ELSEVIER

Available online at www.sciencedirect.com MATHEMATICAL AND

SCIENCE ~ D I R I E C T e COMPUTER MODELLING

Mathematical and Computer Modelling 41 (2005) 1157-1170 www.elsevier.com/locate/mcm

pH Impact of Temperature and

Value on the Stability of hGHRH: An M D Approach

J I A N F E N G F E N G Department of Mathematics, Hunan Normal University

410081, Changsha, P.R. China and

Computer Science Department, Warwick University Coventry CV4 7AL, U.K.

j ianf eng. f eng©warwick, ac. uk http ://www. dcs. warwick, ac. uk/ feng

E. F E R R A R O AND B. TIROZZI Physics Department, Rome University ~La Sapienza', Roma 00185, Italy

<f err aro><tirozzi>©brahma, phys. uniromal, it http ://krishna. phys. uniromal, it/home, html

A b s t r a c t - - I n this work, we apply molecular dynamics to study the stability of growth hormone releasing factor (GHRH), in aqueous solution. The instabilities of the fragment GHRH(1-29) are well known from experimental studies and we performed simulations in order to verify the capability of molecular dynamics to predict unstable behaviours. We checked the occurrence of dynamical transitions of peptide conformation that increase or reduce chain flexibility and solvent exposure, depending on the macroscopical parameters pH and temperature. © 2005 Elsevier Ltd. All rights reserved.

geywords- -Molecula r dynamics, Stability, hGHRH, Protein structure.

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

Molecular dynamics simulations are one of the most powerful approaches to investigate protein

properties [1]. Since the formation of a stable protein occurs within a very short time, it is almost

impossible to experimentally fully track down protein folding dynamics. In fact, as pointed out

in [2], even all experimental results are modified by molecular dynamics simulations. With the

increasing power of computers, now we are capable of simulating a given protein within time

scales of nanoseconds. It has been reported that, based upon molecular dynamics simulation, a

protein could be stabilized within a few picoseconds.

In the current paper, we investigate the stability properties of a well known and important

protein, the human growth hormone releasing factor (hGHRH). The significance of such a study

Work supported by SERONO grant 2001. The work was supported by an IRCS grant. Simulation were performed on CASPUR (Inter-University Consortium for Supercomputing and Research) machines, with the precious help of Giovanni Chillemi.

0895-7177/05/$ - see front matter ~) 2005 Elsevier Ltd. All rights reserved. doi: 10.1016/j. mcm. 2005.05.009

Typeset by ,4A~-TEX

1158 J. FENC et al.

is obvious, both for theoretical reasons and therapeutic treatments. Actually, the original mo- tivation of our study is from the pharmacology company SERONO and in particular the IRCS (Cesare Serono Research Institute of Rome). Experimentally, IRCS has carried out a few years study on the stability of hGHRH subject to various environment conditions. It has been found that ASP3 (aspartic acid), ASN8 (asparagine), and MET27 (methionine) are the most fragile points of the chain (see below for details).

It is then naturally to ask whether the experimentally observed phenomena can be confirmed by molecular dynamics simulations, or whether molecular dynamics simulations can even enable us to gain further insights onto the detailed changes of the structures of hGHRH, and thus, help us to design new drugs to prevent or enhance these changes.

We start our simulation with a microscopic cubic box (20 × 20 x 20/~3) containing the protein and the solvent molecules (water). Usually the number of atoms in the box is about 10,000- 100,000, hence the requirement of computing resources is enormous and we carry out our simu- lations on a parallel computer (IBM parallel machine SP3 with 16 CPUs). In the current paper we confine ourselves to classical interactions among atoms, i.e., the evolution of the protein is Newtonian. Our simulations use AMBER 6.0 software package.

Non equilibrium states are not important since the protein relaxes in a stable state very quickly (within 200 ps ranges). At the moment, it is not clear the implications of the assumption that the interactions are classical. We summarize our findings in this paper.

2. E X P E R I M E N T A L I N F O R M A T I O N

We summarize some known properties of GHRH. GHRH is a hormone released by the hypotha- lamus, acting at the level of the front lobe of the pituitary gland to stimulate growth hormone secretion. Physiological studies showed that the active core of hGHRH lies in the N-terminal region of the peptide and that, in comparison with the whole sequences, the amide fragment hGHRH(1-29) maintains almost all biological activity, both in vitro and in vivo. At pharma- ceutical level, it is available under the name of GEREF (SERONO), a synthetic acetylated form of residues 1 to 29 of GHRH, used for the treatment of growth hormone deficiency. The major references for such information are IRCS and Web data banks.

2,1. Primary and Secondary Structures

The primary sequence of GHRH precursor, composed of 108 residues (molecular weight 12447 Da), is:

10 20 30 MPLWVFFFVI LTLSNSSHCS PPPPLTLRMR

50 60 70 Y R K V L G Q L S A R K L L Q D I M S R QQGESNQERG

90 100 108 SMWAEQKQME LESILVALLQ KHSRNSQG

40 RYADAIFTNS

80 ARARL GRQ VD

(2.1)

where we have used bold words to indicate the active fragment of 29 residues (from 32 to 60). Some features of the sequence are shown in Table 1. The peptide hCHRH fills the positions 32-75 in the precursor sequence. It is preceded by the signal (1-20 positions) and is followed by the 75-108 domain. Both fragments are subject to fission at the moment of peptide activation. All the sequences belong to the glucagon family and the higher similarity can be checked within the mesocricetus auratus sequence (MESAU-GHRH).

The primary sequence of the fragment GHRH(1-29)-NH2 is shown in Table 2, with acidity and solvent affinity information and experimentally found secondary structures [2].

Impact On The Stability Of hGHRH

Table 1. A summary of the GHRH sequence. The entire protein is constituted of 108 residues, where the active core is a 44 residues fragment (from SWISS-PROT database). Experiments showed that the peptide formed by first 29 residues of the active core maintain the biological activity both in vitro and in vivo [2].

Key Start End

Signal 1 20

Peptide 32 75

Mod_ Res 75 75

Variant 103 103

Conflict 92 92

Length

20

44

Description

Somatoliberin (GHRH)

Amidation (G-76 provide amide group)

missing (in a 2nd precursor)

E ~ D (in [3])

Table 2. A summary of the GHRH sequence with acidity, solvent affinity informa- tion and secondary structures. '4-' means hydrophobic and '- ' indicates hydrophilic. Secondary structure configurations (a---- a helices axe from NMR experiments [2].

N

1 Tyr (Y)

2 Ala (A)

3 Asp (D)

4 Ala (A)

5 Ile (I)

6 Phe (F)

7 Tar (T)

8 Asn (N)

9 Ser (S)

10 Tyr(Y)

11 Arg (R)

12 Lys (K)

13 Val (V)

14 Leu (L)

15 Gly (G)

16 Gln (Q)

17 Leu (L)

lS Ser (S)

19 Ala (A)

20 Arg (R)

21 Lys (K)

22 Leu (L)

23 Leu (L)

24 Gln (Q)

25 Asp (D)

26 Ile (I)

27 Met (M)

28 Ser (S)

29 Arg (R)

Residue Solvent affinity 75% Methanol/Water Acidity

+

A

4-

4-

4-

4-

B

B

+

+

4-

4-

B

B

4-

4-

A

4- +

B

O~

Ot

O/

OL

O~ O~

Ot Ot

OL O/

Ot O~

Ot OZ

Ot

O/

O~

Ot

OL

Ot

O~

(:~ OL

OL Ot

Ot O~

O~

Water

1159

2.2. P e p t i d e S t a b i l i t y

Expe r imen ta l l y , the s t ab i l i t y of h G H R H ( 1 - 2 9 ) has been wide ly s tudied . C h r o m a t o g r a p h y ex-

pe r imen t s showed how some env i ronmen t condi t ions make the p e p t i d e d e n a t u r a t i o n and degra-

da t i on and t hen lose i ts biological act ivi ty , resu l t ing in degene ra t i ng in severa l r eac t ion p roduc t s .

The s tud ies also showed specific residues, where t he d e g r a d a t i o n s t a r t s to deac t iva t e t he pep t ide .

These res idues are A S P 3 (aspar t i c acid), A S N 8 (asparag ine) and M E T 2 7 (meth ionine) . In Ta-

1160 J. FENO et al.

ble 3 we summarize the possible degradation pathways, with respect to different conditions of temperature and pH values [3-6].

Table 3. A summary of degradation pathways of three residues.

Residue Degradation Pathways

ASP3 Hydrolysis (pH < 4, T = 55 ° C) Isomerization (pH > 4) ASN8 Deamidation due to structural and environmental factors (pH > 5)

MET27 Oxidation (auto-oxidation, chemical oxidation, photo-oxidation)

In addition, experimental stability studies have been performed by IRCS [7], subjecting hGHRH (1-29) to various treatments including different values of pH and temperatures, and in different solution conditions. The results have been collected by high pressure chromatography, measuring several features of the solution. The measured quantity in experiments is the degree of purity of the solute with respect to time.

Using ANOVA analysis, we evaluate the role played by the environmental factors which deter- mine the purity level of the solute. The collected data of purity levels vs. variables of the fourth column in Table 4 are shown in Figure 1. We can see the time evolution of solute purity for different solvents and temperatures. The slope of decay represents the rate of the degradation process, which seems to be constant. The ANOVA analysis tells us the dependence of the purity decay on various factors. In particular, we analyzed the dependence of purity to the single factor Temp, pH, Soluz, plus all the possible interactions among them.

In Table 5, we reassume the more relevant effects on the variability of solute purity. The validity of the analysis is confirmed by the R 2 value. The triple interaction among factors has no meaning, while the interactions with temperature are the more relevant. In fact, temperature variation seems to be the main factor that explains purity variability, followed by the type of solvent and pH. A mean test also showed that the major effect on purity, in the sense of degradation, is obtained raising temperature from 5 ° C to 25 ° C or from 5 ° C to 40 ° C and changing solution from 3 or 4 (freeze dried formulation) to 1, 2, 4, 5, or 6. Purity decay is also observed after lowering acidity (pH from 5 to 5.2 or 5.8). In Figure 2 all these effects are pointed out.

3. M O D E L L I N G T H E S T R U C T U R E

With all experimental results summarized in the previous section, we now turn our attention to molecular dynamics simulations. The bottleneck of such an approach lies in the fact that the spatial coordinates of hGHRH is not available, since no NMR or crystallography experiments have been carried out on the peptide, with an effective resolution (in [2] the studies was able to predict secondary structure but not three-dimensional conformation of the peptide). At the moment, no pdb entries exists regarding GHRH. Therefore, in order to carry out the MD simulations, we have to reconstruct this information, starting from the primary sequence.

This is a protein prediction structure problem, and can be possibly solved with three method- ologies: comparative modelling, fold recognition and ab initio methods [8]. We applied the first one, exploiting the web resources of CNRS Structural Biochemistry Center 1, which allows us to build a prediction algorithm with the software packages BLAST, TITO, and MODELLER:

1. First Step: We submitted the primary structure of hGHRH(1-29) to the software BLAST, commonly utilized to find sequence homologies among proteins. From BLAST, we found three homologue proteins with known three-dimensional structure (Table 6): Glucagon, the peptide which define the family of GHRH itself; Hormone2, an artificial analog of Glucagon; Neuropeptide 1GEA, a polypeptide responsible of adenylate cyclase in pituitary gland. Hormone2 peptide showed better homology. Even if this similarity is only about

lhttp://www.infobiosud.cnrs.fr/

Impact On The Stability Of hGHRH 1161

Table 4. A summary of experiments on the purity of the peptide with various tern- peratures, pH values and solvents.

Variable Explication

SOLUZ Type of solvent

TEMP Temperature (°C)

Tweek Time (in week)

pH Acidity

PURITY Purity of solute (~)

Values

1

2

3

4

5

6

5 °

25 °

40 °

0-24

4.9-5.8

0-100

Modality

3 mg/vial 5%Mannitol/0.3%m-cresol

7.5 mg/vial 5%Mannitol/0.3% m-cresol

3 mg/vial (saccharose) FD

10mg/vial (saccharose) FD

3 mg/vial 5%Nicotinamide/30%PropyleneGlycol/ 0.3% m-cresol

7.5 mg/vial 5%Nicotinamide/30%PropyleneGlycol/0.3% m-cresol

L o w

Mean

High

Integer

Real

Real

; . 96

5z Ln ,u 96

84

84

,n.= 96 f 9O

84

Purity of hGHRH(1-29): time evolution

~-.• . . 0 .. . . . . . . . ! . . . . . . . .

i i .

........ i ........ ! .......

D ~

0 0 0 . n . . . . . . . . . . . . . . . . . . . ~

• 0 •

:i i,1

• t

• i . . . . .....

• " . ...... : .........

i

8 16 24@ 8 16 24 0 | 16 24 0 8 16 ;~4 0 8 16 24 0 8 16 24

SOLUZ: SOLUZ: SOLUZ: SOLUZ: SOLUZ: SOLUZ:

1 2 3 4 5 G

T_WEEK Figure 1. Purity (in percentile) decay of hGHRH (1-29) vs. time (in weeks, Tweek, with various solutions as specified in Table 4).

30% in terms of amino acid identity, it seems higher if one consider the hydrophobic i ty of

the sequences (Table 7).

2. Second Step: The three homologues pdb files are submi t t ed to TITO (tool for incremental

th read ing opt imizat ion) . It evaluates the compat ib i l i ty of an aminoacid sequence with a

known 3D s t ructure (it validates the al ignment). In this way we produced a secondary

s t ruc ture predict ion of GHRH, on the basis of similari ty and of the known ter t ia ry struc-

tu re of the homologues. TITO also generated te r t ia ry s t ruc ture predict ion of G H R H and

the MODELLER input files. We neglected this last predict ion since it presented s t ructure

anomalies which we cannot control, bu t we utilized all the informat ion acquired to s tar t

the next step. 3. Th i rd Step: We submi t ted the GHRI-I sequence and the s t ruc tura l informat ion acquired

in the previous steps to MODELLER. I t is a p rogram for homology modell ing of protein s t ruc ture with satisfaction of spatial restraints. I t also performs energetic calculations to

1162 J. FENG et al.

Table 5. ANOVA results.

Factors d.o.f. F

SOLUZ 5 7.13

TEMP 2 13.98

pH 9 2.72

SOLUZ x TEMP 7 3.03

SOLUZ x pH 6 1.92

TEMP x pH 11 3.45

SOLUZ x TEMP x pH 6 0.77

Pr >/7 Significant

< 0.0001 ***

< 0.0001 ***

0.0112 **

0.0095 ***

0.0947 *

0.0012 ***

0.5952

9~

95

97

B.

94

g3

92

PURITY vs pH

i: , . . . . . !

4.9 5.0 5.1 5.2 5.3 5.4 5.5, 5.G 53 5.8

98

PURITY v s Solution

96.2

a. g5.6

95

94.4 Sl ~2 83 84 85 86

(a) (b)

98

97.5

97

96,5 >- --- 00 r,,

95.5

95

g4.5

94

PURITY vs Temperature

ii ii iii!i i iiii iiiiiiliiiii iill i i i i ii. 2~ 40

(c) Figure 2. Mean purity dependence from each factor level. Note the complex be- haviour with respect to pH (a), and the peak, corresponding to FD solutions (b). Temperature dependence clearly has a monotonic decrease (c).

validate the s t ructure modelled. For each homologue, MODELLER produced three spatial

s t ructures of GHRH. Each of the nine s t ructure obta ined has a score, by which we can list

them in a descending order. The score expresses the reliability of the prediction, therefore

we decided to const ruct the MD simulation using the first s t ruc ture predicted.

To validate the selection of the final pept ide spatial coordinates, we took into considerat ion

tha t they express a secondary s t ructure comparable wi th the exper imental results (see Table 2 and [2]). This spatial configuration, in P D B format, represents the initial condit ion of the MD

simulation.

Now we can in t roduce our theoret ical approach in an operat ive way. We have defined a protocol

consisting of some well defined steps which also implies the use of powerful parallel computers . The s tar t ing point of such protocol, clearly, is the P D B file, the file wi th a t o m spatial coordinates.

From it we can const ruct all the conditions needed to begin an MI) simulation. Ei ther it comes

from experiments or from modelling, the P D B file mus t also contain informat ion about the pH of the solution. This because it must contains the acid or basic configurat ion of the residues corresponding to a certain pH.

Thus, after the definition of pH, the protocol proceeds as follows in Sections 3.1-3.3.

Impact On The Stability Of hGHRH

Table 6. BLAST Homologies. Primary sequence, homology and PDB code are shown here.

1163

Name PDB code Homology AA sequence

hGHRH (1-29)

Hormone2

Glucagon

Neuropeptide

(IBHO) (~CCN)

(IGEA)

28%

28%

35%

YADAIFTNSYRKVLGQLSARKLLQDIMSR

HSQGTFTSDYSKYLDSKKAQEFVQWLMN-

HSQGTFTSDYSKYLDSRRAQDFVQWLMN-

HS Q G T F T D S Y S R Y R K Q M A V K - -

Table 7. The hydrophobicity in the homology with Hormone2.

N

1

2 +

3 A

4 -{-

5 +

6 ÷

7 ÷

8

9

10

11 B

12 B

13 ÷

14 -{-

15

16

17 -{-

18

19 ~-

2O B

21 B

22 ÷

23 -t-

24

25 A

26 +

27 -I-

28

29 B

GHRH (1-29) Hormone2

Acidity Hydrophobicity Acidity Hydrophobicity Overlap

B

+ ÷

+

÷

÷

+

÷

+

3.1. P r e p a r a t i o n o f t h e S o l u t i o n

1. DEFINITION OF THE BOX. Doing th is means also to check the shape of t h e pro te in , in order

to avoid t h a t p ro t e in ex t remes , dur ing t ime evolut ion , get t oo close or touch the bounda r i e s of

the box. We chose a cubic box of a lmos t 20/~edge and the center of mass of p e p t i d e coincides

wi th the cen te r of t he box.

2. BUILD UP OF THE SOLUTION. T h e pep t i de is su r rounde d by wa te r molecules and counter

ions in o rder to neu t ra l i ze t he solut ion. The n u m b e r of t h e molecules is such t h a t t he dens i ty is

in accordance w i th t he van der Wal ls exc luded volume. This , however, requi res cons t an t pressure

equ i l ib ra t ion .

1164 J. FENG et al.

3. CONSTRUCTION OF TOPOLOGY INFORMATION AND SIMULATION COMPATIBLE COORDINATES ABOUT ALL THE ATOMS IN THE BOX. This procedure is software dependent and characterizes the package used to realize the simulation. In our work, we used the AMBER 6.0 package, a powerful molecular dynamics software, running on high performance parallel machines. It is an high level optimized software, by which it's possible to construct simulations with very simple commands, but it requires transformation of experimental information (i.e., PDB coordinates) to its own formats.

3.2. Equilibration of the Solution

This phase is particularly important to have an acceptable behaviour of the dynamics. In fact, when we build up the solution (point (lb)), we randomize the water molecules in the box, but we have to be sure that solvent molecules are placed at a correct distance between them and the peptide, in order to observe the excluded volume rule. Moreover, peptide coordinates could not correspond to a configuration of minimal energy, so they don't represent a stable situation. In order to realize an effective equilibration, we have to consider the following points.

1. Random displacement of water molecules can highly increase the potential energy and for conservation, also their kinetic energy can strongly increase, transforming the molecules in dangerous bullets for the peptide structure. This implies that water equilibration requires a shorter time scale with respect to peptide stabilization. Then, during the first stage of equilibration, peptide structure must be constrained.

2. Equilibration and stabilization correspond to a total energy minimization of the system, hence this phase is characterized by an algorithm that proceeds towards energy minima. Usually it represents an optimized version of the steepest descent one, but it is subjected to local minima traps. To reduce the possibility of local minima, sometimes dynamical shocks are interposed to minimization procedure.

3. The alternation of dynamical shocks and constrained minimization must go on until the system can be considered enough stabilized. In the hGHRH(1-29) equilibration we needed twelve steps of both shocks and minimization.

3.3. Dynamics

This is the core of the methodology. It produces all the information about the system com- ing from microscopic evolution. We can examine the dynamics from two points of view: the operational and phenomenological ones.

1. In practice, the realization of the dynamics corresponds to the numericM integration of motion equation for each atom contained in the chemical solution. In our case this means to solve a system of almost 40,000 differential equations. For this reason we need high performance parallel computing. The numerical integration of the equations has a time step of two femtoseconds and we follow the trajectories of the atoms for 200,000 steps, corresponding to 400 picoseconds. The high number of degree of freedom could produce an enormous quantity of information if we save them with a low time step. However, some phenomena require different time scale to be analyzed, but we must optimize the space to store information of several simulations, so we decided to save trajectories, from which we can extract dynamical information, with a time step of one picosecond. As technical details, each simulation requires 1Gb of dynamical memory (RAM). Consequently, each simulation has been split in 4 phases of 50,000 steps each (100 picoseconds). Each phase requires seven to eightcomputing hours for completion, and produces a 120 Mb data file. After completion of the four phases, all information can be processed to build up the total trajectory of the system. Simulations are performed on IBM parallel machine SP3 (16 CPU).

Impact On The Stability Of hGHRH 1165

H R Peptide / H I torsion

I angles. C elphe !

!1 o o0" / \ 0 R H

Figure 3. Dihedral angles are extremely important as dynamical variable to express residue flexibility.

.

(a) (b)

Figure 4. Spatial structure of hGHRH (1-29) from MODELLER prediction (homology with hormone2). Colors express atom type (a). Cartoon visualization, useful to enhance c~-helices (b).

The phenomenology investigated by dynamics involves temperature and pH dependence. We defined two kind of solution, in terms of pH: one with pH < 6, according with the

electronegative configuration of aspartic acid, which we called acid and one with 6 < pH < 7, which we called neutral. The first solution simulated at room temperature T = 300 ° K, the second one, instead, has been subjected to three levels of temperature: Tlow = 280 ° K, T = 300 ° K and Thigh : 323 ° K. The consequent phenomenology can be valuated by means of its dynamical origin, so we can investigate how temperature and pH influence the fluctuation of the atomic structure, which residues are more flexible or radically change their position, what is the behaviour in terms of dihedral angles (see Figure 3) and so on.

4 . R E S U L T S

To synthesize the information produced by the simulations, we consider indicators by which it

is possible to analyze the evolution of the structure with respect to environment conditions (pH

and temperature) . The indicators areas follows.

• RMSD (root mean square deviation) of total structure with respect to a reference con- figuration that we chose to be the initial condition of the dynamics. The deviation is computed after a fitting procedure that superimposes the peptide configuration on the reference configuration, in order to avoid overestimation due to translations or rotations of the entire structure. In particular, we have chosen to apply the fitting procedure to

1166 J. FENG et al.

the total protein except hydrogen atoms (Protein-H) and evaluate RMSD of the same structure and main chain. The average is computed on all atoms of the peptide, for each frame of the t rajectory (one picosecond).

• RMSD per residue. This is a time averaged deviation with respect to the reference con- figuration, along the equilibrated trajectory, evaluated for each residue. It is useful to obtain the information on how the residue has moved away from the reference position, in a certain simulation, with a definite temperature and pH.

• Dihedral order parameters. They are indicators which assume values between 0 and 1

and are related with dihedral angles of each residue. Averaged over the trajectory, they express an effective estimate of the flexibility of the residue, being function of dihedral

angles ~ and 9.

• Ramachandran Plots represents a direct explanation of the results expressed from dihedral order parameters. In fact they are graphical representations of the (~, 9) space visited

by the residue during the simulation.

Figure 5 show the RMSD behaviour vs. time for the simulated solutions. The two curves

refer to Protein-H and MainChain RMSD. The difference between them expresses the side chain

contribution. During the first 150 picoseconds in all the simulations RMSD grows up, as a

0.4 0.4E

0.35

0.3

=~. 0.25

0.2

~" 0.15

0.1

0.0, =

0.45

0.4

0.35

A 0.3

0.25

a: 0.2

0.15

0,1

0.050

RMSD (after Isq on ProteinH)

| 5O

[ ~ ProteinH ] MainChaLn

i i i i i 100 150 200 250 300 350 400

Time (ps)

(a)

RMSD (after Isq on ProteinH)

0.,~

0.~

A O.E

0.2E

a: 0.;

0.1E

0.1

0.0E

0.4E

0.,~

0.3E

0.2E =E

0.~

0.I~

RMSD (after Isq on ProteinH)

E± ProteinH MalnChaln

1;0 1;o 2~ do 3~o 3~ Time (ps)

(b) RMSD (after Isq on ProteinH)

. . . . A [ -

Figure 5. RMSD of simulation in acid solution T = 300 ° K (a). RMSD of s imulat ion in neutral solution T ---- 300 ° K (b). RMSD of simulation in neutral solution T ---- 323 ° K (c). RMSD of simulation in neutral solution T = 280 ° K (d). These graphs show t ime evolution of root mean square deviation of the peptide. It expresses the mean deviation of the instantaneous atomic conformation with respect to initial conditions. During the first period of about 150 ps, the peptide is still relaxing, while in the last 250 ps it begins to fluctuate around a stable conformation.

[ ~ ProteinH 0.1 --=- MainChain

Time (ps) Time (psi

(c) (d)

do ~o 3~o 400

Impact On The Stability Of hCHRH 1167

0.8

0.7

0.6

A 0.5 E

~o., ¢ 03

0.2

0.1

RMS Deviation (Temperature dependency)

-=-- T=300K - - - T=323K

T=280K

; 1; 1; 2"0 2"5 Residue

(a)

0.7

0.~

0.5

~ O.l

~ 0.~ n"

0.2

0.1

RMS Deviation (pH dependency)

• - - - nez~al pH acdpH

i

i !

5 ;o 1'5 20 2'5 Residue

(u)

Figure 6. RMSD per residue. In this way we can observe the mean deviation of each residue with respect its initial conformation. The two panels express temperature (left) and pH (right) dependence of such deviation. For the first case, looking at the known fragile points of the chain (residues 3, 8 and 27), it is clear that temperature variations have a minimal effect on RMSD. The transition from acid to neutral pH instead produces a sensible variation of the peaks. Neglecting the tails deviation, the figure on the right side shows how ASN8 deviation became higher than that of LYS12, at neutral pH.

consequence of a further equilibration. After this, the indicator begins to fluctuate around a mean level as in equilibrium. Therefore, for what concerns further analysis, we considered the remaining 250 picoseconds of the trajectory. If we evaluate RMSD per residue, we obtain the results depicted in Figure 6.

Here we can observe some important features: the ASN8 residue is one of the fragile amino acids pointed out by the experiments (see Table 3) as the starting point of degradation reactions. In Figure 6b, ASN8 shows an enhancement of the deviation, when the pH is raised from acid values to neutral values. In particular, in neutral pH its deviation corresponds to the higher value, if we exclude the head and the tail of the peptide, that are usually more mobile. Instead, for acid pH, the ASN8 deviation is only a normal peak, being comparable with other residues. If we observe the temperature dependence of such indicators (Figure 6a), we find that the peak corresponding to ASN8 is one of the higher, with little fluctuations changing the temperature.

1

0.8

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Dihedral Order Parameters (acid pH)

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

Dihedral Order Parameters (neutral pH)

|

nt #¢ $

|

5

Figure 7. Dihedral order parameters as functions of ~ and • here are calculated for each residue for both acid and neutral solutions, at T = 300 ° K. The effect of pH increase is to intensify the angle fluctuations of residues 4, 5 and 6.

Residue Residue

(a) (b)

1168 J. FENG et al.

Ramachandran Plot ALA4 Neutral pH Ramachandran Plot ALA4 acid pH

1 5 0 ~ 150[

~, 50 ~, 50

!ot t!0t -150 : i "150 t

"150 "100 "50 0 50 100 150 / "1513 "100 "50 =' 0 510 Phi (degrees) Phi (degrees)

( a ) ( b )

Figure 8. Analyzing with Ramachandran plots the trajectory of the residues more involved in dihedral angle fluctuations, we can observe different paths toward equi- librium in agreement with neutral (left) and acid pH (right). The ALA4 • angle expresses an oscillating regime for neutral pH that never appear in the acid pH trajectory. This delays the convergence to equilibrium conformation, that can be observed for both graphics around the (-50,-25) point.

100 150

15(] a ~

1IN - - 1"i,

~ 5(]

5O

100

150 ~

150 1

Ramachandran Plot ILE5 neutral pH

iii./ 100 50

15(

10f

J

~- s o

IO0

150

Ramachandran Plot ILE5 acid pH

..... e,,,,, 9

d~'u., a : ' . ' " ,.~o

100 150 150 100 5 0 100 150 Phi(degrees) Phi(degrees)

(a) (b)

Figure 9. Also for ILE5, similar initial conditions ((-137, 172) and (-146,168) respectively for left and right trajectory) produce different behaviours for neutral (left) and acid pH (right). The visited phase space increase in terms of @ angle, and the oscillating regime appears. Also the equilibrium conformation is different, expressing a transformation of the energy profile for higher value of pH.

Thus, the eighth residue change configurat ion if we raise pH, f rom acid to neutral , bu t its new

configurat ion seems to be t empera tu re independent.

Wi th the analysis of RMSD per residue, we cannot obta in informat ion on residue flexibility. To verify which are the more flexible points of the peptide, we have to consider dihedral order

parameters . Looking at Figure 7, we can observe tha t the par t cons t i tu ted by residues 4, 5, and 6

is the more flexible and when we go toward neutral pH (from (a) to (b)) this flexibility s trongly increase. We can explain bet ter these consideration, const ruct ing the R a m a c h a n d r a n plots for

the three residues.

Figures 8-10 represent the explicit confirmation of the results expressed by dihedral order

parameters . For the three residues considered we can observe the increase of the possible values

assumed by the two angles as a consequence of the lowering of solution acidity. This means tha t the rota t ions a round the a lpha-carbon bonds can be wider, involving more possibilities for

Impact On The Stability Of hGHRH

Ramachandran Plot PHE6 neutral pH

150 t

g' o o a~ 1

50 ' r n K . . . . . . . . . . . . . . . . . . . . ' ' 'n nn n ',%l$i, nn % n% I q " n .%~ ] ~ , . o . ..........

150 ), i i , i i n i |

150 100 50 0 50 100 150 Phi (degrees)

150

100

.~ 50

o. 50

100

150

Ramachandran Plot PHE6 acid pH

..~o, e* ' i ° • ." 'a ..." e6 "-. , , , ~

Phi (degrees)

(a) (b)

Figure 10. In the case of PHE6, Ramachandran plots emphasize a different behaviour in 4) angle with respect to the pH values considered. Even if we cannot speak of a proper oscillating regime in the case of neutral pH, the residue spent most of the time fluctuating around a metastable conformation before to converge to the equilibrium one,

1169

hydrogen bonds modification and exposure to solvent interaction. Moreover, considering the

hypothesis that connect the stability of a protein to its primary sequence, it seems to us that it's not a chance that the higher flexibility is concentrated in a part of the sequence that lies between

ASP3 and ASN8. These two fragile residues are more subject to the degradation reactions that could be understood with these dynamical transitions.

5. C O N C L U S I O N S

The problem of protein stability is one of the fundamental question of pharmaceutical produc- tion and protein engineering. We tried to formulate a methodology based on molecular dynamics, in order to exploit the great quantity of information produced by such technique. The goal is to find in the microscopic evolution of the protein the onset of its instabilities. The results we obtain applying the methodology to hGHRH(1-29) in water solution, underline three main effects.

1. The ASN8 deviation with respect its initial configuration has a transition when the pH of

the solution increase. 2. The deviation assumed by ASN8 at neutral pH is stable with respect to temperature

variation. 3. The fragment 4-6 of the sequence shows a higher flexibility with respect to the other

residues. Moreover, such flexibility increases with the pH.

We stress the fact tha t these effects were measured on the known criticM part of the protein. In fact, as we know from experiments, two of the three fragile residues of hGHRH belong to the first ten residues of the sequences: the ASP3 and the ASNS. The latter, in particular, undergoes an increase of its RMSD that could be due to a change in hydrogen bonding interaction, mediated by solvent molecules. The flexible part 4-6 is exactly between the two fragile residues 3 and 8. The increase in its chain rotation following the raise of pH could produce an higher exposure to solvent

interaction of the two fragile points. These should be considered initial results, with respect to all the possible information obtainable from microscopic trajectories. They must be interpreted also taking into account that initial structure has been built up by modelling procedures and its secondary structure is not equal with that of experiments. Finally, for what concerns the molecular dynamics protocol, we want to stress also its fast application that makes possible to have preliminary results in one months, if the starting information (three-dimensional structure, secondary structure, etc.) are available.

1170 J. FENG et al.

In the frame of a molecular dynamics application, we try to construct a new method of in- vestigation on protein stability and in order to improve its reliability, it is important to define what are the real main information that molecular dynamics needs. Yet, we have seen above that molecular dynamics works with atomic coordinates and velocities, within a classical Physics frame, where atoms obey to Newtonian forces and equations. Considering this and the great number of degree of freedom involved in a MD simulation, chemical reactions like deamidation, oxidation, isomerization, hydrolysis, cannot be directly observed, since they obey to quantum theory. Therefore, experimental information on such reactions are useful but not crucial. More- over, long time behaviour or phenomenology that requires time scale longer than microseconds, will never be comparable with molecular dynamics results, since it considers microscopic time scales. Otherwise, structural information about protein, obtained from NMR experiments or high resolution crystallography, can strongly influence the simulation. Detailed experimental results on atomic space coordinates, secondary and tertiary structure properties, solvent interaction in- formation, structural modification as a consequence of pH or temperature variations and so on, are all elements by which a molecular dynamics application can become a very powerful tech- nique. Consequently we want to stress that the absence of such experimental examination, makes simulation results less effective.

R E F E R E N C E S 1. P.D. Tieleman, Atomistic Simulations of Ion Channels, In Computational Neuroscience: A Comprehensive

Approach, (Edted by J.F. Feng), Chapman and Hall/CRC, Boca Raton, FL, U.S.A., (2003). 2. G.M. Clore, S.R. Martin and A.M. Gronenborn, Solution structure of human growth hormone releasing

factor. Combined use of circular dichroism and nuclear magnetic resonance spectroscopy, J. Mol. Biol. 191, 553-561, (1986).

3. A.R. Friedman, A.K. Ichhpurani, D.M. Brown~ R.M. Hillman, L.F. Krabill~ R.A. Martin, H.A. Zurcher-Neely and D.M. Guido, Degradation of growth hormone releasing factor analogs in neutral aqueous solution is related to deamidation of asparagine residues, Int. J. Peptide Protein Res. 37, 14-20, (1991).

4. C.L. Stevenson, A.R. Friedman, T.M. Kubiak, M.E. Donlan and R.T. Borchardt, Effect of secondary structure on the rate of deamidation of several growth hormone releasing factor analogs, Int. J. Peptide Protein Res. 42, 497-503, (1993).

5. J. Bongers, E.P. Heimer, T. Lambros, Y.E. Pan, R.M. Campbell and A.M. Felix, Degradation of aspartic acid and asparagine residues in human growth hormone releasing factor, Int. J. Peptide Protein Res. 39, 364-374, (1992).

6. K. Breddam, F. Widmer and M. MeldaJ, Amidation of growth hormone releasing factor (1-29) by serine carboxypeptidase catalysed transpeptidation, Int. J. Peptide Protein Res. 37, 153-160, (1991).

7. S. Pallotta, Stability studies, IRCS Internal Review, (1999). 8. M.J.E. Sternberg, P.A. Bates, L.A. Kelley and R.M. MacCallum, Progress in protein structure prediction:

assessment of CASP3v, Curt. Op. Struet. Biol. 9, 368-373, (1999). 9. A. Yang and B. Honig, On the pH Dependence of Protein Stability, J. Mol. Biol. 231, 459-474, (1993).

10. G.J. Kleywegt and T.A. Jones, Databases in protein cristallography, Acta Cr~ist. D54, 1119-1131, (1998). 11. C.N. Pace, B.A. Shirley, M. Menutt and K. Gajiwala, Forces contributing to the conformational stability of

proteins, FASEB Journal. 10, 75-83, (1996).