**2. Zebrafish as a model in pharmacology, monoamine oxidase and computational methods to evaluate binding site similarities: An overview**

### **2.1. Zebrafish as an animal model in pharmacology and neurobehavioral studies**

been reported during the last decade, allowing detailed comparison of their overall struc‐ tures and respective active sites. On the other hand, a few studies have shown that zebrafish contains a single MAO gene and that enzyme activity is due to a single form (zMAO) which resembles, but is distinct from, both mammalian MAO-A and MAO-B. No three-dimension‐ al structural data exist thus far for zMAO. Sequence comparison of the putative substrate binding site of zMAO with those of human MAO isoforms suggests that the fish enzyme re‐ sembles mammalian MAO-A more than MAO-B. Nevertheless, biochemical studies have shown that zMAO exhibits such unique behavior toward MAO-A and -B substrates and in‐ hibitors, that the results of studies using zebrafish MAO function, either as a disease model

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

Functional and evolutionary relationships between proteins can be reliably inferred by com‐ parison of their sequences, structures or binding sites. From a drug-discovery perspective, the study of binding site similarities (and differences) can be particularly insightful since it aids the design of selective or non-selective ligands and the detection of off-targets. In addi‐ tion, knowledge of ligand-binding site similarity could increase our understanding of diver‐ gent and convergent evolution and the origin of proteins, even in those cases where no obvious sequence or structural similarity exists. In recent years, a number of algorithms have been developed for the identification and comparison of ligand-binding sites. Even though each method has its own merits and limitations, the performance of these computa‐ tional tools is continuously improving. Advances in this field, associated with the increasing availability of structural data and reliable homology models of thousands to millions of pro‐ tein molecules, provide an unprecedented framework to investigate the mechanisms under‐ lying the molecular interactions between these proteins and their ligands, as well as to

evaluate the similarities between the binding sites of related and unrelated proteins

usefulness for the rational design of multitarget (promiscuous) drugs.

lective MAO-A and -B inhibitors.

On the basis of the foregoing, the first section of this chapter provides an overview on: a) the relevance of zebrafish as an animal model of increasing interest in pharmacology; b) the im‐ pact that MAO crystal structures and molecular simulation approaches have had on the de‐ velopment of novel MAO inhibitors, as well as comparative structural and functional information about zMAO and its mammalian counterparts; c) recent developments in com‐ putational methods to evaluate similarities between ligand-binding sites, emphasizing their

The second part of the chapter describes unpublished results regarding a further characteri‐ zation of zMAO activity and its comparison with MAOs from mammals. Specific topics in this section include: a) the construction of homology models of zMAO, built using human MAO-A and -B crystal structures as templates; b) a three-dimensional analysis of the bind‐ ing site similarities between MAOs from different species using a statistical algorithm; c) a functional evaluation of zMAO activity in the presence of a small series of reversible and se‐

or for drug screening, should be considered with caution.

Applications

406

In order to understand complex behaviors observed in nature, scientists have always tried to develop models that could be used and tested under controlled conditions in the labora‐ tory. In the last 30 years a new animal model, zebrafish (*Danio rerio*), has emerged as a pow‐ erful tool mostly for studying developmental biology. The scientific potential of zebrafish was originally assessed by George Streisinger (Streisinger et al., 1981). This work was the starting point for rapid progress in molecular and genetic analysis of zebrafish neurodevel‐ opment, which allowed the construction of many genetic mutants and the identification of several genes that affect different brain functions such as learning and memory (Norton & Bally-Cuif, 2010). During the last decade zebrafish has also become an attractive model for behavioral and drug discovery studies, particularly those related to actions in the central nervous systems (Chakraborty & Hsu, 2009; King, 2009; Rubinstein, 2006; Zon & Peterson, 2005).

Zebrafish develop rapidly and almost all organs are developed at 7 days post-fertilization. Their fecundity makes it easy to obtain large numbers of individuals for experimentation, which are relatively inexpensive to maintain. In addition, they can absorb chemical substan‐ ces from their tank water, and their genome has been almost fully sequenced, which makes genetic manipulation more accessible. These characteristics have stimulated the use of ze‐ brafish in medicinal chemistry to assay the effects of different compounds in whole animals (Goldsmith, 2004; Kaufman & White, 2009). Another attractive characteristic of zebrafish is its potential for use in *in vivo* high-throughput screening assays. Consequently, a number of studies which take advantage of this possibility have been reported recently (Kokel et al., 2010; Kokel & Peterson, 2011; Rihel et al., 2010; Zon & Peterson, 2005).

Zebrafish exhibit many social characteristics that can be assimilated to those observed in mammals. They recognize each other by sight and odor (Tebbich et al., 2002) and display an interesting social learning (Reader et al., 2003). This teleost also shows a characteristic ag‐ gressive behavior (Payne, 1998), a pheromone-mediated danger alarm (Suboski, 1988; Sub‐ oski et al., 1990), cognitive and adaptive behaviors such as habituation (Miklosi et al., 1997; Miklosi & Andrew 1999), spatial navigation abilities and Pavlovian conditioning (Hollis, 1999). These features make this species a valuable tool for either the development or the adaptation of behavioral paradigms. Thus, behavioral protocols such as an aquatic version of the T-maze, which is used for studies of discrimination, reinforcement and memory in ro‐ dents, had been used to assess color discrimination in zebrafish (Colwill et al., 2005). Anoth‐ er interesting model is the aquatic version of conditioned place preference (CPP), where the fish can be exposed to different stimuli in two separate compartments and is then allowed to freely explore the apparatus without partition (Darland & Dowling, 2001). A further para‐ digm, the novel tank diving test, has been used by different research groups (Bencan & Lev‐ in 2008; Bencan et al., 2009; Egan et al., 2009; Levin et al., 2007) as a model for anxiety. It is

conceptually similar to the rodent open field test, because it takes advantage of the instinc‐ tive behavior of both zebrafish and rats to seek refuge when exposed to an unfamiliar envi‐ ronment (Levin et al., 2007). In the case of the novel tank diving test, the fish dives to the bottom of the tank and remains there until it presumably feels safe enough to explore the rest of the tank and gradually starts to explore the upper zone (Egan et al., 2009). Similar observations can be made in an open field test for rodents, where initially they spend a lot of time near the walls, which is considered as an indication of an anxious state. The time spent by the zebrafish in the lower or upper part of the tank, as well as erratic movements, have been established as anxiety indices (Egan et al., 2009). It is considered that the zebrafish is anxious when it shows a longer latency to enter the upper part of the tank, or when the time spent at the top is reduced. Conversely, when an anxiolytic drug is administered, animals spend much more time in the upper portion of the tank. Figure 1 illustrates this response by showing the typical traces of motor activity observed for control animals (left) and for ani‐ mals exposed to nicotine (right), which has been reported to have anxiolytic properties in this paradigm (Levin et al., 2007).

this model is needed, particularly if human systems or conditions are the final aims to be

Similarities Between the Binding Sites of Monoamine Oxidase (MAO) from Different Species — Is Zebrafish a Useful

Model for the Discovery of Novel MAO Inhibitors?

http://dx.doi.org/10.5772/35874

409

Monoamine oxidase (monoamine oxygen oxidoreductase (deaminating) (flavin-containing); EC 1.4.3.4; MAO) is a key enzyme in the inactivation of neurotransmitters such as serotonin, dopamine and noradrenaline. In mammals it exists in two isoforms termed MAO-A and MAO-B which have molecular weights of ~60 kDa. Both proteins are outer mitochondrial membrane-bound flavoproteins, with the FAD cofactor covalently bound to the enzyme. MAO-A and MAO-B are encoded by separate genes (Kochersperger et al., 1986; Lan et al., 1989) and the isoforms from the same species show about 70% sequence identity, whereas 85-88% identity is observed between the same isoforms from human and rat (Nagatsu, 2004). Both neurological and psychiatric diseases have been related to MAO dysfunction. Consequently, the search for inhibitors of each isoform has lasted decades. Currently, selec‐ tive inhibitors of MAO-A are used clinically as antidepressants and anxiolytics, while MAO-B inhibitors are used to reduce the progression of Parkinson's disease and of symptoms

In 2002, Binda and colleagues (Binda et al., 2002) published a groundbreaking article show‐ ing the high-resolution structure of human MAO-B in complex with the irreversible inhibi‐ tor pargyline. Subsequent structures of this enzyme (Binda et al., 2003, 2004), as well as that of rat MAO-A (Ma et al., 2004), and more recently human MAO-A (De Colibus et al., 2005; Son et al., 2008), have allowed a detailed comparison of the overall structures of both iso‐ forms, and new insights regarding their active sites (Edmondson et al., 2007*,* 2009; Reyes-Parada et al., 2005). Based on these findings, the substrate/inhibitor binding site of both isozymes can be described as a pocket lined by the isoalloxazine ring of the flavin cofactor and several aliphatic and aromatic residues (in the second part, close ups of this binding site are depicted in Figures 5 and 8). In particular, two conserved tyrosine residues (Y407, Y444 and Y398, Y435 in MAO-A and -B, respectively), whose aromatic rings face each other, are located almost perpendicularly to the isoalloxazine ring defining an "aromatic cage". This conformational arrangement provides a path to guide the substrate amine towards the reac‐ tive positions on the flavin ring and therefore seems to be essential for catalytic activity. In addition, a critical role of residues G215 and I180 of MAO-A (G206 and L171 being the corre‐ sponding residues in MAO-B) in the orientation and stabilization of the substrate/inhibitor binding can be inferred from the X-ray diffraction data. In MAO-B, the substrate/inhibitor

cated closer to the protein surface. It has been demonstrated that the I199 side-chain can act as a "gate" opening or closing the connection between the two cavities by modifying its con‐ formation (Binda et al., 2003). In contrast, the MAO-A binding site consists of a single cavity (De Colibus et al., 2005; Ma et al., 2004). It should be noted that, although residues lining the binding site of human and rat MAO-A are identical, the human MAO-A cavity is larger

, termed the "substrate cavity") which can be distinguished,

, termed the "entrance cavity") lo‐

*2.1.1. Monoamine oxidase: general characteristics and the impact of crystal structures on the*

addressed.

*understanding of enzyme function and inhibition*

associated with Alzheimer's disease (Youdim et al., 2006).

in some cases, from another hydrophobic pocket (~300 Å3

binding site is a cavity (~400 Å3

Based on these findings, the potential of zebrafish for neurobehavioral studies is increasing‐ ly recognized (Bencan & Levin, 2008; Eddins et al., 2010). Thus, this animal has been used as a model in studies of memory (Levin & Chen, 2006), anxiety (Bencan et al., 2009; Levin et al., 2007), reinforcement properties of drugs of abuse (Ninkovic & Bally-Cuif, 2006), neuropro‐ tection of dopaminergic neurons (McKinley et al., 2005), and movement disorders (Flinn et al., 2008).

**Figure 1.** Representative traces of characteristic behavior of control-saline- (left) and nicotine- (right) treated zebra fish. Traces were recorded during 5 min in a glass trapezoidal test tank (22.9 cm long at the bottom, 27.9 cm long at the top, 15.2 cm high, 6.4 cm wide), filled with 1.5 L of artificial sea water. Nicotine was administered 5 min before the test. All other experimental conditions were as previously published (Levin et al., 2007).

A final word of caution should be said regarding the apparent usefulness of zebrafish as a research tool. One critical aspect to be considered when using animal models to understand a specific behavior is its validity. Mammals such as rats and mice have been widely used as models to study several functions since, among other characteristics, many brain regions and their neurotransmitter systems are well characterized. Thus, even though genome and the genetic pathways controlling signal transduction and development appear to be highly conserved between zebrafish and humans (Postlethwait et al., 2000), further validation of this model is needed, particularly if human systems or conditions are the final aims to be addressed.

### *2.1.1. Monoamine oxidase: general characteristics and the impact of crystal structures on the understanding of enzyme function and inhibition*

conceptually similar to the rodent open field test, because it takes advantage of the instinc‐ tive behavior of both zebrafish and rats to seek refuge when exposed to an unfamiliar envi‐ ronment (Levin et al., 2007). In the case of the novel tank diving test, the fish dives to the bottom of the tank and remains there until it presumably feels safe enough to explore the rest of the tank and gradually starts to explore the upper zone (Egan et al., 2009). Similar observations can be made in an open field test for rodents, where initially they spend a lot of time near the walls, which is considered as an indication of an anxious state. The time spent by the zebrafish in the lower or upper part of the tank, as well as erratic movements, have been established as anxiety indices (Egan et al., 2009). It is considered that the zebrafish is anxious when it shows a longer latency to enter the upper part of the tank, or when the time spent at the top is reduced. Conversely, when an anxiolytic drug is administered, animals spend much more time in the upper portion of the tank. Figure 1 illustrates this response by showing the typical traces of motor activity observed for control animals (left) and for ani‐ mals exposed to nicotine (right), which has been reported to have anxiolytic properties in

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

Based on these findings, the potential of zebrafish for neurobehavioral studies is increasing‐ ly recognized (Bencan & Levin, 2008; Eddins et al., 2010). Thus, this animal has been used as a model in studies of memory (Levin & Chen, 2006), anxiety (Bencan et al., 2009; Levin et al., 2007), reinforcement properties of drugs of abuse (Ninkovic & Bally-Cuif, 2006), neuropro‐ tection of dopaminergic neurons (McKinley et al., 2005), and movement disorders (Flinn et

**Figure 1.** Representative traces of characteristic behavior of control-saline- (left) and nicotine- (right) treated zebra fish. Traces were recorded during 5 min in a glass trapezoidal test tank (22.9 cm long at the bottom, 27.9 cm long at the top, 15.2 cm high, 6.4 cm wide), filled with 1.5 L of artificial sea water. Nicotine was administered 5 min before the

A final word of caution should be said regarding the apparent usefulness of zebrafish as a research tool. One critical aspect to be considered when using animal models to understand a specific behavior is its validity. Mammals such as rats and mice have been widely used as models to study several functions since, among other characteristics, many brain regions and their neurotransmitter systems are well characterized. Thus, even though genome and the genetic pathways controlling signal transduction and development appear to be highly conserved between zebrafish and humans (Postlethwait et al., 2000), further validation of

test. All other experimental conditions were as previously published (Levin et al., 2007).

this paradigm (Levin et al., 2007).

al., 2008).

Applications

408

Monoamine oxidase (monoamine oxygen oxidoreductase (deaminating) (flavin-containing); EC 1.4.3.4; MAO) is a key enzyme in the inactivation of neurotransmitters such as serotonin, dopamine and noradrenaline. In mammals it exists in two isoforms termed MAO-A and MAO-B which have molecular weights of ~60 kDa. Both proteins are outer mitochondrial membrane-bound flavoproteins, with the FAD cofactor covalently bound to the enzyme. MAO-A and MAO-B are encoded by separate genes (Kochersperger et al., 1986; Lan et al., 1989) and the isoforms from the same species show about 70% sequence identity, whereas 85-88% identity is observed between the same isoforms from human and rat (Nagatsu, 2004). Both neurological and psychiatric diseases have been related to MAO dysfunction. Consequently, the search for inhibitors of each isoform has lasted decades. Currently, selec‐ tive inhibitors of MAO-A are used clinically as antidepressants and anxiolytics, while MAO-B inhibitors are used to reduce the progression of Parkinson's disease and of symptoms associated with Alzheimer's disease (Youdim et al., 2006).

In 2002, Binda and colleagues (Binda et al., 2002) published a groundbreaking article show‐ ing the high-resolution structure of human MAO-B in complex with the irreversible inhibi‐ tor pargyline. Subsequent structures of this enzyme (Binda et al., 2003, 2004), as well as that of rat MAO-A (Ma et al., 2004), and more recently human MAO-A (De Colibus et al., 2005; Son et al., 2008), have allowed a detailed comparison of the overall structures of both iso‐ forms, and new insights regarding their active sites (Edmondson et al., 2007*,* 2009; Reyes-Parada et al., 2005). Based on these findings, the substrate/inhibitor binding site of both isozymes can be described as a pocket lined by the isoalloxazine ring of the flavin cofactor and several aliphatic and aromatic residues (in the second part, close ups of this binding site are depicted in Figures 5 and 8). In particular, two conserved tyrosine residues (Y407, Y444 and Y398, Y435 in MAO-A and -B, respectively), whose aromatic rings face each other, are located almost perpendicularly to the isoalloxazine ring defining an "aromatic cage". This conformational arrangement provides a path to guide the substrate amine towards the reac‐ tive positions on the flavin ring and therefore seems to be essential for catalytic activity. In addition, a critical role of residues G215 and I180 of MAO-A (G206 and L171 being the corre‐ sponding residues in MAO-B) in the orientation and stabilization of the substrate/inhibitor binding can be inferred from the X-ray diffraction data. In MAO-B, the substrate/inhibitor binding site is a cavity (~400 Å3 , termed the "substrate cavity") which can be distinguished, in some cases, from another hydrophobic pocket (~300 Å3 , termed the "entrance cavity") lo‐ cated closer to the protein surface. It has been demonstrated that the I199 side-chain can act as a "gate" opening or closing the connection between the two cavities by modifying its con‐ formation (Binda et al., 2003). In contrast, the MAO-A binding site consists of a single cavity (De Colibus et al., 2005; Ma et al., 2004). It should be noted that, although residues lining the binding site of human and rat MAO-A are identical, the human MAO-A cavity is larger

(~550 Å3 ) than that in rat MAO-A (~450 Å3 ). Remarkably, an exchanged location of aromatic and aliphatic nonconserved residues in the active sites of MAO-A and MAO-B (F208/I199 and I335/Y326, respectively) has been implicated in the affinity and selective recognition of substrates and inhibitors, and provides a molecular basis for the development of specific re‐ versible inhibitors of each isoform (Edmondson et al., 2009).

*2.1.2. Comparative functional and structural information about zebrafish MAO and its mammalian*

Similarities Between the Binding Sites of Monoamine Oxidase (MAO) from Different Species — Is Zebrafish a Useful

Model for the Discovery of Novel MAO Inhibitors?

http://dx.doi.org/10.5772/35874

411

Unlike mammals, zebrafish have only one MAO gene (Anichtchik et al., 2006; Setini et al., 2005). This gene is located in chromosome 9 and exhibits an identical intron-exon organiza‐ tion as compared to mammals, which suggests a common ancestral gene (Anichtchik et al., 2006; Panula et al., 2010). Sequencing studies have shown that zebrafish MAO (zMAO) con‐ tains 522 amino acids and has a molecular weight of about 59 kDa (Setini et al., 2005), which is very similar to that found in mammalian MAO-A and MAO-B. zMAO displays about 70% identity with human MAO-A or -B, and its predicted secondary structure indicates that the flavin-binding-, the substrate- and the membrane-binding- domains, which are typical in other MAOs, should also be present in the fish enzyme. Indeed, a recent study (Arslan & Edmondson, 2010) has demonstrated that (like the mammalian isoforms), zMAO is also a mitochondrial enzyme, presumably bound to the outer membrane, and that the flavin cofac‐ tor is covalently bound to the protein via an 8α-thioether linkage likely established with C406. Beyond its overall identity, the amino acid sequence of the presumed zMAO binding domain shows ~67% and ~83% identity with the corresponding binding sites of human MAO-B and MAO-A respectively (Panula et al., 2010). Interestingly, some residues that have been shown to be critical for inhibitor and substrate selectivity in human MAOs such as the pairs F208/I335 (in MAO-A) and I199/Y326 (in MAO-B), are identical or conservative‐

Regarding functional studies, recent data obtained using *para*-substituted benzylamine ana‐ logs as substrates suggest that, as in mammalian MAOs, α-C-H bond cleavage is the ratelimiting step in zMAO catalysis (Aldeco et al., 2011). Furthermore, a variety of substrates and inhibitors have been tested against zMAO. Preferential substrates of both MAO-A (e.g. serotonin) and MAO-B (e.g. phenethylamine, benzylamine, MPTP) as well as non-selective substrates such as tyramine, dopamine or kynuramine, have been shown to be deaminated, although with different catalytic efficiency, by zMAO (Aldeco et al., 2011; Anichtchik et al., 2006; Arslan & Edmondson, 2010; Sallinen et al., 2009; Setini et al., 2005). In addition, irre‐ versible selective inhibitors such as clorgyline (MAO-A) or deprenyl (MAO-B) exhibit simi‐ lar inhibitory profiles toward zMAO (Anichtchik et al., 2006; Arslan & Edmondson, 2010; Setini et al., 2005). Interestingly, the *in vivo* administration of deprenyl to zebrafish increases serotonin levels about 10-fold while levels of dopamine remain unchanged (Sallinen et al., 2009). These data indicate that zMAO is essential for serotonin metabolism in zebrafish, but also underline the distinctive character of this enzyme since in rodents dopamine concentra‐ tions are increased after deprenyl treatment, whereas serotonin levels remain unchanged. Structurally diverse reversible MAO inhibitors such as harmane, tetrindole, methylene blue, amphetamine, 8-(3-chlorostyryl)-caffeine, 1,4-diphenyl-1,3-butadiene, farnesol, safinamide or zonisamide display a wide range of inhibitory potencies, from nM to μM to no effect, against zMAO (Aldeco et al., 2011; Binda et al., 2011). Remarkably, methylene blue is the

Based on sequence similarity, substrate preference and inhibitor sensitivity, it has been con‐ sistently suggested that the functional properties of zMAO resemble more strongly those of

value of 4 nM.

ly replaced in zMAO (F200/L327) as compared with MAO-A.

most potent zMAO inhibitor tested thus far, exhibiting a *K*<sup>i</sup>

*counterparts*

The availability of the aforementioned crystal structures has made an enormous impact on our knowledge about the function and regulation of the enzyme and has also allowed a quicker pace in the rational design of novel MAO inhibitors. Different theoretical ap‐ proaches and computational methods have been used since, to explore **how**, **where** and **why** some interactions are central in MAO-ligand complexes. For instance, quantum me‐ chanics calculations have been used to obtain insights about the mechanism by which amines are oxidized by MAO (Erdem & Büyükmenekşe, 2011), whereas molecular dynamics simulations have been recently employed to study specific interactions involved in the ac‐ cess of reversible MAO inhibitors to their binding site (Allen & Bevan 2011). In addition, a number of studies describing potent and selective inhibitors have been reported during the last decade and in most of them molecular simulation approaches have been used to ration‐ alize and/or to predict the functional interactions between the proteins and their inhibitors. Figure 2 illustrates this situation by showing the progression of published articles about MAO in which computational methodologies were used.

It should be pointed out however, that crystal structures only provide a snapshot of one of the many conformations available to proteins. Therefore theoretical (and experimental) ap‐ proaches, adequately considering dynamic aspects, will grow in importance in order to bet‐ ter understand the physiological functioning of these enzymes.

**Figure 2.** Progression of research articles involving docking studies on MAO before and after (2002) the first threedimensional structure of MAO was deposited in the Protein Data Bank. Data from PubMed. "MAO" and "docking" were used as keywords.

Similarities Between the Binding Sites of Monoamine Oxidase (MAO) from Different Species — Is Zebrafish a Useful Model for the Discovery of Novel MAO Inhibitors? http://dx.doi.org/10.5772/35874 411

#### *2.1.2. Comparative functional and structural information about zebrafish MAO and its mammalian counterparts*

(~550 Å3

Applications

410

were used as keywords.

) than that in rat MAO-A (~450 Å3

versible inhibitors of each isoform (Edmondson et al., 2009).

MAO in which computational methodologies were used.

ter understand the physiological functioning of these enzymes.

and aliphatic nonconserved residues in the active sites of MAO-A and MAO-B (F208/I199 and I335/Y326, respectively) has been implicated in the affinity and selective recognition of substrates and inhibitors, and provides a molecular basis for the development of specific re‐

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

The availability of the aforementioned crystal structures has made an enormous impact on our knowledge about the function and regulation of the enzyme and has also allowed a quicker pace in the rational design of novel MAO inhibitors. Different theoretical ap‐ proaches and computational methods have been used since, to explore **how**, **where** and **why** some interactions are central in MAO-ligand complexes. For instance, quantum me‐ chanics calculations have been used to obtain insights about the mechanism by which amines are oxidized by MAO (Erdem & Büyükmenekşe, 2011), whereas molecular dynamics simulations have been recently employed to study specific interactions involved in the ac‐ cess of reversible MAO inhibitors to their binding site (Allen & Bevan 2011). In addition, a number of studies describing potent and selective inhibitors have been reported during the last decade and in most of them molecular simulation approaches have been used to ration‐ alize and/or to predict the functional interactions between the proteins and their inhibitors. Figure 2 illustrates this situation by showing the progression of published articles about

It should be pointed out however, that crystal structures only provide a snapshot of one of the many conformations available to proteins. Therefore theoretical (and experimental) ap‐ proaches, adequately considering dynamic aspects, will grow in importance in order to bet‐

**Figure 2.** Progression of research articles involving docking studies on MAO before and after (2002) the first threedimensional structure of MAO was deposited in the Protein Data Bank. Data from PubMed. "MAO" and "docking"

). Remarkably, an exchanged location of aromatic

Unlike mammals, zebrafish have only one MAO gene (Anichtchik et al., 2006; Setini et al., 2005). This gene is located in chromosome 9 and exhibits an identical intron-exon organiza‐ tion as compared to mammals, which suggests a common ancestral gene (Anichtchik et al., 2006; Panula et al., 2010). Sequencing studies have shown that zebrafish MAO (zMAO) con‐ tains 522 amino acids and has a molecular weight of about 59 kDa (Setini et al., 2005), which is very similar to that found in mammalian MAO-A and MAO-B. zMAO displays about 70% identity with human MAO-A or -B, and its predicted secondary structure indicates that the flavin-binding-, the substrate- and the membrane-binding- domains, which are typical in other MAOs, should also be present in the fish enzyme. Indeed, a recent study (Arslan & Edmondson, 2010) has demonstrated that (like the mammalian isoforms), zMAO is also a mitochondrial enzyme, presumably bound to the outer membrane, and that the flavin cofac‐ tor is covalently bound to the protein via an 8α-thioether linkage likely established with C406. Beyond its overall identity, the amino acid sequence of the presumed zMAO binding domain shows ~67% and ~83% identity with the corresponding binding sites of human MAO-B and MAO-A respectively (Panula et al., 2010). Interestingly, some residues that have been shown to be critical for inhibitor and substrate selectivity in human MAOs such as the pairs F208/I335 (in MAO-A) and I199/Y326 (in MAO-B), are identical or conservative‐ ly replaced in zMAO (F200/L327) as compared with MAO-A.

Regarding functional studies, recent data obtained using *para*-substituted benzylamine ana‐ logs as substrates suggest that, as in mammalian MAOs, α-C-H bond cleavage is the ratelimiting step in zMAO catalysis (Aldeco et al., 2011). Furthermore, a variety of substrates and inhibitors have been tested against zMAO. Preferential substrates of both MAO-A (e.g. serotonin) and MAO-B (e.g. phenethylamine, benzylamine, MPTP) as well as non-selective substrates such as tyramine, dopamine or kynuramine, have been shown to be deaminated, although with different catalytic efficiency, by zMAO (Aldeco et al., 2011; Anichtchik et al., 2006; Arslan & Edmondson, 2010; Sallinen et al., 2009; Setini et al., 2005). In addition, irre‐ versible selective inhibitors such as clorgyline (MAO-A) or deprenyl (MAO-B) exhibit simi‐ lar inhibitory profiles toward zMAO (Anichtchik et al., 2006; Arslan & Edmondson, 2010; Setini et al., 2005). Interestingly, the *in vivo* administration of deprenyl to zebrafish increases serotonin levels about 10-fold while levels of dopamine remain unchanged (Sallinen et al., 2009). These data indicate that zMAO is essential for serotonin metabolism in zebrafish, but also underline the distinctive character of this enzyme since in rodents dopamine concentra‐ tions are increased after deprenyl treatment, whereas serotonin levels remain unchanged. Structurally diverse reversible MAO inhibitors such as harmane, tetrindole, methylene blue, amphetamine, 8-(3-chlorostyryl)-caffeine, 1,4-diphenyl-1,3-butadiene, farnesol, safinamide or zonisamide display a wide range of inhibitory potencies, from nM to μM to no effect, against zMAO (Aldeco et al., 2011; Binda et al., 2011). Remarkably, methylene blue is the most potent zMAO inhibitor tested thus far, exhibiting a *K*<sup>i</sup> value of 4 nM.

Based on sequence similarity, substrate preference and inhibitor sensitivity, it has been con‐ sistently suggested that the functional properties of zMAO resemble more strongly those of

MAO-A than those of MAO-B. Nevertheless, virtually all articles published so far recognize that, although some overlapping properties can be detected, zMAO also shows characteris‐ tics of its own that distinguish it from its mammalian counterparts.

sequence analysis (Xie et al., 2009), detection of evolutionary relationships (Xie & Bourne, 2008) and polypharmacology predictions (Milleti & Vulpetti, 2010; Pérez-Nueno & Ritchie, 2011). The main step before finding similarity between two or more binding sites is their characterization. Several methodologies have been proposed with this purpose: geometrics approaches, which mainly analyze cavities through the exploration of the solvent-accessible protein surface (Weisel et al., 2007); energetics approaches, which use van der Waals and electrostatic energies to define cavities (Laurie & Jackson, 2005); structure and sequence comparison approaches, which use the information of known binding sites to compare and define unknown cavities through the analysis of sequence and structural similarity (Brylin‐ ski & Skolnick, 2009); and approaches involving the dynamics of protein structures, which use dynamics simulations to include the natural flexibility of proteins and possible allosteric modifications of binding sites (Landon et al., 2008). Although the determination of similari‐ ties between binding sites could seem a simple mathematical method, several approaches have been developed using different characteristics. For example, the Isocleft algorithm measures the similarity by initially defining a cleft in any protein to be compared. These clefts are determined by a set of overlapping spheres that are represented by the van der Waals radii of atoms in the binding sites. Finally each cleft is viewed like a graph and the similarity is measured by finding the largest common subgraph (Najmanovich et al., 2008). The SitesBase algorithm uses a triangular geometric determination of binding sites establish‐ ing the cutoff at 5 Å. Similarity is measured by an atom–atom score which finds the largest possible matching constellation (similar atom types with a similar spatial orientation) (Gold & Jackson, 2006). The ProFunc server uses sequence and structural information to find simi‐ larities between binding sites. This process includes a phylogenetic component that is used for the identification of homologous proteins (Laskowski et al., 2005). The Sumo algorithm flags each functional group as a node in a graph. Then the similarity is measured through a strategy that does not necessarily find the maximal common subgraph between a pair of binding sites (Jambon et al., 2003). The FLAP algorithm utilizes GRID methodology to calcu‐ late the energy of interaction between a molecular probe and the binding sites. These inter‐ actions, which include van der Waals and electrostatic terms, are then compared through a geometric approach (Baroni et al., 2007). In another recently developed algorithm (Hoff‐ mann et al., 2010) the binding sites are represented as a set of atoms in the 3D space descri‐ bed by 3D vectors. Initially the algorithm calculates the similarity between two binding sites comparing vectors that only consider the atom coordinates, although different additional parameters such as atom type and charges could be included in the algorithm. The Pocket‐ Match algorithm involves three basic steps: a) each binding site is represented as a sort list of distances between three selected points in every amino acid present at one specific dis‐ tance from the ligand, b) the two sets of sorted distances are aligned and c) finally the simi‐

Similarities Between the Binding Sites of Monoamine Oxidase (MAO) from Different Species — Is Zebrafish a Useful

Model for the Discovery of Novel MAO Inhibitors?

http://dx.doi.org/10.5772/35874

413

larity percentage is calculated (Yeturu & Chandra, 2008).

Although most algorithms used to measure the similarities between binding sites have shown high performance when the comparison involves related proteins, doubtful results are obtained when the proteins are not related. In these cases it is very important to select the best algorithm taking into account some critical issues: a ligand may change its orienta‐ tion in different binding sites; some protein-ligand conformations may have a favorable

### **2.2. Recent developments in computational methods to evaluate similarities between ligand-binding sites**

The concept of protein binding-site similarity and the development of methods to evaluate it are receiving much attention. This is viewed as a step forward in protein classification, as compared with classical sequence-based approaches, since it should allow proteins with low sequence similarity but high similarity at their binding sites to be related (Milletti & Vulpet‐ ti, 2010). On the contrary, as will be analyzed below, this approach can also detect subtle dif‐ ferences between highly homologous proteins, and therefore be useful to determine the suitability of non-human proteins as models for drug design aimed to the treatment of hu‐ man conditions.

One of the newest applications of the study of binding site similarities is polypharmacology. Thus, the classical idea that selective drugs acting on a single target related to one disease will have maximal efficacy has been challenged by increasing evidence showing that most clinically effective drugs bind to several targets, even if these targets are not originally relat‐ ed to the disease (Keiser et al., 2009; Schrattenholz & Soskić 2008). Even though this pharma‐ cological promiscuity may be seen as a negative property, primarily related with the incidence of side effects, recent observations increasingly indicate that multitarget com‐ pounds might have better profiles regarding both efficacy and side effects, since they would be acting on a pharmacological network, where several nodes underlie the physiopathology of the disease (Apsel et al., 2008; Hopkins 2008). Thus, the concept of polypharmacology has motivated several groups to find new drug-target associations, based on the idea that a giv‐ en compound can interact simultaneously with two or more relevant targets if they have similar binding sites. It should be stressed that these associations are pursued considering that two proteins could share a ligand even if they are structurally or functionally very dif‐ ferent (Kahraman et al., 2007).

One aspect that has critically fueled this field is the increasing availability of 3D protein structures in public databases (almost 75.000), which allows us to explore the complexity of protein-ligand interactions. This exploration has yielded important insights in order to ob‐ tain a good characterization of the binding sites and has confirmed the notion that proteinligand binding depends not only on shape complementarity but also on complementary physicochemical features (Henrich et al., 2010).

Several algorithms have been developed to compare binding sites of different proteins. In most of them, two main steps are present: the creation of a database that requires the calcu‐ lation of fingerprints describing each binding site and a pocket screening that requires mul‐ tiple similarity alignments between the query pocket and the database. These applications are used as a strategy to assess specific issues, such as off-target identification for drug repurposing (Cleves & Jain, 2006; Keiser et al., 2009; Moriaud et al., 2011), functional classifica‐ tion of unknown proteins (Kinnings & Jackson, 2009; Russell et al., 1998), drug discovery by MAO-A than those of MAO-B. Nevertheless, virtually all articles published so far recognize that, although some overlapping properties can be detected, zMAO also shows characteris‐

The concept of protein binding-site similarity and the development of methods to evaluate it are receiving much attention. This is viewed as a step forward in protein classification, as compared with classical sequence-based approaches, since it should allow proteins with low sequence similarity but high similarity at their binding sites to be related (Milletti & Vulpet‐ ti, 2010). On the contrary, as will be analyzed below, this approach can also detect subtle dif‐ ferences between highly homologous proteins, and therefore be useful to determine the suitability of non-human proteins as models for drug design aimed to the treatment of hu‐

One of the newest applications of the study of binding site similarities is polypharmacology. Thus, the classical idea that selective drugs acting on a single target related to one disease will have maximal efficacy has been challenged by increasing evidence showing that most clinically effective drugs bind to several targets, even if these targets are not originally relat‐ ed to the disease (Keiser et al., 2009; Schrattenholz & Soskić 2008). Even though this pharma‐ cological promiscuity may be seen as a negative property, primarily related with the incidence of side effects, recent observations increasingly indicate that multitarget com‐ pounds might have better profiles regarding both efficacy and side effects, since they would be acting on a pharmacological network, where several nodes underlie the physiopathology of the disease (Apsel et al., 2008; Hopkins 2008). Thus, the concept of polypharmacology has motivated several groups to find new drug-target associations, based on the idea that a giv‐ en compound can interact simultaneously with two or more relevant targets if they have similar binding sites. It should be stressed that these associations are pursued considering that two proteins could share a ligand even if they are structurally or functionally very dif‐

One aspect that has critically fueled this field is the increasing availability of 3D protein structures in public databases (almost 75.000), which allows us to explore the complexity of protein-ligand interactions. This exploration has yielded important insights in order to ob‐ tain a good characterization of the binding sites and has confirmed the notion that proteinligand binding depends not only on shape complementarity but also on complementary

Several algorithms have been developed to compare binding sites of different proteins. In most of them, two main steps are present: the creation of a database that requires the calcu‐ lation of fingerprints describing each binding site and a pocket screening that requires mul‐ tiple similarity alignments between the query pocket and the database. These applications are used as a strategy to assess specific issues, such as off-target identification for drug repurposing (Cleves & Jain, 2006; Keiser et al., 2009; Moriaud et al., 2011), functional classifica‐ tion of unknown proteins (Kinnings & Jackson, 2009; Russell et al., 1998), drug discovery by

**2.2. Recent developments in computational methods to evaluate similarities between**

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

tics of its own that distinguish it from its mammalian counterparts.

**ligand-binding sites**

Applications

412

man conditions.

ferent (Kahraman et al., 2007).

physicochemical features (Henrich et al., 2010).

sequence analysis (Xie et al., 2009), detection of evolutionary relationships (Xie & Bourne, 2008) and polypharmacology predictions (Milleti & Vulpetti, 2010; Pérez-Nueno & Ritchie, 2011). The main step before finding similarity between two or more binding sites is their characterization. Several methodologies have been proposed with this purpose: geometrics approaches, which mainly analyze cavities through the exploration of the solvent-accessible protein surface (Weisel et al., 2007); energetics approaches, which use van der Waals and electrostatic energies to define cavities (Laurie & Jackson, 2005); structure and sequence comparison approaches, which use the information of known binding sites to compare and define unknown cavities through the analysis of sequence and structural similarity (Brylin‐ ski & Skolnick, 2009); and approaches involving the dynamics of protein structures, which use dynamics simulations to include the natural flexibility of proteins and possible allosteric modifications of binding sites (Landon et al., 2008). Although the determination of similari‐ ties between binding sites could seem a simple mathematical method, several approaches have been developed using different characteristics. For example, the Isocleft algorithm measures the similarity by initially defining a cleft in any protein to be compared. These clefts are determined by a set of overlapping spheres that are represented by the van der Waals radii of atoms in the binding sites. Finally each cleft is viewed like a graph and the similarity is measured by finding the largest common subgraph (Najmanovich et al., 2008). The SitesBase algorithm uses a triangular geometric determination of binding sites establish‐ ing the cutoff at 5 Å. Similarity is measured by an atom–atom score which finds the largest possible matching constellation (similar atom types with a similar spatial orientation) (Gold & Jackson, 2006). The ProFunc server uses sequence and structural information to find simi‐ larities between binding sites. This process includes a phylogenetic component that is used for the identification of homologous proteins (Laskowski et al., 2005). The Sumo algorithm flags each functional group as a node in a graph. Then the similarity is measured through a strategy that does not necessarily find the maximal common subgraph between a pair of binding sites (Jambon et al., 2003). The FLAP algorithm utilizes GRID methodology to calcu‐ late the energy of interaction between a molecular probe and the binding sites. These inter‐ actions, which include van der Waals and electrostatic terms, are then compared through a geometric approach (Baroni et al., 2007). In another recently developed algorithm (Hoff‐ mann et al., 2010) the binding sites are represented as a set of atoms in the 3D space descri‐ bed by 3D vectors. Initially the algorithm calculates the similarity between two binding sites comparing vectors that only consider the atom coordinates, although different additional parameters such as atom type and charges could be included in the algorithm. The Pocket‐ Match algorithm involves three basic steps: a) each binding site is represented as a sort list of distances between three selected points in every amino acid present at one specific dis‐ tance from the ligand, b) the two sets of sorted distances are aligned and c) finally the simi‐ larity percentage is calculated (Yeturu & Chandra, 2008).

Although most algorithms used to measure the similarities between binding sites have shown high performance when the comparison involves related proteins, doubtful results are obtained when the proteins are not related. In these cases it is very important to select the best algorithm taking into account some critical issues: a ligand may change its orienta‐ tion in different binding sites; some protein-ligand conformations may have a favorable binding energy, but natural allosteric regulations (not always considered) might not favor such conformations; protein structures from databases could have been determined in dif‐ ferent conformational states (active, inactive, closed, open, etc.); finally, it is also very impor‐ tant to consider the solvent and ion concentrations in every system.

Beyond these considerations, the continuous increase in both the number of protein struc‐ tures and computational power, augurs the development of ever more accurate similarity searching tools, which likely will allow not only better results in virtual screening programs but also a novel view on the evolution of structure and function of proteins.
