chapter 2: protein folding

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Chapter 2

REVIEW OF RELATED LITERATURE

History

Cases of diseases in what appeared to be protein misfolding and aggregation disorders

had been described during the 17th

century (Kyle, 2001). Rudolf Virchow coined the term

amyloid to describe the structural bodies of deposited tissue in the brain that reacted to the

iodine solution in combination with hydrated sulfuric acid. These deposits, termed wax-like

or lardaceaous, was believed to be cellulosic in nature. It was not until Fritz Friedrich and

August Kekule dissected and performed extensive chemical tests on amyloid-rich segments

of spleen that they found that amyloid is protein in nature (Westermark, 2005).

In 1907, Alois Alzheimer reported senile plaques and neurofibrillary tangles in the

neocortex and hippocampus of a middle-aged woman with memory deficits and progressive

loss of cognitive function, a disorder later known as Alzheimers disease (AD) (Forman et

al., 2004). Five years after, in 1912, Lewy bodies, neuronal cytoplasmic aggregations

containing misfolded fibrillar-synuclein (Jelliger, 2007) that was the pathological hallmark

of Parkinsons disease (PD) was described by Friedrich Lewy (Forman et al., 2004).

Spielmeyer in 1922 used the term Creutzfeldt-Jakob disease to describe a human

neurodegenerative disease described in earlier reports by Hans Gerhard Creudtzfeldt and

Alfons Maria Jakob (McKintosh et al., 2003). This term later became used to describe the

human form of bovine spongiform encephalopathy (BSE). Both BSE and CJD and its

variants belong to the group of disease known as transmissible spongiform encephalopathy

(TSE). Creudzfelt-Jakob disease, Gerstmann-Strussler-Scheinker disease (GSS), kuru, and


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amyloids has been described in the case of amyloid- peptide in AD, -synuclein in PD, the

polyglutamine stretch of huntingtin in Huntingtons disease and prion proteins in prion

diseases.

Prions are self-templating elements known to be transmissible between individuals and

even cross the specie barrier via the food chain (King et al., 2012). The prion hypothesis

(also known as the protein-only hypothesis) postulates that infectious agents that cause TSE

contain no nucleic acids, but are instead a post-translationally modified form of the native

protein, possibly of different structural conformation. Upon introduction to the host, the

misfolded protein form induces the conversion of the normal protein to the misfolded form

(Aguzzi et al., 2008).

However, not all PMD exhibit the infectivity shown by prion diseases, with its ability

to cause widespread epidemics by transmission between individuals, and even with members

of different species as with the case of BSE. Amyloid-forming proteins that exhibit

infectivity with neighboring proteins or with neighboring cells, but not between individuals

are calledprionoids. (Aguzzi and Rajendran, 2009). In other words, prionoids are proteins

that exhibit the misfolded and self-templating capability of bonafide prions but the

transmission of prionoids, unlike prions that can cause widespread epidemic and even cross

specie barrier, are restricted within the tissue or the individual. Natural transmission of

prionoids between individuals is yet to be observed but it is possible in experimental models

(King et al., 2012). Regardless of these differences, both the amyloid-forming prionoid and

highly infectious prion proteins propagate through their -sheet-rich structure, forming

nucleates and subsequently growing and polymerizing by recruiting and converting their

soluble protein conformers (Alberti et al., 2009).


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Studies on yeast prions led to the observation that these proteins share a common prion

domain of about 60 amino acid in length rich in uncharged polar residues asparagine,

glutamine and tyrosine as well as glycine (Alberti et al., 2009, King et al., 2012). These

yeast prion domains, when over-expressed in proteins, increased priongenicity of the

resulting protein while deletion of this domain renders the protein incapable of accessing its

prion state (King et al., 2012). Furthermore, this domain is shown to be portable; for instance,

appending the prion domain of the yeast prion prtein Sup35 to a reporter gene such as -

galactosidase or green fluorescent protein (GFP) confers prion behavior (King et al., 2012).

Furthermore, closer analysis of these prion domains showed that certain key amino acids play

a significant role in protein behavior (Alberti et al., 2009, King et al., 2012). It was

previously believed that asparagine and glutamine residues equally contribute to prion

behavior (King et al., 2012), but a study made by Alberti and colleagues (2009) showed that

aggregation-prone prions have asparigine-rich prion domains while glutamine, proline and

charged residues were abundant in non-aggregating prion domains. Furthermore, the spacing

of amyloid breaking prolines and charged amino acids contributes significantly to prion

behavior (Alberti et al., 2009). This and other studies pointed to the importance of

asparagines in the formation of toxic oligomeric species and glutamines in the formation of

self-templating prions (King et al., 2012).

Mechanism of Protein Folding and Misfolding

Central to PMDs are the change in protein conformation and aggregation. But how do

proteins attain such conformations? Christian Anfinsen, based on his studies of ribonuclease

A, introduced the thermodynamic hypothesis, stating that the three dimensional structure of a

protein in its physiological environment, that is, in consideration with the solvent, pH, ionic


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strength, temperature and presence of other solutes, is the one which the Gibbs free energy of

the system is the lowest (Anfinsen, 1973), and that the proteins conformation is encoded in

the physicochemical properties of the amino acid sequence. Cyrus Levinthal pointed out that

a distinct, predetermined pathway must be in play, or else the native state of protein would

not be found within reasonable time by any random search through conformational space,

known as Levinthals paradox. Furthermore, it is also implied that the proteins native

conformation may not necessarily by its lowest energy state, and many proteins adopt a still

lower energy conformations as a molecular aggregate, such as amyloids (Englander et al.,

2008).

According to Levinthal, a protein folds by following an energetic funnel a funnel-

shaped energy landscape that allows protein to fold to its most stable conformation (Figure 2.

1). The funnel-shaped landscape is brought about by initial local interactions of a proteins

amino acids, limiting the conformational space a protein must explore to fold properly

(Renaud, 2010).


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Figure 2. 1 Levinthal's Energetic Funnel

Using Levinthals concept of the energetic funnel, specific folding intermediates have

been successfully found and observed in real time, most notably by Robert Baldwin and T. E.

Creighton. (Englander et al., 2008). Baldwin characterized two types of folding patterns for

proteins. The hierarchic model describes folding as a process beginning with structures that

are local in sequence and marginal in stability. These local structures interact to produce

intermediates of ever increasing complexity that ultimately grow to its native conformation.

On the other hand, the non-hierarchic model is a process where tertiary structures stabilizes

and determines local interactions (Baldwin and Rose, 1999). The hierarchic model eventually

became known as the framework model. The hydrophobic collapse model stems from the


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non-hierarchic model, postulating that the hydrophobic effect is the main driving force of

folding, starting with the collapse of the chain and eventual formations of secondary

structures (Szilagyi et al., 2007). In an attempt to unify the two models, the nucleation-

condensation model was proposed. This model postulates that the overall structure condenses

around an element of structure, the nucleus, that itself consolidates during the condensation

(Itzhaki et al., 1995). Long range and other native hydrophobic interaction form in the

transition state to stabilize the weak secondary structure, taking a route that is somewhere

between the framework and hydrophobic collapse models (Szilagyi et al., 2007).

Studies to detect prion domains

The existence of prion domain in yeast proteins prompted the scouring of the human

genome for proteins bearing the same or similar domains.

Studies of prions in yeast showed that these proteins tend to have common distinctive

domains in the amino acid sequence rich in asparagines, glutamine and glycine (King et al.,

2012). A scan of the human genome made light of several RNA-binding proteins that tend to

have these domains and rich in the amino acids suspected to be involved in prion formation.

The study made by King et al. (2012) yielded four RNA-binding proteins with particularly

high susceptibility to prion and other misfolding diseases diseases.

FUS

FUS is a multifunctional protein component of the nuclear ribonucleoprotein complex

involved in pre-mRNA splicing and export of mature mRNA in the cytoplasm. The function

of this particular protein is to bind both single-stranded and double stranded DNA and

promote ATP-independent annealing of complementary single-stranded DNA. It is believed

to play a role in maintenance of genomic integrity. Mutations in this protein is shown to be


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involved in familial as well as sporadic Amyotrophic Lateral Schlerosis (ALS) and Fronto-

Temporal Lobar Degeneration with Ubiquitin-positive inclusions (FTLD-U), and aggragation

of this protein is linked to Huntingtons disease and spinocerebellar ataxia.

TDP-43

TDP-43, also referred to as TRDBP (TAR DNA binding protein) is a DNA and RNA

binding protein that acts as a repressor. It is also associated with ALS and other

neurodegenerative diseases. It can adopt specific isfolded forms that are highly toxic, and

recently, it has been surmised that different RNA molecules can induce TDP-43 to take on

different conformations or strains. Its aggregates might also sequester essential RNA

molecules and further promote neurodegeneration.

EWSR1

EWSR1 is a multifunctional protein that is involved in various cellular processes,

including gene expression, cell signaling, and RNA processing and transport. The protein

includes an N-terminal transcriptional activation domain and a C-terminal RNA-binding

domain. Recently, it has also been linked to FTLD-U. it forms cytoplasmic aggregates and is

toxic in yeast.

Intrinsic disorder in proteins

All of these models rely on the specific physicochemical properties of the proteins

primary structure to dictate its final conformation. However, proteins are flexible

macromolecular structures, and this flexibility allows them to perform their specific tasks

within the cell. Some proteins, especially those involved in the regulatory pathways of higher

eukaryotes display these characteristics. Known as intrinsically disordered proteins (IDPs),

these proteins exists as an ensemble of rapidly interconverting conformations that resemble


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denatured states (Tompa and Han, 2012). Some of IDPs have been implicated in PMD, such

as -synuclein and the prion protein (Csizmok and Tompa, 2009, Tompa and Han, 2012).

Since amyloid formation is mainly characterized by an extended hydrogen bonding

interaction between the backbone amides in a cross -sheet formation, IDPs, with their

unstructured state and exposed backbone, are especially prone to aggregation. Indeed, in

studies of protein aggregation of a range of mutants under conditions favoring the unfolded

states of globular proteins, it was found that amyloidogeneicity shows a significant positive

correlation with hydrophobicity and -sheet forming potential, and negative correlation with

total charge. Furthermore, IDPs are also found to be depleted in order-promoting amino acids

WCFIYVL and enriched in dirsoder-promoting amino acids KESPQRA (Csizmok and

Tompa, 2009).

Aggregation and amyloid formation

There are three existing models that describe the molecular mechanism of protein

misfolding and aggregation based on kinetic modeling of protein aggregation (Estrada et al.,

2006, Soto, 2001). The polymerization hypothesis postulates that protein oligomers that act

as seeds that induce protein misfolding (Soto, 2001). First, there is a slow formation of an

ordered nucleus due to the unfavorable interaction between the native monomeric

conformation followed by polymerization and a growth phase, where the growing amyloid

recruits native protein and induces it to misfold (Estrada et al., 2006). Hence, in this model,

misfolding is a consequence of aggregation. Recent publications cited the template-directed

(heterodimer) model (Apetri, 2004) developed from studies of prioon proteins which closely

resembles the polymerization hypothesis of protein aggregation (Figure 2. 2). As such, both

models stressed the presence of a misfolded seed as a necessary requirement for protein


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misfolding and aggregation. It further postulates that there is a high activation energy barrier

preventing a spontaneous conformation change. Instead, the misfolded prion protein forms a

heteromeric complex with the functional protein. Subsequently, the complexation promotes

the conformational change in the functional protein, resulting to two misfolded proteins

homodimers. The dissociation of the homodimer to two misfolded prion monomer that can

catalyze conformational changes in native proteins is the final and rate-limiting step in this

model (Aguzzi et al., 2008, Apetri, 2004). Their difference lies in the function of the

amyloid. For the polymerization hypothesis, the amyloid induces the misfolding whereas the

template-directed refolding model describes it merely a by-product of misfolding.

Figure 2. 2 Template- directed refolding (top) and polymerization hypothesis (bottom)

An alternative to the polymerization hypothesis, the conformational hypothesis,

postulates that the protein spontaneously converts from its native conformation and its

aggregation-prone form without the need for an inducer or outside interference. The


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aggregation-prone forms then interact with one another to form amyloid. Hence, aggregation

is a consequence rather than the cause of the disease (Soto, 2001). Likewise, this is mirrored

in the more recent paper by Apetri (2004) and Aguzzi (2008). Called the seeding (nucleated

crystallization) model, it presents a thermodynamically-controlled conversion of the native

functional protein to the misfolded prion fom. The conformational change is a reversible

process and the native functional form is favored. The prion form is favored only when it

forms a complex with other misfolded form, leading to aggregation. Once the seed is

established, the formation of the misfolded conformation is accelerated. Supporting evidence

for the spontaneous interconversion of the folded protein and its misfolded conformation is

suggested by the discovery of the IDPs (Section 3.2).

Figure 2. 3Seeding (top) and conformational hypothesis (bottom)

The third model is intermediate between these two forms. Slight conformational

changes result in the formation of an amyloidogenic intermediate, which is unstable in an

aqueous environment because of exposure of hydrophobic segments to the solvent. This

unstable intermediate is stabilized by intermolecular interactions with other molecules


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forming small -sheet oligomers, which by further growth produce amyloid fibrils. In this

model the conversion of the folded protein into the pathological form is triggered by

structural changes, but complete misfolding is dependent upon oligomerization (Soto, 2001).

This is termed the conformation/oligomerization hypotheis (Figure 2. 4).

Figure 2. 4 Conformation/Oligomerization Hypothesis

Molecular Dynamics

Experimental determination of the folding mechanism and early aggregation steps were

difficult to obtain, hence computational approaches were used to obtain an insight into these

mechanisms. This is due to the metastable and short-lived nature of soluble pre-fibril

oligomers at the early step of fibril formation (Jiang et al., 2009). Hence, computer

simulations are carried out in the hopes of understanding the properties of molecules in terms

of their structure and the microscopic interactions among them (Allen, 2004). The general

process of describing complex chemical systems in terms of realistic atomic models, known

as molecular modeling, has the ultimate goal of understanding and predicting macroscopic

properties based on detailed knowledge on the atomic scale (Spoel et al., 2010). There are

two main methods in molecular modeling: Monte Carlo simulations and Molecular

Dynamics simulations. Molecular Dynamics, which is the focus of this study, is appropriate

for generation of non-equilibrium ensembles for the analysis of dynamic events. It consists of


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numerical, step-by-step solution of Newtons classical equation of motion for a system with

Ninteracting atoms:

=

,

= 1

(2.1)

The forces Fare the negatve derivatives of a potential function V(r1, r2, , rN)

= (2.2)The coordinates of the atoms as a function of time represents the trajectory of the

system. The average of the equilibrium trajectory gives an idea as to the macroscopic

properties of the system (Spoel et al., 2010).

In reality, atoms, consisting of the nucleus, electrons and photons that interact through

electromagnetic and gravitational forces, and obey Dirac and Schrodingers equations

(Berendsen, 2004). In order to simulate a large number of atoms in a system, molecular

dynamic simulations had to employ several assumptions. First is the application of classical

mechanics to describe atomic motion (Spoel et al., 2010). Although most atoms, especially

the heavier ones, behave classically at normal temperatures, hydrogen and deuterium atoms

behave quantum-mechanically at 300 K while electrons are fully quantum-mechanical in all

cases (Berendsen, 2004). Furthermore, there were suggestions from literature (Berendsen,

2004, Berendsen et al., 1995, Spoel et al., 2010, Spoel et al., 2005) that hydrogen bonding

occurs by quantum tunneling. Equations of classical harmonic oscillators also vary

appreciable from the real quantum oscillation that occurs in atomic bond vibrations. This

limitation has been corrected by the use of corrections in the classical harmonic oscillator or

the use of bond constraints. Specifically treating bonds and bond angles as constraints in

equations of motions is advantageous since quantum oscillators in ground state resemble


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bond constraints more than they do classical harmonic oscillators. Furthermore, the

application of this allows the algorithm to use larger time steps without the system blowing.

Another assumption is that the electrons are in the ground state (Spoel et al., 2010).

Hence, the force field accounts for the atoms only and no reactions take place. This is based

on he Born-Oppenheimer Approximation stating that the wave function of the electron is

affected by the location of the nucleus, hence the computation of the wave function of the

electron is limited by the position of the nucleus (Berendsen, 2004).

Molecular dynamics also rely on the use of force fields. The calculations of bonded and

non-bonded forces and interactions as well as the parameters used are dictated by the type of

force field implemented. Also, force fields have the capacity of being pair-additive (Spoel et

al., 2010); all non-bonded forces result from the sum of all non-bonded pair interactions.

This, however, pose a problem for large systems, as non-bonded energies tend to hit off the

charts. Therefore, long-range interactions are cut-off and periodic boundary conditions are

implemented to prevent unrealistic ballooning of the energy of the system. GROMACS 4.5.4

especially uses a cut-off radius for Lennard-Jones interactions as well as for Coulombic

interactions.

Force field

Force field is a set of terms describing various forces and interactions in a molecule,

namely, bond-stretching, bond-bending, electrostatic attraction and repulsion, along with a

set of parameters obtained from experimental data. (Spoel et al., 2005). The force field

relevant to this study is the Optimized Potential for Liquid Simulations all-atom (OPLS-

AA) force field. OPLS was developed from the earlier AMBER-united atom force field but

was parametrized to produce experimental thermodynamic and structural data on fluids.


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Based on the rationale that proteins and peptides consist of organic functional groups

alcohols, esters, thioesters, amides, hydrocarbons, the OPLS force field was built up from

parameters that model organic liquids (Jorgensen and Tirado-Rives, 1988). The non-bonded

interactions are computationally represented through Coulomb and Lenard-Jones terms

interacting between sites centered on the nuclei. The intermolecular interaction energy

between molecules a and b is the sum of interactions between the sites on the two molecules

(2.3).

= 2

+

12

6

(2.3)

The non-bonded contribution to the intermolecular energy is evaluated with the same

expression for all pairs of sites separated by more than three bonds. In the 1988 version of the

OPLS force field, each atomic nucleus has an interaction site except for hydrocarbon (CHn)

groups which are treated as united atom centered on the carbon atom. No special functions

were used to describe the hydrogen bonding and there were no additional interaction sites for

lone pairs. Standard geometries were used for the molecules with fixed bond lengths and

bond angles, though torsional motion was included. Particular emphasis was placed on

reproducing the experimental densities and heats of vaporization for the liquids (Jorgensen

and Tirado-Rives, 1988). However computationally inexpensive united-atom models are, all-

atom models allow more flexibility for charged distributions and torsional energetic. Hence,

the OPLS all-atom force field was developed (Jorgensen et al., 1996).

However, amidst all these changes, there are still inaccuracies in the force fields

developed. A major problem persisting is the functional form of the potential energy, which

has several restrictions, namely, the use of atom-centered charges rather than the explicit


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representations of the lone pairs on atoms which is a more accurate description of molecular

charge distribution, and a failure to explicitly treat electronic charge distribution. To make up

for these shortcomings, the parameters are adjusted accordingly, hence the continuing

improvements in the OPLS force field series (Kaminski et al., 2001).

Algorithms

In order to solve equations of motion, molecular dynamics make use of algorithms to

perform step-by step numerical integrations of the systems function. The simplest algorithm

for solving these equations of motion is the Euler solution (Berendsen, 2004). However, it is

unstable and inaccurate for MD purposes so several sophisticated predictor-corrector

algorithms were developed. But the best and simplest algorithm that is widely-used is the

Verlet Algorithm, and its other form, the Leap-Frog algorithm. These algorithms are favored

in MD simulations because they are time-reversible, stable, symplectic (conserves volume in

phase space) and simple (Allen, 2004). In GROMACS ver. 4.5.5, the leap-frog algorithm is

the default integrator used in MD simulations:

+ 12 = 12 + () (2.4) + 12 = () + + 12 (2.5)

Figure 2. 5 Visualization of the Leap-Frog Algorithm


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As seen in Figure 2.5, the Leap-frog algorithm computes the position x and velocity v

over a time tin alternating steps resembling two frogs leaping over each others backs.

Also an important part of molecular dynamics simulation is the energy minimization. It

ensures that there are no stearic clashes and inappropriate geometry that might cause the

system to explode. There are several algorithms that are used in energy minimization, the

most common of all is the steepest descent. It makes use of derivative information and takes

a step in the direction of the negative gradient without considering the previous steps. It is

fast, simple and easy to implement but like all other energy minimization mthods, it can only

find the local minima of the system. It calculates the forces F and potential energy first

followed by the new positions given as:

+1 = + (||) (2.6)This equation defines the vector r as the vector of all 3N coordinates and its new

position n +1 as defined by its maximum displacement hn, the negative gradient of the

potential Vor the force Fn, its previous position rn and the largest absolute value of the force

components max|Fn|. If (Vn+1 < Vn) the new positions are accepted andhn+1 = 1:2hn. If (Vn+1

Vn) the new positions are rejected andhn = 0:2hn. The algorithm stops when either a user-

specified value has been reached, the maximum nuber of steps has been performed or the

value ofFhas converged to machine precision.


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