Modeling the 3D structure of GPCRs from sequence

Modeling the 3DStructure of GPCRsfrom Sequence

Sharon Shacham, Maya Topf,� Noa Avisar, Fabian Glaser, Yael Marantz, Shay Bar-Haim,Silvia Noiman, Zvi Naor,y Oren M. Becker

Bio IT (Bio Information Technologies) Ltd., 3 Hayetzira St., Ramat Gan, Israel

!

Abstract: G-protein-coupled receptors (GPCRs) are a large and functionally diverse protein

superfamily, which form a seven transmembrane (TM) helices bundle with alternating extra-

cellular and intracellular loops. GPCRs are considered to be one of the most important groups of

drug targets because they are involved in a broad range of body functions and processes and are

related to major diseases. In this paper we present a new technology, named PREDICT, for

modeling the 3D structure of any GPCR from its amino acid sequence. This approach takes into

account both internal protein properties (i.e., the amino acid sequence) and the properties of the

membrane environment. Unlike competing approaches, the new technology does not rely on the

single known structure of rhodopsin, and is thus capable of predicting novel GPCR conformations.

We demonstrate the capabilities of PREDICT in reproducing the known experimental structure of

rhodopsin. In principle, PREDICT-generated models offer new opportunities for structure-based

drug discovery towards GPCR targets. ß 2001 John Wiley & Sons, Inc. Med Res Rev, 21, No. 5, 472±483,

2001

Key words: GPCR; modeling; structure based drug discovery

1 . I N T R O D U C T I O N

G-protein-coupled receptors (GPCRs) are membrane embedded proteins, involved in communica-

tion between the cell and its environment by passing chemical signals across the cell membrane.

These proteins form a large and functionally diverse superfamily which consists of a single

polypeptide chain of variable length that traverses the lipid bilayer seven times, forming

characteristic transmembrane helices (TM) and alternating extracellular (ECL) and intracellular

(ICL) loops.1±2 Many and diverse ligands (e.g., ions, biogenic amines, nucleosides, lipids, peptides,

proteins, and even light) use this class of receptors to convert external and internal stimuli into

intracellular responses.

GPCRs activate one or more members of the guanine-nucleotide-binding signal transducing

proteins (G-proteins) that carry the information received by the receptor to cellular effectors such as

472

*Currentaddress:Departmentof Chemistry,NewChemistry Laboratory, South Parks Road,Oxford,OX13QT,United Kingdom.yCurrentaddress: Departmentof Biochemistry,Wise Facultyof Life Sciences,Tel Aviv University,Ramat Aviv,Tel Aviv 69978, Israel.Correspondence to: Oren M. Becker, Bio IT (Bio InformationTechnologies) Ltd., S.A.P. Building (11th floor), 3 Hayetzira St., RamatGan 52521, Israel.E-mail: [email protected]

Medical Research Reviews, Vol. 21, No. 5, 472^483, 2001ß 2001 John Wiley & Sons, Inc.

enzymes and ion channels.3±5 These effectors in¯uence levels of second messengers that regulate a

wide variety of cellular processes including cell growth and differentiation.6±7 Each G-protein

consists of three subunits, commonly denoted as a, b, and g. Sixteen distinct mammalian G-protein

a-subunits have been molecularly cloned. Similarly, 11 G-protein b-subunits and ®ve G-protein g-

subunits have been identi®ed. Thus, GPCRs are likely to represent the most diverse signal

transduction systems in eukaryotic cells. Furthermore, GPCRs may also couple to other proteins, for

example, those containing PDZ domains.8 The regulation of receptor-G-protein signal selectivity

and speci®city is highly complex and involves the activation of a network of mechanisms and

pathways that eventually lead to biological responses.

Based on nucleotide and amino acid sequence similarity, the superfamily of GPCRs can be

subdivided into six families of receptors whose protein sequences share signi®cant similarity.9

The main family (family A) is of the rhodopsin/adrenergic receptors, which consists of the majority

of G-protein-coupled receptors identi®ed to date. This family is the best studied from both the

structural and functional points of view.1,9±11 Receptors belonging to this family are activated by a

variety of stimuli including photons, odorants, hormones, and neurotransmitters with molecular

structures ranging from small biogenic amines (e.g., catecholamines and histamine) to peptides

(e.g., gonadotropin-releasing hormone (GnRH), thyrotropin-releasing hormone (TRH)), and

complex glycoproteins, such as luteinizing hormone (LH), follicle-stimulating hormone (FSH),

and thyroid-stimulating hormone (TSH).12±15 The other subfamilies are the secretin/vasointestinal

peptide (VIP) family (family B), which binds several neuropeptides and peptide hormones, the

metabotropic glutamate receptor family (family C), which comprises at least six closely related

subtypes of receptors that bind glutamate, the major excitatory neurotransmitter in the central

nervous system. Three additional GPCR families are the fungal pheromone P and a-factor (STE2/

MAM2) family (family D), the fungal pheromone A and M-factor (STE3/MAP3) receptors (family

E), and the cyclic adenosine monophosphate (cAMP) receptors of Dictyostelium (family F).

A number of new putative GPCR families have been discovered with varying degrees of similarity

to the established families, including frizzled, smoothened, basal vomeronasal receptors, and bride

of sevenless (BOSS) of Drosophila and mammals, latrophilin, several plant GPCRs, another yeast

GPCR, GPR1, as well as other mammalian sequences, p40 and pm1.8,16±19 Figure 1 depicts the

distribution of the 508 human GPCRs identi®ed so far, grouped according to the type of ligand.

Approximately half of these GPCRs are still orphans, indicating that their function is yet unknown.

It is expected that by the completion of the analysis of the human genome project even more

GPCRs, that are potential new drug targets, will be discovered.

The mechanisms controlling ligand binding, activation, and signal transduction of the GPCRs/

G-protein system as well as the mechanisms required for de®ning the speci®city of receptor-G

protein-effector interaction and the ef®ciency and regulation of signal transduction are highly

complex and multifactorial. Knowledge and mapping of the structural determinants and require-

ments for optimal receptor function are of paramount importance for understanding the molecular

basis of ligand action and receptor function in normal and abnormal conditions. Deciphering

structure-function relationships in GPCRs will promote computer-aided drug discovery by

studying the binding mode of known ligands into their receptor binding-sites and identify the

pharmacophores involved.

2 . D R U G D I S C O V E R Y A P P R O A C H E S

GPCRs are considered as one of the most important groups of drug targets. This is because GPCRs

are involved in a very wide range of body functions and processes, including cardiovascular,

nervous, endocrine, and immune systems; and are related to major diseases such as hypertension,

cardiac dysfunction, depression, eating disorders (obesity), certain types of cancer, pain,

MODELIN G-PROTEIN-COUPLED RECEPTORS * 473

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schizophrenia, and viral infection. Thus, while GPCRs are only a small subset of the human genome

(2%±3% of the human genome) they constitute about 50% of the drug targets that are of interest to

the pharmaceutical industry.

Conventional drug discovery often involves combinatorial chemistry techniques to create (often

randomly) very large numbers of molecules that are subsequently screened for bioactivity using

high throughput screening tools. These methods suffer from drawbacks such as the consumption of

signi®cant resources for creating hundreds of thousands of molecules, only a small fraction of which

are active, and the fact that even very large molecular libraries can explore only a small portion of

the chemical of drug-like molecules.

Structure-based drug discovery is an alternative approach to conventional drug discovery,

which relies on the fact that interactions of molecules within the human body take place in three-

dimensions. Drug molecules compete with natural ligands by inserting themselves into the

functional site of the target protein and inducing (agonists) or inhibiting (antagonists) its activity.

The af®nity of these drugs to their respective target proteins is due to structural and chemical

complementarily, and can be explored by computational methods. This allows for using relatively

cheap and ef®cient computational screening technology instead of the expensive and low yield

experimental high-throughput screening (HTS) techniques. Furthermore, structure based drug

discovery can identify possible binding modes for ligands within the receptor cavity, typically

through the identi®cation of pharmacophoric centers complementary in character to the centers

found on the surface of the receptor.

3 . G P C R S T R U C T U R E

As indicated by the name, structure-based drug discovery requires knowledge of the targets'

structure, in this case GPCRs. Members of the GPCR family are characterized by seven regions,

each 20±25 amino acid sequences in length, that are believed to represent the TM hydrophobic

regions of the proteins. The seven TM domains are thought to form a barrel shape, oriented roughly

perpendicular to the plane of the membrane in a counterclockwise manner. Each receptor is believed

to have an extracellular N-terminal region that varies in length from less than 10 amino acids

(e.g., adenosine receptors) to several hundred amino acids (e.g., metabotropic glutamate receptors)

and an intracellular C-terminal region. The majority of intracellular and extracellular loops are

thought to be 10±40 amino acids long, although the third intracellular loop and the C-terminal

sequence may have more than 150 residues. The overall size of GPCRs varies signi®cantly from less

than 300 amino acids in the case of adrenocorticotrophin hormone receptor to more than the 1100

amino acids for the metabotropic glutamate receptors.20

Most of the primary sequence homology among the different groups of GPCRs is contained

within the TM domains. The most conserved residues among the GPCR superfamily are located

within the TM domains and apparently represent essential structural determinants of receptor

structure and function.

Due to technical dif®culties, which complicate experimental X-ray crystallography and NMR

structure determination of GPCRs, the 3D structure of most GPCRs is still unknown. The only

known GPCR structure, a 2.8 AÊ resolution structure of rhodopsin, was published only recently by

Palczewski et al.21 This structure sheds light onto the mechanism of receptor activation and on

speci®c ligand receptor interactions. Until the publication of this work the only structural

information that existed about any GPCR was the low-resolution structure of rhodopsin that was

solved by cryoelectron microscopy.22 To this one should add the 2.5 AÊ resolution X-ray structure of

bacteriorhodopsin, obtained from micro crystals grown in lipid cubic phases, that was determined a

few years ago,23 even though bacteriorhodopsin is not a GPCR. The projection maps of bacte-

riorhodopsin and rhodopsin clearly showed the presence of seven TMs and con®rmed the basic


seven-helix bundle structure. However, the spatial organization of the TMs in rhodopsin is different

from that in bacteriorhodopsin. To date, rhodopsin is still the only GPCR of known 3D structure.

A known fact is that the location of the ligand binding-site differs from one receptor to another,

depending on the type of GPCR. Mutagenesis and biophysical studies of several GPCRs have

indicated that small-molecule agonists and antagonists bind to a hydrophilic pocket buried in the

transmembrane core of the receptor.24 For example, binding of biogenic amines to their corre-

sponding receptors is characterized by a complex network of interactions involving several

transmembrane domains in which key residues in TM3, TM5, and TM6 are essential for forming the

binding pocket, with speci®city for agonist recognition.25 It is believed that in these receptors, the

ligand's amine group pairs with a carboxyl group from an Asp residue located in TM3, whereas its

catechol ring interacts with residues in TM5 and TM6. Interactions of the ligand with TM3 through

its amine group are important for binding, while interactions with TM5 and TM6 are more important

for receptor activation.26 On the other hand, peptide ligands bind to both extracellular and

transmembrane domains.11 For example, in the NK1 receptor (which binds substance P, an 11 amino

acids peptide), three residues in the ®rst extracellular segment (Asn23, Gln24, and Phe25) and two

in the second (Asp96 and His108) are particularly required for ligand binding.27 Several residues in

TM2 and TM7 domains of this receptor (Asn85, Asn89, Tyr92, and Asn96 in TM2, and Tyr287 in

TM7) are, however, also important in determining the af®nity of the receptor for its ligand.28±29 For

moderate-sized peptides, binding usually occurs in both the extracellular loops and the N-terminal

segment and for larger ligands such as the glycoprotein hormone receptors, the binding site usually

resides solely within the extracellular N-terminus.30 Receptors that bind large ligands are often

characterized by long N-termini.31 For example, in the parathyroid hormone/calcitonin receptor

subfamily, an approximately 100-residue extracellular N-terminus contains regions shown to be

critical for ligand binding speci®city.32 The binding sites of agonists and antagonists of small

peptides are different, whereas the binding sites of larger peptide hormones and endothelin overlap

for both agonists and antagonists.20,33

4 . T H E M O D E L I N G A P P R O A C H

Due to the lack of experimental three-dimensional structures of GPCR membrane protein receptors,

structural insights must be inferred with the aid of three-dimensional computer models. As

discussed above, the structures of only two heptahelical membrane proteins were determined to date

in high resolution, rhodopsin and bacteriorhodopsin (the latter is not a GPCR).21,23,34 Therefore,

rhodopsin (and before it bacteriorhodopsin) is widely used as a template for modeling the backbone

structures of the TM domains of many GPCRs using homology-modeling techniques. Homology

modeling describes an extended collection of techniques with the goal of modeling the 3D structure

of a protein with an unknown structure, based on the known structures of related proteins. The

accuracy of the prediction relies heavily on the number of structures that serve as a template and on

their homology to the protein of interest (typically it requires more than 35% homology).35 This

method has proven very successful in modeling certain types of globular proteins. However,

applying homology modeling to GPCRs is hampered by the low sequence homology between most

GPCRs and rhodopsin (or bacteriorhodopsin). Furthermore, the great diversity of ligands that bind

to GPCRs and the known diversity in binding sites (discussed above) suggest that ligands may

interact with the receptor in different and diverse ways. Since the main purpose of GPCR models is

to describe the ligand binding sites, the homology modeling approaches are clearly limited in their

ability to predict novel binding pocket structures for the vast space of GPCR's ligands.

To overcome these problems we developed a new and novel modeling technology, named

PREDICT,36 which is able to predict the 3D structure of any GPCR. This approach requires as input

only the protein's amino acid sequence and is not based on the limited known structural information


from rhodopsin or bacteriorhodopsin. While this method is close in its concept to ab initio protein

folding approaches, it is speci®cally directed towards structure prediction of membrane-embedded

polyhelical proteins. In particular the new modeling technology stands on two pedestals: protein±

protein interactions encoded in the protein's amino acid sequence (primary structure) and protein±

membrane interactions that highlight the role of the unique environment in which these receptors

are embedded. Figure 2 schematically depicts the concepts underlying the PREDICT modeling

approach.

The role of the membrane in this context cannot be overstressed, since it determines to a large

extent the folded structure of the protein. As depicted in Figure 3, the membrane, which is a bilayer

formed by phospholipids, is a complex environment with three spatially and chemically distinct

regions: a hydrophobic core formed by the phospholipid hydrocarbon tails, polar (or charged)

interfaces on both sides formed by the phospholipid head groups, and regions of ordered water.37 It

should be noted, that while the overall structure of the membrane is determined by the lipid

components, the interactions of lipids with the surrounding water molecules and with membrane-

bound proteins are responsible for much of its diversity and function. In particular, the complexity of

the membrane environment indicates that transmembrane helices should exhibit different properties

when interacting with different regions of the membrane. These considerations are taken into

account in the new modeling approach.

The main driving force for GPCR folding, like that of any other protein, is hydrophobicity.38

This driving force has two general consequences in terms of the present modeling procedure. First, it

is reasonable to assume that the TM helices form a closed structure of some sort; especially since

most TM helices in multihelical membrane embedded proteins are amphipathic.39 This general

assumption is corroborated by, but does not depend on, the observed closed packing arrangements

of the seven TM helices in both bacteriorhodopsin22 and rhodopsin.40±42 Second, the fact that

packing is hydrophobically driven can be used for optimizing the conformation of the folded

protein.

The new technology is speci®cally tailored for GPCRs since it uses some GPCR-speci®c

assumptions. In particular it is assumed that the TM helices are arranged in a sequential manner, so

that the TM order along the sequence is also their order in the folded structure. This is based

on Baldwin's sequence analysis of minimal lengths of interhelical loops,43 performed across the

whole GPCR superfamily, which strongly supports the simple topology. We also assume, in

agreement with known residue contacts in various GPCRs, that the TM helices are arranged in a

counterclockwise manner when viewed from the extracellular side.43±44 These assumptions are

introduced mainly for computational ef®ciency, in principle the same modeling technology

can be adapted to additional protein groups also characterized by membrane embedded helical

bundles.

Figure 2. Aschematic representationof the concepts underlying the PREDICTmodelingapproach.


A signi®cant obstacle facing GPCR modeling is that while it is fairly easy to roughly identify

the TM sequences, it is very hard to pinpoint the exact `̀ ends'' of the TM helices, namely, the

location of the boundaries of the transmembrane regions. Our modeling approach is unique in the

careful way it treats this uncertainty. Most computational studies rely on hydropathy pro®les to

predict the putative TM domains of the receptor. However, secondary structure prediction tools,

such as the PredictProtein program,45 are not accurate enough to determine the exact location of the

boundaries of TM helices (or any secondary structure element for that matter) and the corrugated

character of the membrane surface itself37,46 makes the precise de®nition of such a boundary

inappropriate. Indeed, in the new structure of rhodopsin TM1, 2, 3, and 6 are signi®cantly longer

than those predicted from hydropathy plots (30, 30, 33, and 31 compared with 25, 25, 20, and 24,

respectively). This critical issue, where the TMs cross the membrane surface, is carefully treated by

our technology at several different stages of the modeling process, each time optimizing this

property rather than assuming it is known.

Since the folded structure of proteins is characterized by their lowest free energy, the modeling

procedure gets to this low-energy conformation by optimizing the model for a large number of

properties, including helical-packing geometry, multihelical tilts, helix orientations, sidechain

Figure 3. An atomistic picture of a phospholipid bilayer. Depicted is a snapshot from a molecular dynamics simulations of a 1,2-dipalmitoyl-3-sn-phosphatidylcholine, DPPC (details in Refs. 37, 46).The three regions of the membrane are color coded: (a) Thehydrophobic core formed by the phospholipid hydrocarbon tails (red), (b) Polar (or charged) interfaces on both sides formed bythe phospholipid head groups (yellow), and (c) Regions of ordered water (blue). Overlaid is a schematic representation of a trans-membranehelix.Different helix properties characterize its interactionswith the differentmembrane regions.

Table I. PREDICT Optimization

1. Helical-packinggeometry2. Multihelical tilts3. Helixorientations4. Sidechain rotamers5. Helixmembrane-surface crossing6. Helical kinks


rotamers, helix membrane-surface crossing, and helical kinks (Table I). The new modeling

procedure deals with the huge size of the protein conformation space through a unique hierarchical

design, starting with a coarse representation and gradually increasing the complexity of the

representation until reaching a full atomistic model. This algori thmic design also has a bene®t of

being computationally very ef®cient.

5 . R E S U L T S

The new PREDICT technology was already used for generating 3D models of many family-A

GPCRs. As expected, the resulting models span a wide range of conformations, some of them

signi®cantly different from the structure of rhodopsin. This is not surprising, since, for example,

many peptide receptors have signi®cantly different binding-site characteristics than in rhodopsin.

These models also agree well with a broad range of biological mutagenesis data highlighting 3D

binding sites that can be used for structure-based drug discovery.

In most cases the PREDICT technology is able to distinguish between favorable folded

conformations and unfavorable conformations. Figure 4 shows an energy score pro®le calculated by

PREDICT for, in this example, a diverse set of 279 possible initial 3D structural models of

rhodopsin. It is clearly seen that the method is able to point to a small number of low-energy

conformations as being favorable in comparison to the other conformations.

In particular, when introduced with the amino acid sequence of rhodopsin or bacteriorhodop-

sin the new modeling technology was able to recover the respective experimentally determined

structure. Unlike homology modeling approaches, which rely on these experimental structu-

res, our approach was able to achieve this result using only the protein's amino acid sequence as

input.

The rhodopsin model, shown in Figure 5, was generated from the amino acid sequence of

bovine rhodopsin (taken from ®le OPSD_BOVIN in the GPCR database GPCRDB), without using

any of the experimental structural information. The resulting model ®ts very well the experimental

2.8 AÊ resolution structure of rhodopsin,21 including the tilts and the kinks that were observed in the

experimental structure. A small deviation was observed in the position of TM4, which is an outlying

helix that does not participate in the retinal binding-site. The rms distance between the model and

the experimental structure (excluding TM4) was 3.2 AÊ , close to the level of the experimental

resolution. This rms increases to 3.87 AÊ when TM4 in included. More important is the fact that the

sidechain details in the retinal binding-site were reproduced accurately by the modeling procedure.

Figure 4. An energy score profile calculatedby PREDICT fora diverse set of 279 possible initial 3D structural models of rhodopsin.Themethodpoints toasmallnumberofconformations (the low energyconformations) asbeingsuperior toall otherconformations.


For example, the key distance between the O atom of residue Glu113 (TM3) and the N atom of

residue Lys296 (TM7) in the model is 3.6 AÊ , in close agreement with the experimentally determined

distance of 3.9 AÊ .

6 . S U M M A R Y

In this paper we presented a new modeling approach for predicting the 3D structure of any G-protein

coupled receptor (GPCRs). GPCRs are considered to be one of the most important groups of drug

targets involved in a broad range of body functions and processes and are related to major diseases.

Clearly, developing drugs to GPCR targets is a major task facing today's biotech and pharmaceutical

industries. Unfortunately, conventional drug discovery, in the case of GPCRs as well as in other

cases, is a slow process. It would be advantageous if structure-based drug discovery approaches

could also be used for GPCRs. This, however, requires knowledge of the receptors' 3D structures,

which due to technical dif®culties are very hard to obtain experimentally. We overcame this problem

by using a new modeling approach, named PREDICT, which combines the protein internal

properties (its amino acid sequence) with the properties of its membrane environment. Unlike

competing approaches, the new technology does not rely on the known structure of rhodopsin, and is

capable of predicting novel GPCR structures and, more important, novel binding sites. It was

demonstrated that this modeling approach could reproduce the 3D structure of rhodopsin even

though it does not rely in any way on the experimentally determined structure of rhodopsin. The

PREDICT-generated models offer new opportunities for structure-based drug discovery and for

computational screening of virtual molecular libraries towards GPCR targets.

Figure 5. The PREDICT model of rhodopsin (yellow) overlaid on the 2.8 Ð resolution experimental structure of rhodopsin (blue),21

viewed from the extracellular side (letters indicate theTMs).The model fits the experimental structure very well (except for a smalldeviation in thepositionof TM4).Thekey retinal bindingresidues,Glu113 (TM3) and Lys296 (TM7), arehighlighted.


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Dr. Sharon Shacham is head of development at Bio IT Ltd. She received a B.Sc. degree in chemistry, MBA

degree, and a Ph.D. degree in biochemistry and computational biology from Tel Aviv University. She has

academic and industry experience in information systems and bioinformatics programming.


Maya Topf is a D.Phil student in the Department of Physical and Theoretical Chemistry, Oxford University,

UK. She received a B.Sc. degree and a M.Sc. degree in Chemistry from Tel Aviv University, Israel. She has been

working on protein design and on applications of QM/MM methods to model enzymatic reactions.

Noa Avisar is a research scientist at Bio IT Ltd. She received a B.Sc. degree in biology, a M.Sc. in

neuroendocrinolgy, and a Ph.D. degree in biochemistry and molecular biology from Tel Aviv University, Israel.

She has industry experience in bioinformatics and structural biology.

Fabian Glaser is a research scientist at Bio IT Ltd. and a Ph.D. student in the Department of Biochemistry, Tel

Aviv University, Israel. He rceived a B.Sc. degree and an M.Sc in Medicinal Chemistry from the Hebrew

University of Jerusalem, Israel. He has academic and industrial experience in analysis of protein surface

properties and in information technology.

Dr. Yael Marantz is a research scientist and project manager at Bio IT, Ltd. She received a B.Sc. degree in

chemistry, a M.Sc. degree in biochemistry and endocrinology, and a Ph.D. in biophysics and structural biology

from Tel Aviv University, Israel. She has industry experience in bioinformatics and computational drug

discovery.

Shay Bar-Haim is a research scientist in the development group at Bio IT, Ltd. He received a B.Sc. in chemistry

from Bar-Ilan University, Israel, and a M.Sc. in biology from the Weizmann Institute of Science, Israel. He has

industry experience in computer programming and bioinformatics.

Dr. Silvia Noiman is a cofounder and Chief Operations Of®cer of Bio IT. She is the author or coauthor of

numerous publications in molecular biology. She received a M.Sc. degree in population genetics, a MBA

degree, and a Ph.D. degree in molecular biology from Tel Aviv University, Israel. She formerly held an

academic position at the Weizmann Institute of Science, Israel, was the coordinator of the Israel National

Biotechnology Committee and founded and managed a diagnostic laboratory for genetic diseases in Tel-

Hashomer Hospital, Israel.

Prof. Zvi Naor is a Professor of biochemistry at Tel Aviv University, Israel. He formerly held an academic

position at the Weizmann Institute of Science, Israel and was a visiting scientist at the NIH and at Kobe Medical

School, Japan. He is the author or coauthor of numerous publications in endocrinology. He received a B.Sc.

degree in chemistry and M.Sc. in biochemistry from Bar-Ilan University, Israel. He received a Ph.D. degree in

biochemistry from the Weizmann Institute of Science, Israel and was a Postdoctoral Fellow at the University of

Texas, Health Scince Center. He is the recipient of the Chaim Weizmann Phostdoctoral Fellowship (1976), the

Charles H. Revson Career Development Chair (1980), the Juludan Prize (1989), and the Israel Fertility

Association Prize (1991).

Prof. Oren M. Becker is a cofounder, Chief Scientist, and Chief Technology Of®cer of Bio IT Ltd, formerly an

Assistant Professor at Tel Aviv University, Israel was a visiting Assistant Professor at Harvard University,

Cambridge, Massachusetts. He is the author or coauthor of numerous publication on protein modeling and

simulation. He is a coeditor of the textbook `̀ Computational Biochemistry and Biophysics'' (Marcel Dekker:

NY 2001). He received a B.A. degree in philosophy, a B.Sc. degree in chemistry and physics and a Ph.D. degree

in theoretical chemistry from the Hebrew University of Jerusalem, Israel, and was a Postdoctoral Fellow at

Harvard University, Cambridge, Massachusetts. He is the recipient of the Rothschild Postdoctoral Fellowship

(1991), the Fulbright Postdoctoral Fellowship (1991), and the Yig'al Alon Fellowship for Outstanding Young

Scientists (1994).


Documents

Modeling the 3D structure of GPCRs from sequence