Computational Methods for the structuraland dynamical understanding ofGPCR-RAMP interactions

Silvia Yahel Bahena Hernández

Degree project in bioinformatics, 2020Examensarbete i bioinformatik 30 hp till masterexamen, 2020Biology Education Centre, Uppsala University, and Molecular Biophysics Stockholm-ScilifeLabSupervisors: Lucie Delemotte and Oliver Fleetwood

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Abstract Protein-protein interaction dominates all major biology processes in living cells. Recent studies suggest

that the surface expression and activity of G protein-coupled receptors (GPCRs), which are the largest

family of receptors in human cells, can be modulated by receptor activity–modifying proteins (RAMPs).

Computational tools are essential to complement experimental approaches for the understanding of

molecular activity of living cells and molecular dynamics simulations are well suited to provide

molecular details of proteins function and structure. The classical atom-level molecular modeling of

biological systems is limited to small systems and short time scales. Therefore, its application is

complicated for systems such as protein-protein interaction in cell-surface membrane.

For this reason, coarse-grained (CG) models have become widely used and they represent an important

step in the study of large biomolecular systems. CG models are computationally more effective because

they simplify the complexity of the protein structure allowing simulations to have longer timescales.

The aim of this degree project was to determine if the applications of coarse-grained molecular

simulations were suitable for the understanding of the dynamics and structural basis of the GPCR-

RAMP interactions in a membrane environment. Results indicate that the study of protein-protein

interactions using CG needs further improvement with a more accurate parameterization that will allow

the study of complex systems.

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Solving the mystery of drug failures in clinical human trials

Popular Science Summary

Silvia Yahel Bahena Hernández

Human bodies are integrated by millions of small units, each of which have specific functions which

makes life possible. These smalls units are known as cells. Because cells develop in complex systems,

they must communicate to each other to be able to respond to environment changes and adapt.

For this to happen, cells need to recognize signals from their surroundings to activate changes inside

the cell which will generate the corresponding response. This signal recognition depends on the

interaction of two molecules, one it's located in the surface of the cell which is called receptor and the

other one is known as ligand and it’s the signaling molecule.

One common cell receptor in human cells are the G protein-coupled receptors (GPCR). These receptors

are diverse and have a wide range of functions such as the detection of smells and taste. As well, they

are implicated in the cause of several diseases for example: migraine and hypertension. Indeed

approximately 50% of the medical drugs today target GPCRs.

But still one challenge that drug discovery is facing today is that several preclinical studies targeting

GPCRs have failed in human trials for reason that can’t be yet explained. It has been hypothesized that

GPCRs interact with other molecules which regulate their function and for this reason the mechanism

of ligand-receptor becomes very complex. Recent studies have shown that GPCRs do interact with

receptor activity modifying proteins (RAMPs) which play different roles from ligand specificity to

trafficking of the receptor to the cells surface.

One alternative application to study these complexes systems are computational tools. Nowadays, they

are essential to complement experimental approaches for the understanding of molecular activity of

living cells. Specifically, molecular dynamics simulations are well suited to provide molecular details

of proteins function and structure.

It is thus important to determine how RAMPS may influence GPCR activation mechanism and signaling

for a better understanding of GPCRs pharmacology which will allow the development of drugs that

target these GPCR-RAMP complexes.

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TABLE OF CONTENTS

Introduction ......................................................................................................................................... 10

Materials and methods ....................................................................................................................... 15

Coarse-grained simulations ............................................................................................................... 15

Complexes .................................................................................................................................... 15

Simulation Parameters ............................................................................................................... 15

MARTINI force field interaction parameters .......................................................................... 16

Protein structure constraints .............................................................................................................. 17

Trajectory Analysis ........................................................................................................................... 18

Fraction of Native Contacts ....................................................................................................... 18

Distances between proteins ........................................................................................................ 18

Results .................................................................................................................................................. 19

Coarse Grained Molecular Dynamics Simulation Systems .............................................................. 19

Retention of the native structure during simulation ................................................................ 22

Coarse-Grained Receptor Structures ........................................................................................ 23

GPCRs studied by CG-MD simulation ............................................................................................. 24

Complex formation of GPCRs and RAMP1 ..................................................................................... 24

Number of contacts between GPCRs and RAMP1 during simulation .................................. 24

Inter-peptide contacts between CALCRL and RAMP1 simulation ....................................... 26

Discussion ............................................................................................................................................ 27

Trajectory Analysis ........................................................................................................................... 27

Further improvements ....................................................................................................................... 28

Conclusion ........................................................................................................................................... 28

Acknowledgement ............................................................................................................................... 29

References ............................................................................................................................................ 30

Appendix A.1 ....................................................................................................................................... 32

Appendix A.2 ....................................................................................................................................... 33

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Abbreviations

AM Adrenomedullin

CALCRL Calcitonin-like receptor

CNS Central nervous system

CCR5 C-C chemokine receptor type 5

CRHR1 Corticotropin-releasing hormone receptor 1

CG Coarse-grained

ECD Extra-cellular domain

GPCR G protein-coupled receptor

GLP1R Glucagon-like receptor 1

LJ Lennard-Jones

PTH1R Parathyroid hormone 1 receptor

POPC Phosphatidylcholine

RAMP Receptor activity-modifying protein

SBA Suspension bead array

TMD Transmembrane domain

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Introduction

In order to respond to changes in the environment cells must be able to receive and process signals that

come from their surroundings. These signals will activate a reaction within the cell which will allow it

to adapt. Most of these signals are chemical reactions between proteins that cells have in their surfaces,

which are called receptors, with signaling molecules such as growth factors, hormones,

neurotransmitters, which are classified as ligands (Alberts et al. 2013). Receptors are generally

transmembrane proteins which bind to a ligand that is outside the cell and transmit the signal for the

physiological response to start inside the cell and this will dictate the behavior of an organism.

The mechanisms of receptor response are complex in mammalian cells. One example is the G protein-

coupled receptor (GPCR), which i the largest studied family of cell-surface receptors in human cells,

comprising nearly the 2% of the human genome. (Sexton et al., 2009). There are more than a thousand

GPCRs and they have a significant range of functions such as hormone response and regulation of

immune system causing an implication in metabolic, neurologic and cardiovascular diseases.

GPCRs represent 30-50% of all targets of medical drugs. But, just a small portion of these receptors are

actively targeted for therapeutic purposes. In this sense, understanding the functionality of GPCRs is

crucial not only for drug development applications, but also to have a clearer picture of their basic

physiological processes.

Their structure consists in a seven-transmembrane-domain which regulates diverse cascade signaling

as a response to ligands such as hormones, neurotransmitters and even phothins and odorants (Hilger et

al., 2018). In Fig 1, their phylogenetic tree is presented, where GPCRs are classified into five different

families depending on their sequence data and structural homology.

GPCRs have been found to be oligomeric proteins (Archbold et al., 2011). Oligomerization is the

faculty of interaction between more than one polypeptide chain, in order, to create a quaternary structure

(Nirwan & Kakkar, 2019). The oligomerization can be homodimerization, interaction between two

identical GPCRs, or heterodimerization, interaction between different types of GPCRs or with other

membrane proteins. The former is of special interest because it creates a diversity in the biological

function of the receptors, because different heterodimers have diverse effects over ligand binding and

signaling pathways (Parameswaran & Spielman, 2006).

As a matter of fact, in 1998, a study published by McLatchie and colleagues generated a shift in the

understanding of the GPCRs’ regulation.(McLatchie et al. 1998). They were having problems to

successfully reproduce the expression of calcitonin-like receptor (CALCRL) in cell lines. This indicated

that cofactors were required for the expression and function of the receptors (Parameswaran &

Spielman, 2006). After experimentally testing this hypothesis, a protein of 148 amino acids was

identified as being required for the receptor to express in the cell surface allowing calcitonin to bind.

Consequently, a database search was performed and two other related proteins in mammals were found.

(McLatchie et al., 1998).

The discovery of these receptor activity-modifying proteins (RAMPs) reveled functional diversity by

interacting with GPCRs. Another interesting discovery from McLatchie study about RAMPs was that

the type of ligand that binds to the calcitonin receptor is determined by the RAMP that interacts with

the receptor. In the case of RAMP1, the interaction generates a receptor for calcitonin, which is involved

in the control of bone metabolism and it is active in the central nervous system (CNS). Meanwhile, the

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interaction with RAMP2 creates a receptor for adrenomedullin (AM) which is part of the calcitonin

peptide family and acts as a potent vasodilator (McLatchie et al., 1998). These examples indicate that

there is a great diversity in the mechanisms of receptor response regulated by RAMPs, and this could

be one explanation why clinical trials for drugs targeting GPCR tend to fail in humans.

Currently, RAMPs have been identified as being able to alter trafficking of some GPCRs to the cell

surface, ligand selectivity and can even influence the fate of the receptor by recycling or degradative

pathways (Hay and Pioszak 2016). In a previous comparative genomics study (Barbash et al. 2017) a

global coevolution of GPCRs and RAMPs was demonstrated. This was followed by another study

(Lorenzen et al. 2019), where the GPCR-RAMP complexation of 23 GPCRs, including all secretin-like

family, and the 3 RAMPs was experimentally tested by a multiplexed suspension bead array (SBA)

immunoassay. For this assay, they engineered epitope tags to GPCRs and RAMPs respectively and

complexes were detected by antibodies against the engineered epitope tags. The results suggest which

receptor of the secretin-like family probably have an interaction with RAMPs.

Since the discovery of RAMPs, almost two decades ago, the focus has been toward calcitonin receptors

which are part of the GPCR secretin class. But now it is known that RAMPS are also able to interact

with several different GPCRs from secretin family and some from the Rhodopsin and Glutamate family.

(Booe et al., 2015) Although it is not yet clear how RAMPs regulate the receptors function.

Thus, it is important to determine how RAMPs may influence the GPCR activation mechanism for a

better understanding of GPCR pharmacology, as well as, to explore the potentiality that RAMPs have

as drug targets and their role in the development of different diseases such as cancer and hypertension.

Fig 1 . Position of selected GPCRs on the phylogenetic tree (Stevens et al. 2013)

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These approaches will allow the development of drugs that target effectively GPCR-RAMP complexes

(Spielman and Parameswaran 2012).

Computational tools are essential to complement experimental approaches for the understanding of

molecular activity of living cells. Specifically, molecular dynamics simulations are well suited to

provide molecular details of protein function and structure. Nowadays with the rapid increase of

computer power together with the development in theoretical chemistry and physics, it has becoming

evident that these computational tools could be essential in the understanding of many biological

processes at a molecular level, such as the interaction between GPCRs and RAMPs.

In order to perform Molecular Dynamic Simulations a set of potentials known as force field which

describes the interactions between atoms of the molecules, is needed. Force fields can be based on

molecular physics or statistical analysis of structural structures (Singh and Li 2019). The classical atom-

level molecular modeling of biological systems is limited to small systems and short time scales, due

this, it is complicated its application for systems such as protein-protein interaction in cell-surface

membrane.

Recently, as an alternative, coarse-grained (CG) models have become widely used and they represent

an important step in the study of large biomolecular systems. The reason is that CG models are

computationally more effective because they simplify the complexity of the protein structure allowing

simulations to have longer timescales, as well as, being able to handle larger size systems. CG

simulations implements a mapping process which implies the transformation of the atomistic structure

to coarse-grained beads.

So far, the successful application of CG simulations has been seen in the studying of protein folding

mechanisms, protein structure prediction and protein-lipid interaction (Kmiecik et al. 2016). The

implementation of this type of models ignores significant important features of real proteins, yet they

can explain fundamental features such as the dynamics and interactions of proteins residues. Most

biomolecular systems, including proteins and their complexes, are too complicated to be efficiently

handled by classical molecular modeling tools, they require significant time to perform a simulation.

For this reason, CG models are promising to understand complicated biology systems.

Fig 2 All-atom compared with CG representations in the MARTINI model. (Kmiecik et al. 2016)

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One example of a GC model is the MARTINI force field where the mapping is based on one-to-four,

which means that on average four heavy atoms are represented by a single bead (Kmiecik et al. 2016),

as it is shown in Fig 2. MARTINI parameters were calculated from structural data derived either from

atomistic geometry or from coarse-grained representations that were adjusted to overlap with all-atom

simulations of corresponding atom groups.(Bradley and Radhakrishnan 2013). These beads are

classified by their polarity (polar, nonpolar, apolar and charged), along with their hydrogen bonding

capabilities (donor, acceptor, both or none) (Bradley and Radhakrishnan 2013).Giving a total of

eighteen unique “building blocks”(Kmiecik et al. 2016).

The parameters that describe the interactions between the CG beads are Lennard-Jones (LJ) potential

for nonbonded interactions, Coulomb potential for charged groups and bonded interactions, such as

bond lengths, angles and dihedrals by a standard set of potential energy functions.(Bradley and

Radhakrishnan 2013)

The general factor that describe the bonding between particles are bond energy, energy holding particles

together. The bond length, how far they are going to be apart. Figure 3 explains the Lennard-Jones

Potential, where the depth of the well (ε) represents the bond energy controlling the interaction strength

between particles.

Another important characteristic of MARTINI is that it counts with parameters for a significant number

of biomolecules such as lipids. Moreover, this force field is developed to be use with GROMACS.

(Kmiecik et al. 2016).

As it has been mentioned, when proteins are mapped from all-atom to CG, they lose important features

which stabilize their integrity during the simulations. The preservation of the correct structural

configuration is important because proteins can have many shapes but only when they are in the correct

configuration they can be selected by their binding partners.

In this sense, it is important to integrate structural constraints to the proteins during a dynamic

simulation. These constraints are known as Elastic Networks and consist on a network of point masses

connected to each other through springs when the distance between the masses is less than the

predefined cutoff distance (Periole et al. 2009). Elastic Network and Elnedyn are common models for

protein constraints for CG protein simulations.(Xie, Chen, and Wu 2017)

Fig 3 Lennard-Jones Potential: The deeper the well the stronger the interaction between the particles. When the bonding

energy is equal to zero, the distance between the particles will be equal to σ.

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● Elastic Network: It is a simple model that maintains a constant constrains through the structure.

Conserves structures without compromising the realistic dynamics of the protein (Periole et al.

2009).

● Elnedyn: Combines a structure-based model with the MARTINI force field. The constraint is

built just across the backbone beads (Periole et al. 2009).

The aim of this degree project was to determine if the applications of coarse-grained molecular

simulations were suitable for the understanding of the dynamics and structural basis of the GPCR-

RAMP interactions in a membrane environment.

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Materials and methods

Coarse-grained simulations

Complexes

This degree project was principally based on the paper by Lorenzen et al. 2019, where the GPCR-

RAMP complexation of 23 GPCRs and the 3 RAMPs was experimentally tested. To evaluate the ability

of the CG simulation to give an insight into the dynamics and structural basis of these protein

complexes, five GPCRs were selected which are presented in the table below (Table 1). This choice

was made because the selected GPCRs give an integrated picture of the biology of GPCR-RAMP1

complexes as well as having available structures (A.1). The simulations were performed just with

RAMP1 (PDB:6E3Y) because it is the only full-length crystal structure available with interaction with

CALCRL.

Table 1 GPCR selected for simulations

Gene PDB Code Protein Interacting RAMPs

CALCRL 6E3Y Calcitonin receptor-like

receptor

All

GLP1R 6B3J

Glucagon-like receptor 1 All

CCR5

6MET C-C chemokine receptor

type 5

None

CRHR1 4Z9G

Corticotropin-releasing

hormone receptor 1

2-3

PTH1R 6NBI

Parathyroid hormone 1 receptor All

Simulation Parameters

The MARTINI force field version 2.2 was employed for the simulations. Each system was simulated

with the parameters shown in table 2. The proteins (GPCR and RAMP1) were embedded in a POPC

bilayers, with the “insane” MARTINI tool. The proteins were coarse-grained using the “martinize” tool

from MARTINI which performs the mapping. All GC simulations were carried using Gromacs.

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Table 2. Simulation Parameters

Force Field: MARTINI v2.2

Size of simulation box: 116 Å x 185 Å x 185 Å

Duration of simulation: 1 us

Timestep: 20 fs

Initial distance between the

center of mass of the proteins:

5 nm (2 different placements in front and behind GPCR in the

membrane.)

Temperature 300 K

Barostat: Parrinello-Rahman

Lipid bilayer: POPC

Protein structures: Proteins are separated into 2 monomers (GPCR and RAMP) and they

are oriented in the z-axis to maintain the orientation of the complexes

normal to the bilayer.

MARTINI force field interaction parameters

Nonbonded interactions:

o Lennard-Jones potential

𝑉(𝑟) = 4 𝜀 [𝜎12

𝑟−

𝜎6

𝑟] (1)

LJ approximates the interaction between a pair of neutral atoms, 𝜺 the bond energy, 𝝈 is a

constant for a specific type of molecule when the potential energy is equal to 0 and r is the

distance between the particles.

Electrostatic interactions

o Coulomb potential

𝑈(𝑟) =𝑞𝑖𝑞𝑗

4𝜋𝜀0𝑟𝑖𝑗 (2)

The Coulomb potential is applied for charged particles where q is the charge and r the distance

and 𝟒𝝅𝜺𝟎 is the Coulomb’s constant.

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Bonded interactions

𝑉𝑏 =1

2𝐾𝑏(𝑑𝑖𝑗𝑑𝑏)2 (Bond) (3)

𝑉𝑎 =1

2𝐾𝑎(𝑐𝑜𝑠(𝜙𝑖𝑗𝑘) − 𝑐𝑜𝑠(𝜙𝑎))2 (Angle) (4)

𝑉𝑑 =1

2𝐾𝑑(1 + 𝑐𝑜𝑠(𝜃𝑖𝑗𝑘𝑙) − (𝜃𝑑))2 (Dihedral) (5)

𝑉𝑖𝑑 =1

2𝐾𝑖𝑑(𝜃𝑖𝑗𝑘𝑙) − (𝜃𝑑)2 (Improper dihedral) (6)

V represents the potential energy from bond, angle, dihedral and improper dihedral contribution. K are

the stiffness constants and {𝑑𝑏, 𝜙𝑎, 𝜃𝑖, 𝜃𝑖𝑑} are the equilibrium values for the interaction (Bradley and

Radhakrishnan 2013)

Protein structure constraints For the simulation of CALCRL-RAMP1 in order to determine the best conformational constraint Elastic

Network or Elnedyn were used (Table 3). For the rest of the simulations Elnedyn constraint only were

applied.

These models consider two parameters: the spring force constant and the cutoff distance. These

parameters characterize the rigidity and the extent of the network.

Table 3. Protein structure constraints parameters

Elastic Network Elnedyn

Elastic bond force constant: 500 kJ.mol-1.nm-2 Elastic bond force constant: 500 kJ.mol-1.nm-2

Lower elastic bond cut-off to 0.5 Upper elastic bond cut-off to 0.9 nm

Upper elastic bond cut-off to 0.9 nm

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Trajectory Analysis

Fraction of Native Contacts

○ MDTraj and Jupyter Notebook

For the analysis of the fraction of native contacts (Q), MDtraj was used, which is a tool for trajectory

analysis. Q is calculated as the total number of native contacts for a given time frame divided by the

total number of native contacts from the reference structure. For CG, a native cutoff of 6.0 Å was used

and a lambda constant of 1.5. The resultant array was visualized with Jupyter Notebook.

Distances between proteins

○ Gromacs mindist and R

Mindist computes the distance between any two pair of atoms from different groups, in this case GPCR

and RAMP1. The number of contacts within a cuff off <0.6 nm was used. The resultant array was

visualized with R.

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Results

Coarse Grained Molecular Dynamics Simulation Systems For the validation of CG simulations, CALCRL, the GPCR which presents the most significant

interaction with RAMP1, was used as reference. As well, this receptor is the only one with an available

Cryo-EM structure of the active, Gs-protein complexed human CGRP receptor with RAMP1 (PDB:

6ey3). (Fig 4)

The following picture (Fig 5) shows the CG structures of the two proteins in the CALCRL-RAMP1

complex. CALCRL, is shown in orange and RAMP1 in purple. RAMP1 makes extensive stable

interactions with CALCR residues, ~23% of its surface is buried within the interface, which is show by

the pink beads on the CG structure. (Liang et al. 2018). The receptor residues which are involved in the

interaction are colored in green. The residues which were found to be highly involved in the binding,

are in black in CALCRL and red in RAMP1.

As it can be observed, the interaction between these proteins take place in the extra-cellular (EC) domain

of both proteins and the RAMP1 transmembrane (TM) domain sits at an interface formed by 3-5 TM

domain of the CALCLR. The location of these residues was important to generate a sequence analysis

to determine if they were conserved among the different selected receptors. ( A.2)

For each receptor the simulation was prepared by placing RAMP1 in the front and back of the receptor

to eventually evaluate if it was able to find the correct location for the interaction. (Fig 6)

Fig 4 Cryo-EM Structure: CGRP–CLR–RAMP1-Gs heterotrimer (PDB: 6ey3) (Liang,Y, 2018).

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Fig 5 . Coarse-grain structures from the CALCRL-RAMP1 complex. A) CALCRL: The overall structure is shown in

orange. The green beads represent the contact area with RAMP1 and the black beads represent the residues highly involved in

the interaction. B) RAMP1: The overall structure is shown in purple were pink beads represent the contact area with CALCRL

and the red beads represent the residues highly involved in the interaction.

Fig 6 Coarse Grained Protein-Protein Interaction of CALCRL-RAMP1. The figure shows the same structures as Fig 5

but here interaction between CALCRL and RAMP1 is represented.

Fig 7 Simulation system of CALCRL-RAMP1 embedded in a POPC bilayer. Example of the simulation system, here

RAMP1 is in front and back of the receptor. Chain of lipids (POPC) are shown in cyan, glycerol beads in pink, phosphate in

brown and choline in dark blue Water has been omitted from the images for clarity.

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Protein sequence alignment

The crystal structure (PDB: 6e3y) for CALCLR and RAMP1 identified interactions between the

proteins in the N-term domain as well as in TMD 3,4,5 of CALCLR. For this reason, the alignment

between the different receptors was done in these specific domains.

For the N-term of the receptors (A.2), CALCRL is more similar to PTH1 with 28 similar amino acids

and 17 which are exactly the same. CALCRL has no similarity with CCR5 which belongs to the GPCR

class A, while all the others are class B. Concerning, the class B receptors, CALCR is less similar to

CRHR1. The former is the only receptor in class B which didn’t interact with RAMP1 as showed in

A.1.

Table 4. Summary of sequence alignment of the N-term and 3-5TMD between studied receptors and CALCRL

CALCRL

N-term TMD 3,4,5

Similarities

Identities

Similarities

Identities

PTH1R

28 17 55 36

GLP1R 23 7 54 33

CRHR1 12 2 52 40

CCR5 3 0 30 12

The comparison for TMD 3,4,5 of the receptors (A.2) show that CALCR is more similar to PTH1. This

alignment shows certain similarity between CCR5 and CALCR, which could indicate that for a receptor

to be able to establish an interaction with RAMP1 it should have the corresponding residues in both,

the N-term and the transmembrane domains.

Table 5. Sequence conservation of the contact residues identified for CALCL-RAMP1 stable interaction in the four

other studied receptors.

CALCRL CCR5 CRHR1 GLP1R PTH1R

N-term 42 M - L 15 L 50 G 85

N-term 46 Y - N 19 P 54 P 89

N-term 49 Y - S 22 A 57 E 92

N-term 50 Q - A 23 T 85 E 93

N-term 53 M - Q 26 F 61 E 96

TM3 235 I I 124 C 211 V 249 L 304

TM4 262 F V 155 V 238 V 276 L 331

TM4 258 L T 152 I 234 I 272 F 327

TM4 254 W T 148 M 230 L 268 G 323

TM5 300 A L 205 L 278 F 315 A 369

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As it is shown in the table 5, residues are not highly conserved between the receptors. Most of the

residues appear to be hydrophobic and the interaction could therefore be rather non-specific. For this

reason, it would be important to perform molecular dynamic simulations which can explain the form

RAMP1 is interacting with the receptors.

Retention of the native structure during simulation

To evaluate the stability of the protein conformation during the simulation two structural constraints

that are common for CG protein simulations were tested (Elastic Network and Elnedyn) on the

CALCRL-RAMP1 complex.

The fraction of native contacts (Q) were determined for the individual protein structures using its Cryo-

EM structure as reference (Fig 8). Q is defined as the fraction of heavy atoms proximal in the native

structure that are in close spatial proximity at some instant in time. (Best, Hummer, and Eaton 2013).

The calculation was done for both systems, when RAMP1 was placed in front and back of the receptor.

The comparison of Q for both type of contains for each protein are shown in Fig 9.

As observed on Fig 9. a higher fraction of native contacts is retained in the presence of Elnedyn making

the structure more native like compared to the case where Elastic Network was used. This justifies the

implementation of Elnedyn for the rest of the proteins for more stable CG simulations.

Fig 6 Initial and final coarse-grained structures for CALCRL-RAMP1 simulations. A) Elenedyn B) Elastic Network

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Coarse-Grained Receptor Structures

The ability of the MARTINI Force Field to predict the complex formation of GPCRs and RAMP1 was

evaluated. A total of five GPCR-RAMP1 systems were simulated, as previously mentioned in table 1.

Fig 7 Time evolution of the fraction of native contacts of the proteins: A) Calcitonin. B) RAMP. The orange and blue lines

show the simulation, in front and back, respectively, with Elnedyn and red and green line show the simulations in front and

back, respectively, with Elastic Network

Fig 8 CG GPCR receptors: A) CALCRL, B) PTH1R, C) GLP1R, D) CRHR E) CCR5. Inside the blue box are the class

B Receptors and inside yellow box, the class A receptor: Inside the blue rectangle. The lines dividing the boxes indicate the

ECD, T M and ICD (intracellular domain). For CALCRL, black beads represent the residues highly involved in the interaction

with RAMP1, green beads in the rest of the receptors (red in GLP1R) show the residues equivalent to the ones represented in

black for CALCRL

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GPCRs studied by CG-MD simulation Five GPCRS were selected to perform the simulations based on their biological characteristics and in

the availability of their crystal structure. Their biological characteristics are described below.

Calcitonin-like-receptor (CALCR) is the most studied receptor for interaction with RAMPs. It is the

only receptor for which a structure involving the interaction with RAMP1. It is in the cell-surface and

the conformation of the complex with RAMP1 is compulsory for the receptor to be expressed in the

cell-surface. This action can explain how RAMP1 modulate the trafficking to the GPCR to the cell-

membrane (McLatchie et al. 1998).

Parathyroid hormone 1 receptor (PTH1R) is a class B receptor which has a similar structure to

CALCRL but its affinity to RAMP1 is less specific. The study of this receptor would clarify the

specificity of CALCR (Kusano et al. 2012) .

Glucagon-like receptor 1 (GLP1R) is a class B receptor and it was newly identified by Lorenzen et

al. 2019 as forming complex with RAMP1. The assay detected interactions everywhere in the cell, not

just in the cell-surface so this could be an explanation why this complex wasn’t identified until now.

Probably the complex forms inside the cell and it is preventing the receptor to be expressed in the cell-

surface. Showing the opposite action that RAMP1 is having with CALCRL. Giving a better insight on

how RAMPs modulate receptors trafficking to the cell-surface. (Lorenzen et al. 2019)

Corticotropin-releasing hormone receptor 1 (CRHR1) is the only class B receptor (Lorenzen et al.

2019) which didn’t form a complex with RAMP1. This would explain the differences within class B on

why some receptors are able to form complex with RAMP1 and others not.

C-C chemokine receptor type 5 (CCR5), is a class A GPCR. It has been demonstrated that most

receptors that can interact with RAMPs are from the class B. The study of this receptor, which doesn’t

interact with RAMP1, would explain the differences between families for the interaction with RAMP1.

(Lorenzen et al. 2019)

Finally, the reason for the interactions to just be done with RAMP1 is that having experimental structure

data about the interaction is important to be able to validate the correct simulation of the complex. This

cannot be validated with RAMP2 and RAMP3 which lack structures.

Complex formation of GPCRs and RAMP1

Number of contacts between GPCRs and RAMP1 during simulation

Protein-protein contacts were defined as the number of heavy atoms of each protein being within 6 nm

of heavy atoms of the other. The number of contacts indicates that the proteins form a complex in a few

nanoseconds (Fig 11). The contacts were expected between the pink beads in RAMP1 and the green

ones in CALCRL which would indicate the correct interface compared to the crystal structure. For the

rest of the receptors the interaction was expected in the highlighted residues which were equivalent to

the ones interacting with CALCRL. But it is observed that the binding between the two proteins is

disproportionately strong and fast affecting the correct dimerization interface.

This shows that the number of inter-protein contacts is overestimated in comparison with a real process,

probably due an irreversible dimerization of the proteins during the simulation. The graph shows that

the number of contacts always increase which could indicate that after the initial contacts there is an

irreversible binding process.

25

Fig 9 Number of contacts between the receptor and RAMP. A) CALCRL, B) PTH1R, C) GLP1R, D) CRHR E) CCR5.

Right panels show the simulations with RAMP1 Infront of the receptors and left panel show RAMP1 in the back of the receptor.

The jagged line shows the number of contacts and the smooth line shows the tendency for easier visualization of the interaction.

26

Inter-peptide contacts between CALCRL and RAMP1 simulation

The correct prediction of the complex can just be evaluated for CALCRL because it’s the only available

crystal structure of the complex with RAMP1. The fraction of native contacts (Q) which is the fraction

of heavy atoms proximal in the native structure that are in close spatial proximity at some instant in

time. (Best, Hummer, and Eaton 2013) between Calcitonin residues (42 M,46 Y,49 Y,50 Q,53 M,262

F,300 A,235 I,258 L,254 W) and RAMP residues (66 Y,97 H,123 I,126 P,130 T,134 T,137 V) which

were identified as the highly involve residues in the interaction between the proteins from the Cryo-EM

Structure.

Fig 10 Time evolution of the fraction of inter-peptide contacts between Calcitonin and RAMP: Blue line: Show few

contacts because the proteins were facing the correct orientation for the interaction. Orange Line: No correct contacts were

identified because the initial contacts between the proteins were presented in the incorrect interface.

On the graph, the blue line presents few correct interactions (~4%) for around half of the simulation the

fraction of inter-peptide contacts between CALCRL and RAMP1 demonstrated poor agreement

between the simulation and the experimental results, probably due the irreversible dimerization.

27

Discussion

Inspired by the experimental evidence of protein-protein interactions of different GPCR-RAMP

complexes generated by Lorenzen et al. 2019. computational approaches such as molecular dynamics

simulation, were applied to get an insight into the dynamics and structural basis of GPCR-RAMPs

interactions.

For the simulation of small transmembrane helices, atomistic simulations can be implemented. But for

larger proteins, all atom molecular simulations could be very computationally expensive. For this reason

CG molecular simulation were chosen, which reduces the size of the simulation system (Yoo and Cui

2013).

Trajectory Analysis It is noteworthy that simulations couldn’t predict the complex conformation for GPCR-RAMP1. The

interaction between the proteins result into too many inter-peptide contacts, suggesting protein-protein

overbidding. This could be an indication that MARTINI proteins are too “sticky” and will interact to

any other protein, generating kinetically trapped structures. This is probably due to an irreversible

dimerization of the protein after the first contact.

The correct prediction of the complex could just be evaluated for CALCRL because it is the only

available crystal structure of the complex involving RAMP1. The fraction of inter-peptide contacts

between CALCRL and RAMP1 demonstrated poor agreement between the simulation and the

experimental results because of the irreversible dimerization effect.

One reason for the loss of accuracy of the interaction between the proteins is that when the structures

are mapped to CG, atoms are going to be pulled together. The smaller the number of explicitly treated

united atoms, the faster the simulation but the lower the accuracy. This effect increases the bond strength

and the bond length decreases, creating strong repulsive forces.

One alternative to adjust the protein-protein interaction levels in the MARTINI force field is to scale

down the LJ interactions among the proteins (10%) in order to lower the dimerization free energy

(Javanainen, Martinez-Seara, and Vattulainen 2017). The LJ potential represents the attraction and

repulsion of the particles. The proposed technique reduces 𝜺 in Eq. 1, which represent the bond energy.

The lower this value, the lower the interaction between the two particles. Allowing the proteins to

sample more stable conformations until they find their optimal orientation

The former was tested in a previous study obtaining a reduction in the fast clustering between the

proteins, however, the correct prediction of the dimerization couldn’t be achieved. (Javanainen,

Martinez-Seara, and Vattulainen 2017)

As well, it was emphasized that all scaling schemes should be adapted carefully and validated by

comparison with experimental data. This is not possible yet for GPCR-RAMP interactions, since most

of them remain unverified by experimental measurements of association. Nowadays, one Cryo-EM

structure is available for the CALRCL-RAMP1 interaction. For this reason, setting the correct

parameters would be difficult especially if no experimental data is available for these protein complexes.

For the specific case of GPCRs, it is stated that they require more specific scaling because their different

domains are exposed to water and lipid environments. Probably a careful residue specific scaling

adjustment of the protein-protein interaction will improve the results with CG simulations, but this

technique would result to be very laborious and increase greatly the simulation time. (Javanainen,

Martinez-Seara, and Vattulainen 2017)

Indeed, it is not possible for any CG model to perfectly match simultaneously different features such as

thermodynamic, structural and kinetics. The representations must be carefully designed based on

specific experimental results. In this way, CG simulations can be combined with restrains derived from

specific systems to allow a better interpretation of biological process.

28

Further improvements The factors that control the strength and pattern of protein association in membranes have not yet been

well defined in molecular dynamics simulations, especially for CG simulations. It is known that an

effective interaction depends on the three-dimensional structure of the protein, and the bilayer properties

(Yoo and Cui 2013).

Regarding to the three-dimensional structure, simulations were performed to determine the best

conformational constraints finding out that Elnedyn showed to be a constraint model which could retrain

~97-98 % of the native contacts from the original crystal structure configuration during the simulation.

The effect of using these restrictions over the structure and the dynamics of the model is still not well

determined, therefore, the importance on evaluate which type of constraint best suits each system

depending on the biological question to be answered. (Periole et al. 2009)

For bilayer properties, since the membrane modulates protein-protein associations by altering the rate

and interface of the complex formation, membrane lipid characteristics should be considered

(Javanainen, Martinez-Seara, and Vattulainen 2017). Simulation models often assume that all lipid

membranes behave the same way and many recent studies have suggested that annular and bulk lipids

have different properties. In this sense, it is important to also determinate on what degree these

characteristics influence the simulations. (Yoo and Cui 2013)

Conclusion The five GPCR-RAMP1 complexes is proposed might describe the mechanisms by which RAMP1

alters GPCRs activity, more experimental data regarding their binding affinity and kinetic rate constants

would facilitate the implementation of computational methods to elucidate their biophysical principles.

The computational method of molecular dynamic simulation by Coarse-Grained demonstrate an

unrealistic complex formation which resulted in an irreversible process, indicating an overestimation of

the protein-protein interaction with the MARTINI force field. Therefore, the study of protein-protein

interactions using CG is an active field of research which requires further improvement with a more

accurate parameterization allowing the study of complex systems such as protein-protein interaction in

a membrane.

29

Acknowledgement

I would first like to express my sincere gratitude to my supervisor Lucie Delemotte for providing me

the opportunity of participating with her for this degree project. For all her guidance, advices and time

invested in this project. As well, I am grateful to Oliver, Sergio, Lea, Annie, Koushik, Ahmad, Yuxuan

and Sarah for all their help to complete the different steps of this project.

I also would like to thank the Molecular Biophysics Stockholm-ScilifeLab deparment for let me

participate in their multiple activities, which always resulted to be very interesting.

I would also like to acknowledge Jens Carlsson, Associate Professor of the Department of Cell and

Molecular Biology at Uppsala University for his time and acceptance of being the subject reader of this

thesis.

Finally, I would like to thank the Consejo Nacional de Ciencia y Tecnologia (CONACyT) for the

financial support making possible my participation in this master program.

30

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APPENDIX A.1 The following table summarized the 23 GPCRs used by Lorenzen et al. 2019. Light green color in the

box name means there is an availed structure and the orange outline identify the selected 5 GPCRs for

this project. Their found interaction with each RAMP is also presented with red as no interaction and

green as high interaction.

A1. Summary of structures and RAMPs interaction for 23 GPCRs

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APPENDIX A.2 Sequence alignment from GPCRdb of the studied receptors (N-term, TMD 3,4,5). Red rectangles

indicate the contact residues identified for CALCL-RAMP1 stable interaction.

N-term

34