., 2007; Lawrence, 2009; Maqbool et al., 2013; Qin et al., 2007).

 

4 Inhibitors of the NFkB Pathway

Owing to the plethora of cellular functions governed by the NFkB pathway, the regulation and inhibition of NFkB pathway by various levels of the pathway could be useful in various dysfunctions of the cell. There are more than 700 known inhibitors which regulate the NFkB at various levels (Gilmore and Herscovitch, 2006). The NFkB pathway in general could be regulated at three points: 1) blocking the signal reception by interfering with the receptor-ligand binding resulting in the abolition of the signaling cascade at the initial stage. Bluml et al. described therapeutic consequences of blocking TNF receptor in arthritis (Bluml et al., 2012)2) by interfering with any of the factors involved in the cascade in the cytoplasm. Targeting IKK (Luo et al., 2005), IkB (Anchoori et al., 2010)and 3) targeting the nuclear translocation of the NFkB factors or interfering with their DNA binding (D’Acquisto, 2002). The blocking of the E3 ligases and targeting the nuclear translocation of the NFkB pathway can confer specific inhibition and response and hence could discourage cellular toxicity in patients. We here have discussed about the details of E3 ubiquitin ligases and why they are a potential drug target.

 

4.1 E3 ubiquitin ligases: activity and inhibition

In the NFkB pathway, ubiquitin-proteosomal system (UPS) plays a major role in selective catabolism of proteins in the cell (Figure 1). Avram Hershko, Aaron Ciechanover and Irwin Rose were awarded the Nobel Prize in 2004 for deciphering the central role of the UPS. Ubiquitin protein is found only in eukaryotic cells and is ubiquitously expressed in all cells. It is localized in the nucleus as well as the cytoplasm (Melino, 2005). The ubiquitination cascade is catalyzed by the interplay of three enzymes: E1 which is an ubiquitin activating enzyme, E2 which acts as ubiquitin conjugating enzyme and E3 which shows ubiquitin ligase activity. E1 activates ubiquitin by forming thioester linkages between the Cys residue of E1 active site and C-terminus Gly 76 residue of ubiquitin. The E1-ubiquitin complex is transferred to E2. The E2 also forms thio-ester linkages via active site Cys residue with the Gly 76 of ubiquitin. The final step is catalyzed by E3 ligase where it mediates covalent attachment of the ubiquitin by forming an iso-peptide bond between the internal Lys residue of the target protein and Gly 76 of ubiquitin. Multiple rounds of the cycle results in K48 linked poly-ubiquitinated conjugates. This polyubiquitin acts as a marker of protein for identification by 26S proteosome (Ciechanover, 1994; Glickman and Ciechanover, 2002; Landré et al., 2014; Scheffner et al., 1995). Lys 6, 11, 27, 29, 33, 48 and 63 are the specific Lys residues of the ubiquitin by which it can conjugate to itself and participate in chain formation, Lys 48 and 63 being more common. Gly residue in the C-terminus of one ubiquitin molecule attaches to any seven Lys of another ubiquitin (David et al., 2010). The recognition of a substrate by E3 ubiquitin ligase for ubiquitination is the most important step in conferring specificity of the reaction. The ubiquitination reaction could be reversed, the process is named as deubiquitination. The enzymes catalyzing these reactions are termed as deubiquitinases (DUBs) (Amerik and Hochstrasser, 2004). There are also a group of proteins which are similar to ubiquitin and are termed as ubiquitin-like proteins (UBLs). Some of the known UBLs include SUMO, NEDD8, ISG15 and FAT10 (Herrmann et al., 2007). The E3 ligases can be classified into three types based on domain, structure and mode of action: the HECT (homologous to E6-associated protein C-terminus) type, the RING (Really Interesting New Gene) finger type and the U-box family (modified RING finger without the full Zn2+ binding ligands) E3 ubiquitin ligases. The RING type can be further classified into two groups, RIR (RING in between RING-RING) and multi-protein complexes CRL (Cullin-RING E3) type. The RING type and the structurally similar U-box family E3 ligases bind the E2-ubiquitin complex and target protein simultaneously, acting as scaffolding proteins. It transfers the ubiquitin from E2 to the target protein by placing the target Lys close to E2-ubiquitin thioester bond. In the HECT type E3 ligase ubiquitin is first transferred from E2 to the active site Cys residue of the E3 ligase. The thioester linked ubiquitin is then transferred to the target protein (Metzger et al., 2010).

 

Figure 1 Schematics of UPS pathway Note: It is a cascade mediated by the interplay of three enzymes, E1(red), E2(green) and E3(purple). Ubiquitin (yellow) is activated by binding with E1 (activation) which is then transferred to E2 (conjugation) and lastly to E3 ligase (ligation). E3 transfers the ubiquitin to the target protein (grey). As the cycle goes on, the protein is poly-ubiquitinated and is finally targeted to the proteosome (pink) for degradation. Ubiquitin molecule becomes free and is cycled back. It is an energy consuming step.

 

The E3 ubiquitin ligases are the one which recognize the target proteins and prepare it for proteosomal degradation. Hence, E3 ligases confer more specificity to the reaction than E2 or DUBs and can be a potential drug target conferring specific inhibition to the NFkB pathway. Selected E3 ligases are considered good therapeutic targets as discussed:

 

4.1.1 HECT

The HECT domain is a bi-lobed structure. It comprises of an N-terminal and a C-terminal lobe tethered by a flexible hinge. The larger N-terminal lobe contacts with the E2 enzyme whereas the smaller C-terminal lobe which contains the reactive site Cys residue. The hinge region helps in positioning the active site Cys residue of E2 with the E3 during ubiquitination. The thioester bond formation requires conformational changes which involves changes in the orientation of each lobe (Maspero et al., 2013). There are ~30 HECT domain E3s in mammals. The HECT domain is positioned at the C-terminus of E3 enzymes. The terminal 60 amino acid residues of the C-terminal lobe contribute to the specificity of ubiquitin chain linkage. The HECT E3s are grouped into three families: the Nedd4 family, the HERC family and other HECTs (Scheffner et al., 2014).

 

The Nedd4 HECT E3s consist of nine members. The structure of Nedd4 family E3s have a C2 domain at the N-terminus, 2-4 WW domains and a HECT domain at the C-terminal end. The WW domain identifies prolinne rich consensus sequences, PPXY or PY. They also interact with phosphorylated Ser and Thr residues. Some of the members of Nedd4 along with their cellular functions are: ITCH (cell differentiation and signaling), NEDD4L (cellular uptake) , SMUR1/2 (cellular signaling)  and NEDD4 (transcription)(Ingham et al., 2005; Scheffner et al., 2014). Many tumor suppressor proteins act as substrate for the HECT E3s, hence revealing their oncogenic potential. Also, genetic mutation in some of the family members of HECT E3s leads to cancer progression (Bernassola et al.,  2008). Human HERC family consists of six members and is categorized on the basis of molecular mass, HERCs of more than 500 kDa are placed in one category and HERCs of molecular mass of ~120-130 kDa are placed in other category. HERCs consist of a characteristic RLD domain. Smaller HERCs carry a single RLD whereas larger HERCs have more than one RLD. A classical RLD consists of seven-repeats of 50-60 amino acids, it was first demonstrated for RCC1 protein. The activity of the RLD of HERCs is still not clear. HERCs are believed to be evolved from the nematodes. Other HECTs exhibit domain variations (Hochrainer et al., 2005).

 

4.1.2 RING

In mammals more than 600 genes code for RING E3 ligases (Deshaies and Joazeiro). A RING domain coordinates a pair of zinc ions and contains 50-60 amino acids. RING domain was discovered by Freemont et al., in 1991(Freemont et al., 1991). The conserved Cys and His residues of RING domain are buried in the core probably for maintaining the overall structure. The two zinc ions and the interacting residues form a criss-brace motif (Deshaies and Joazeiro). However the RING domain of TRAF6 does not form any criss-brace motif, the Cys residue in the C-terminal involved in Zn ion binding is replaced by an Asp residue (Mercier et al., 2007). The RING unlike the HECT do-not form a catalytic site rather it forms a scaffold or linker to bring the E2 and the substrate in close proximity. The zinc binding sites in the RING finger form a rigid structure essential for protein-protein interactions; this unique feature distinguishes them from the Zn fingers (Metzger et al., 2010). RING E3s can function as monomers, dimmers or oligomers. RING domain is involved in the process of dimerization (Liew et al., 2010; Yudina et al., 2015)to form a higher order structure for interacting with E2. Some of the important examples of RING E3 ligases as good prospects for drug targeting are: TRAF family proteins, Parkin, SCF-cullin and MDM2. These E3 ligases are involved in the progression of chronic inflammation which leads to cancer progression and neurodegenerative diseases.

 

4.1.2.1 TRAF

TRAF derives its name from tumor necrosis factor receptor associated factor and as the name implies binds the TNF receptor to its cytoplasmic side. Six known members for the TRAF family TRAF (1-6) are known till date. All TRAFs except for TRAF1 contain RING domain on its N-terminal end. Besides working as E3 ligases, TRAFs work also work as adaptor proteins associating upstream and downstream signaling factors. TRAF2, 3, 5 and 6 function as E3 ligases. TRAF6 is a major E3 ligase apart from interacting with TNF receptors also interacts with interlukin-1 (IL-1) receptor and toll-like receptor (TLR) superfamily to mediate immunological responses. This very property makes TRAF6 unique and different from other TRAF members. TRAFs consist of an N-terminal RING domain which is followed by zinc fingers and a conserved C-terminal MATH/TRAF domain (Figure 2). MATH/TRAF domain is engaged in receptor recognition and oligomerisation (Lamothe et al., 2008; Wu and Arron, 2003). Sequence analysis studies reveal diversity in the C-terminal domain and gene structure of TRAF6. TRAF6 engages in E3 ubiquitin ligase activities by interacting with E2 hUbc13 through the RING domain. Studies on TRAF6-E2 association suggest that RING domain alone is not sufficient for the interaction and requires the involvement of the first zinc finger (ZF1) (Yin et al., 2009). There is always fixed spacing in between the zinc fingers of all members of TRAFs representing a conserved feature of the zinc finger arrangement (Yin et al., 2009). Reports suggest TRAF6 ubiquitinates and activates the Akt signaling pathway(Yang et al., 2009).

 

Figure 2 TRAF6 E3 ubiquitin ligase Note: (a) Domain architecture showing an N-terminal RING domain (green) preceded by four zinc fingers (yellow) and TRAF domain (purple) at the C-terminus. (b) ribbon representation showing RING domain of TRAF6 (green) binding to E2 enzyme (PDB ID: 3HCT). The first zinc finger (Z1) along with the RING domain is involved in TRAF6-E2 interaction. Grey balls represent zinc ion.

 

4.1.2.2 Parkin

Misfolded protein misfolding and aggregation is a hallmark of the neurodegenerative disorder, Parkinson’s (Tan et al., 2009). Malfunctioning UPS system contributes to the pathological progression of the disease (Lim and Tan, 2007). Mutations in parkin, an RING E3 ligase contributes to around 50% of the cases of juvenile parkinson’s. Parkin is involved in maintaining mitochondrial quality and mitochondrial autophagy (Figure 3). Upon signal reception, parkin ubiquitinates mitochondrial membrane proteins leading to their degradation and hence elimination of any damaged organelle. Mutations in parkin gene lead to accumulation of damaged mitochondria which is a source of reactive oxygen species (ROS). ROS is a major cause of neural cell degeneration (Durcan and Fon, 2015; Tanaka, 2010). Parkin belongs to the unique RING-between-RING (RBR) sub-class of RING E3 ligases. The RBR type of ligases is known to have a active site Cys residue in addition to the usual RING domain. Hence, they function as RING as well as HECT type E3 ligases. Structure of parkin involves an ubiquitin (Ubl) domain in its N-terminal and RBR domain in the C-terminal end. The overall structure of RBR domain consists of two compact domains. First has the merged R1 and in-between R (IBR) domain structure. R1 domain represents the classical RING domain cross-brace structure and is known binds E2. Second domain comprises the R0 and R2 in close proximity. The R2 domain comprises of the catalytic Cys 431 residue. Mutation of the hydrophobic residues in R0-R2 domain results in autoubiquitination of parkin. The IBR domain forms a bi-lobed structure and co-ordinates two Zn ions. Mutations in particular regions leads to the malfunctioning parkin : 1) In the Zn binding residues of the R1 domain resulting is structure collapse 2) residues binding to E2, inhibiting the E3-E2 intractions 3) in the active site pocket with Cys 431(Beasley et al., 2007; Riley et al., 2013). As loss-of function mutations in parkin gene leads to the onset of Parkinson’s syndrome, drugs inhibiting the autoubiquitination of parkin may prove to be neuroprotective.

 

Figure 3 Parkin Note: (a) Domain architecture of parkin (RBR) E3 ligase. Ubl (ubiquitin binding domain) in N-terminus, yellow binds E2 enzyme.  RBR domain is located at the C-terminus consists of R0(pink), R1(green), IBR (blue) and R2(red). R1 represents the real criss-brace RING domain, while the R2 consists of the catalytic Cys 431 residue and act as a HECT domain. (b) Ribbon representation of parkin protein (PDB ID: 4I1F) showing R2(red) in close proximity to R0 (pink). L and T means linker and tether respectively. Grey balls represent zinc ion.

 

4.1.2.3 SCF

SCF (Skp1, cullins, F-box) are a class of multisubunit RING E3 ligases. It is the largest E3 ubiquitn ligase family and is involved in regulation of ~20% protein regulated by the UPS. The SCF comprises of four structural domains: a F-box protein which binds substrates and confers substrate specificity, SKP-1 which acts as an adaptor protein, cullin (CUL -1,-2,-3,-4,-5 and -7) which acts as a scaffold protein and RBX/ROC RING also known as SAGs (sensitive to apoptosis) proteins. The cullin protein binds SKP-1 and F-box protein in its N-terminus and the RBX/ROC RING in its C-terminus (Figure 4). The CUL/RBX mediates the ligase activity by transferring the ubiquitin from E2 to the substrate. Human genome is known to code around 69 F-box proteins, with only few of them being well studied. The F-box protein binds the SKP-1 and cullin protein by the F-box domain and substrates by the leucine rich or WD40 domains (Jia and Sun, 2012; Zheng et al., 2002). Cullin in the cytosol is inhibited by CAND1, neddylation of cullin disrupts its association with CAND1 and makes it functional(Merlet et al., 2009). Majority of SCF regulated substrates are involved in various cell signaling cascades. Evidences suggest malfunctioning SCF in cancer progression(Skaar et al., 2014). Fbw7 an F-box protein , is a tumor suppressor (Welcker and Clurman, 2008)and is found to be mutated in many cancers(Calhoun et al., 2003; Jardim et al., 2014). Over expression of the SCF component Skp2, which acts as an oncogenic is related to cancer progression (Gstaiger et al., 2001; Yang et al., 2002). The many components of SCF RING E3 ligases could be regulated at various levels making them an attractive drug target.

 

Figure 4 Schematic diagram of SCF E3 ubiquitin ligase Note: F-box (orange) provides the specificity by binding to the substrate. Skp1/2 (blue) acts as a linker between cullin-1 (pink) and F-box proteins. RBX/ROC (green) is the RING proteins which bind to E2 enzyme to mediate the process of ubiquitination. It transfers the ubiquitin molecule (yellow) from E2 to the substrate.

 

4.1.2.4 MDM2

Tumour suppressor protein p53 is regulated in two ways: first by post-translational regulation and second by the RING E3 ligases, MDM2 and MDMX. Functional MDM2 is either a homodimer or a heterodimer with MDMX. MDM2 works as an antagonist in regulation of p53. Murine double minute 2 (MDM2) known as HDM2 in humans, binds and blocks the N-terminal transactivation domain (TAD) of p53 protein and targets p53 for ubiquitin-proteosomal degradation(Wade etal., 2010). MDM2 comprises a p53-binding domain in its N-terminus followed by an acidic domain and a zinc finger domain. The RING finger domain containing the nucleolar localization signal (NoLS) lies at the C-terminal end. The structure of MDMX is similar to MDM2, the acidic domain is shorter. MDM2 consists of nuclear localization signal (NLS) and nuclear export signal (NES) which lies in between the p53 binding domain and the acidic domain, these features are missing from the structure of MDMX (Figure 5).

 

Figure 5 MDM2 E3 ligase Note: (a) domain architecture shows N-terminal p53 binding domain, preceded by an acidic domain (orange) and a zinc finger motif (yellow) and a C-terminal RING domain (magenta). Nuclear localisation signal (NLS) and nuclear export signal (NES) are present in between p-53 binding domain and acidic domain. The RING domain consists of an nucleolar localisation signal (NoLS). (b) Ribbon representation shows binding of MDM2 (magenta) binding to MDMX (blue) by their respective RING domains (PDB ID: 2VJE). Grey balls represents zinc ion.  As MDM2 negatively regulates p53 tumor suppressor protein in the cell, molecules inhibiting the activity of MDM2 can be beneficial in cancer therapy. Also, as a functional MDM2 is either a homodimer or a heterodimer with MDMX interacting with their RING domains, inhibitors of the RING domain may be attractive in drug therapy for targeting MDM2.

 

Mdmx consists of a RING domain but lacks ubiquitin activity. Mdm2 interacts with Mdmx via their respective RING domains(Tanimura et al., 1999). MDM2 is also known to ubiquitinate the DNA binding domain (DBD) of p53 in addition to C-terminal domain (Chan et al., 2006). By attaching ubiquitin in Lys residues in the DBD domain of p53, MDM2 mediates nuclear export of p53(Gu et al., 2001). MDM2 regulates its cellular concentration by undergoing autoubiquitination. Self-ubiquitination increases its substrate’s ubiquitin ligase activity, as ubiquitinated MDM2 recruits more E2 and increases its concentration (Ranaweera and Yang, 2013). 

 

The discovery that inhibition of proteosome can induce apoptosis in cancer cells has made the UPS a centre of attraction as a drug target in cancer progression. The FDA in 2003 approved the drug bortezomib which inhibits the proteosome by binding to its β5 sub-unit reversibly. This results in the crowding of polyubiquinated proteins in the cell (Chen et al., 2011). Although bortezomib proved to be effective in clinical trials, the effectiveness of the drug is very narrow and also it confers cyto-toxic effects due to accumulation of misfolded proteins in the endoplasmic reticulum. This activates the unfolded protein response (UPR) in the cell which ultimately results in massive tumor necrotic response (Obeng et al., 2006). Carfilzomib was approved by FDA in 2012, it irrevesibly inhibits the proteosome to a site other than bortezomib and is used for the treatment of lymphoma and multiple myeloma (McBride et al., 2015). Rather than blocking the general proteosome, inhibitors which target a particular E3 ligase could be more pharmacologically specific with less cyto-toxic effect. Majority of the small molecule inhibitors known to inhibit the E3 ligase are known to target the RING-type E3 ligases. Pyrrolidine dithiocarbamate (PDTC) and its analogues have proved to be potent inhibitors of the NFkB pathway; they bind to the RING domain of the E3 ligases to affect its function. Studies show pyrrolidine ring binds to the Cys residues at the zinc binding site and breaks the bonds. As a result, the zinc ion pops out of the structure and the whole protein structure collapses (Carta et al., 2012; Cvek and Dvorak, 2007; Zhang et al., 2011). The major small molecule inhibitors of different E3 ligases are listed in Table 1.

 

Table 1 Molecules inhibiting the E3 ubiquitin ligases

 

4.2 Computational aided drug discovery

The Fortune magazine published an article titled “Next Industrial Revolution: Designing Drugs by Computer at MERCK” in 1981(Van, 2007). Since then the computer aided drug designing (CADD) has been on the fore front of pharmacological drug designing and discovery. Virtual screening (VS) provides the first platform for short-listing molecules which interact with the particular domain or residues of target protein (Cheng et al., 2012). With the advancement of computational processing, the method for finding a potent drug candidate is becoming faster and more accurate. Computer programs can calculate the binding energies and interaction energies of a protein-ligand, protein-protein, nucleic acid-protein and nucleic acid-ligand interaction which helps in finding the lead compound. Crystal structures of proteins, nucleic-acid and ligands are easily accessible from the databases. Computational methods can also be used to characterize and optimize the existing ligand libraries Apart from screening techniques, computational tools could also be used to identify the peptides residing in the protein-protein interface, which could be targeted to inhibit protein-protein interactions, hence discovering potential drug targets (Fletcher and Hamilton, 2007). Peptide drugs are advantageous in being selective or specific in inhibition, they also exhibit less cellular toxicity. The major problem or drawback of peptide inhibitors is their membrane impermeability and poor proteolytic stability (Fosgerau and Hoffmann, 2015; Otvos and Wade, 2014). Researchers have found solution to this problem by developing drugs with specificity and efficacy of peptide and efficient cellular uptake of small molecules. These kinds of peptides which are attached to a non-natural compound are called stapled peptides. This concept was laid by Blackwell and Grubs. Stapling of peptides provides stabilization of α-helical structure that makes peptides resistant to proteolysis and increases cell permeability (Verdine and Hilinski, 2012). The most promising examples of this new technique is the hydrocarbon-stapled α-helical peptides (Walensky and Bird, 2014). Recent research has developed stapled peptide against MDM2 E3 ligase which inhibits MDM2-p53 interaction (Chang et al., 2013; Madden et al., 2011). The process of drug discovery involves four steps:

 

1)Prediction of the target: A drug target is selected based on the prior knowledge of its involvement in the progression of the disease. Also, an in-depth knowledge of its structural detail along with the critical amino acid residues involved in its interaction with other compounds and also its stabilization can help to confer drug specificity (Hughes et al., 2011).  There are many tools and servers (Table 2) which can predict the binding site of proteins. Crystal structure of the target could be fetched from various databases, e.g., protein database (PDB) (Berman, 2008). If crystal structure of the protein is not available one could go for computer aided homology modeling which provides a predictive model of the target (Vyas et al., 2012).

 

Table 2 Computer based prediction tools for finding binding pocket and identifying critical residues in target protein

 

2)Lead generation through virtual screening: Lipinski et al., analyzed described the ideal properties of a drug after analysing more than 2000 molecules. He laid out five rules which makes a candidate molecule fit as a ligand. The five rules are: 1) less than 500 Dalton molecular mass 2) lipophilicity (LogP) less than 5 3) less than 5 H-bond donor 4) less than 10 H-bond acceptors 5) molar refractivity of 40-130. Any molecule has to pass the Lipinski’s five rules to be considered as a potential drug candidate (Leeson, 2012). Screening for lead can be carried out by two approaches: knowledge based design and screening or random screening. Zinc database is a library of small molecules with drug potential in their 3-D conformations. It has about 727 842 molecules ready to be used for virtual screening (Irwin et al., 2012). Knowledge based design requires a thorough knowledge of the chemical structure of the ligand or small molecule. This helps in narrowing of the lead compound to be screened. On the other side random screening does not require any prior knowledge of the compounds and involves screening hits from a large collection of compounds(Hoelder et al., 2012). Virtual screening is a computer program which uses structure based docking approach to screen chemicals from the library which bind to a specific target. Two different methods could be followed for screening. First, ligand based approach in which ligand is screened from the library on the basis of prior knowledge of its structural similarity to an already existing ligand. Second, structure based approach in which screening is done on the basis of fit of the ligand in the active site pocket(Cheng et al., 2012). Quantitative structure activity relationship (QSAR) calculates the relative binding energies of the ligand for studying the interaction of ligands with target (Verma et al., 2010). A steady conformation of the drug and the protein complex could be derived by applying molecular dynamic simulations (MD). MD will provide a structure free from structural restraints by the application of appropriate force-field. This is will further narrow down the screening process (Kerrigan, 2013).

 

3)Selection of a clinical candidate: Before in vivo studies a lead drug is tested for its pharmacological properties. In pharmacology and pharmacokinetics four properties of a potential drug absorption, distribution, metabolism, excretion and toxicity (ADMET) are considered before promoting it to the next level. There are many commercial softwares (ADMET predictor, MedChem studio, ADMET modeller) as well as web-servers (ALOGPS, ToxPredict, ADME-Tox) available which can successfully predict these properties of a compound ( Cao et al., 2012).

 

Clinical Trials: Finally, the selected lead compounds are clinically tested in various animal models and finally on humans.

 

5 Conclusion

The NFkB pathway marshals numerous signaling cascades in the cell. These signaling pathways encounter each-other and develop a complex signaling network in which molecules interact and influence each other’s functioning.  Biochemical assays have provided us with enormous knowledge of these pathways, yet there are some answers still unknown. Computational modeling which relies on the previous and existing biochemical evidences and use sophisticated mathematical calculations and theories to join the missing links in the hypothesis. It provides the clue of which functions related to the object are essential and are to be considered and which one should be abstracted. Hence, it helps to narrow down the search. As, NFkB mal-function gives rise to chronic inflammation which lays the foundation for diseases like neuro-degeneration, arthritis, diabetes and cancers, detailed study of its network and the molecules involved could provide a better understanding of the problem. Biochemical studies combined with computational modeling can open doors to many questions.

 

NFkB could be regulated at many levels of the pathway. Apart from others targeting the E3 ligases in the UPS pathway for drug designing and discovery would provide with drug specificity and less cyto-toxic response. Unlike other members of the pathway, E3 is responsible for substrate specificity. Mutations or dys-regualtion in the E3 ligases results in a myriad of chronic diseases. There are drugs available which bind to the HECT and RING domains of the E3 ligases to abolish their function. But, as there are around 600 E3s, the search is still on. Computer aided drug designing has open gates to search and screen for molecules which can act as a potential drug against a target molecule. Use of computational tools like virtual screening, molecular docking and molecular dynamic simulations act as predictive tools for candidate drug in pharmacology. It narrows down the search for further clinical trials. Hence, by combining in vitro, in vivo and in silico approaches the results are more reliable and with a high success rate.

 

Acknowledgements

The authors acknowledge University of Kalyani, Kalyani (W.B.) India to provide financial support and Bioinformatics infrastructure facility (BIF) funded by DBT, India for infrastucture facilities.

 

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