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Development of a normal mode-based geometric simulation approach for investigating the intrinsic mobility of proteins [Elektronische Ressource] / von Aqeel Ahmed

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Development of a normal mode-based geometric simulation approach for investigating the intrinsic mobility of proteins Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften vorgelegt im Fachbereich Biowissenschaften der Goethe Universität in Frankfurt am Main von Aqeel Ahmed aus Phullahdyoon, Pakistan Frankfurt am Main 2009 (D30) vom Fachbereich Biowissenschaften der Goethe Universität als Dissertation angenommen. Dekan: Prof. Dr. Volker Müller Gutachter: Prof. Dr. Holger Gohlke Prof. Dr. Peter Güntert Datum der Disputation: Table of contents 1 Introduction and aims......................................................................................... 1 2 State of the art...................................................................................................... 7 2.1 Molecular dynamics (MD)..................................................................................... 7 2.2 Normal mode analysis (NMA)............................................................................... 9 2.3 Elastic network model (ENM)............................................................................. 11 2.4 FIRST, ROCK and FRODA ............................................................................... 14 2.5 CONCOORD........................................................................................................ 16 3 Theory and implementation ............................

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Published 01 January 2009
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Development of a normal mode-based geometric simulation
approach for investigating the intrinsic mobility of proteins


Dissertation
zur Erlangung des Doktorgrades
der Naturwissenschaften

vorgelegt im Fachbereich Biowissenschaften
der Goethe Universität
in Frankfurt am Main

von
Aqeel Ahmed
aus Phullahdyoon, Pakistan

Frankfurt am Main 2009
(D30)




vom Fachbereich Biowissenschaften
der Goethe Universität als Dissertation angenommen.






Dekan: Prof. Dr. Volker Müller
Gutachter: Prof. Dr. Holger Gohlke
Prof. Dr. Peter Güntert
Datum der Disputation:


Table of contents

1 Introduction and aims......................................................................................... 1
2 State of the art...................................................................................................... 7
2.1 Molecular dynamics (MD)..................................................................................... 7
2.2 Normal mode analysis (NMA)............................................................................... 9
2.3 Elastic network model (ENM)............................................................................. 11
2.4 FIRST, ROCK and FRODA ............................................................................... 14
2.5 CONCOORD........................................................................................................ 16
3 Theory and implementation .............................................................................. 18
3.1 Rigid Cluster Normal Mode Analysis (RCNMA) approach ............................ 20
3.1.1 Elastic Network Model (ENM) ...................................................................................... 20
3.1.2 Coarse3graining in RCNMA.......................................................................................... 21
3.2 Normal Mode Simulation (NMSim) approach .................................................. 22
3.2.1 Mode extension techniques ............................................................................................ 23
C direction ......................................................................................................................... 24 α
Random direction................................................................................................................ 24
Distance dependent C and random direction..................................................................... 24 α
3.2.2 Mode combination techniques........................................................................................ 26
Linear combination of modes in freely3volving NMSim ................................................. 26
Linear combination of modes in target3directed NMSim.................................................... 26
3.2.3 Structure distortion in normal mode directions .............................................................. 27
3.2.4 Structure correction module ........................................................................................... 28
General overview ................................................................................................................ 28
Constraint types and modeling............................................................................................ 28
Covalent bonds.................................................................................................................... 29
Non3covalent bonds ........................................................................................................... 30
Steric clashes....................................................................................................................... 31
Phi/psi (ϕ /ψ) modeling....................................................................................................... 31
Rotamer modeling............................................................................................................... 33
Backbone and side3chain planarity and chirality................................................................ 35
Constraint adjustment.......................................................................................................... 37
3.2.5 Pathway selection in ROG3guided NMSim................................................................... 38
3.3 Model testing ........................................................................................................ 39
3.3.1 Testing the ϕ/ψ model on an Ala36 system .................................................................... 40
3.3.2 Testing the rotamer model on lysozyme......................................................................... 43
4 Materials and methods ...................................................................................... 45
4.1 Comparative study of ENM and ED .................................................................. 45
4.1.1 ED modes and protein data set ....................................................................................... 45
4.1.2 RCNMA and ENM parameters used.............................................................................. 47
4.1.3 ED and CGNM comparison ........................................................................................... 48
4.1.4 Similarities/dissimilarities in classes/folds: ED and ENM modes.................................. 50
4.2 NMSim and methodological comparisons.......................................................... 51
4.2.1 Analysis of MD, NMSim, FRODA, CONCOORD and experimental HEWL ensembles
51
4.2.2 Rotamer states and derived measures: rotamericity, heterogeneity, and occupancy ...... 53
4.2.3 Structure quality using Procheck.................................................................................... 54

4.3 NMSim and biological applications.................................................................... 54
4.3.1 The proteins in the dataset.............................................................................................. 54
4.3.2 The NMSim run: parameters and ensemble generation.................................................. 56
4.4 NMSim and the pathways of conformational change....................................... 57
5 Results and discussions..................................................................................... 58
5.1 A large scale comparative study of ENM and ED............................................. 58
5.1.1 Influence of the reference structure: Average vs. open .................................................. 60
5.1.2 Influence of the level of coarse3raining: ENM vs. RCNMA....................................... 61
5.1.3 Comparison between ED and ENM modes.................................................................... 62
5.1.4 Similarities/dissimilarities in classes/folds: ED and ENM modes.................................. 67
5.2 Comparison of the performance of NMSim to other conformation generation
methods .............................................................................................................................. 71
5.2.1 Residue fluctuations and correlations............................................................................. 71
5.2.2 Conformational space exploration.................................................................................. 75
5.2.3 Essential dynamics ......................................................................................................... 78
5.2.4 Side3chain flexibility and rotamers................................................................................ 80
5.2.5 Structure quality using Procheck.................................................................................... 85
5.3 Performance of NMSim in exploring biologically relevant conformational
changes ............................................................................................................................... 87
5.3.1 Domain motions ............................................................................................................. 88
Comparison of essential dynamics between experimental and NMSim structures ............. 89
Intrinsic fluctuations and conformational changes.............................................................. 92
Ligand bound conformations generated from an unbound one........................................... 95
ROG3guided trajectory leads to ligand bound conformation ............................................. 98
5.3.2 Functionally important loop motions ........................................................................... 104
Ligand bound loop conformation computed from unbound.............................................. 104
Intrinsic fluctuations and conformational changes............................................................ 109
5.4 NMSim and Conformational change pathways............................................... 112
5.4.1 Adenylate kinase: a test case ........................................................................................ 112
5.4.2 NMSim generated pathways using Close directed and ROG3guided simulations........ 112
6 Summary.......................................................................................................... 117
Zusammenfassung.................................................................................................... 122
Outlook...................................................................................................................... 128
Acknowledgements ................................................................................................... 129
Appendix ................................................................................................................... 131
Bibliography ............................................................................................................. 141
Curriculum vitae....................................................................................................... 156
Introduction 1

1 Introduction and aims

1
Macromolecules are dynamic, and their motions are critical for their functions. The
2first evidence of a conformational change was reported in 1938 by Felix Haurowitz.
His startling discovery showed that native hemoglobin adapts different conformations
during and as part of its functional cycle. Since then, many examples illustrating
relationship between molecular motions and functions have been reported. For
example, conformational changes are required for the functioning of transport
3,4 5,6
proteins, catalytic processes of enzymes, molecular mechanism of protein
739 10,11regulations, and working of motor proteins. Important conformational changes
upon ligand binding have also been observed in several proteins, e.g., HIV31
12 13 14316 17,18protease, aldose reductase, adenylate kinase, tyrosine phosphatase, and
19,20calmodulin. These conformational changes range from side chain fluctuations to
21,22reorientations of domains and partial unfolding and refolding.
Several different models have been proposed to explain conformational changes upon
ligand binding to a protein. Assuming rigid receptor and shape complementarities of
the binding partners, “lock3and3key” was proposed ni the nineteenth century by Emil
23Fischer. Later on, it was found incompatible with the evidences of conformational
changes observed in binding partners during binding processes. Consequently, the
24“induced fit” model was proposed to account for the plasticity in receptor proteins.
This model assumes that substrate binding induces a conformational change to a
receptor. Thus, a geometric fit is ensured only after the structural rearrangement of the
receptor caused by the binding interactions. However, the extent to which the
25conformational changes are literally induced is questionable. For example, Bosshard
has reported that induced fit is possible only if the match between the interacting sites
is strong enough to provide the initial complex enough strength and longevity so that
induced fit takes place within a reasonable time. In recent years, the “conformational
26329
selection/preexisting equilibrium” model has emerged as an alternative for
induce3fit. Here, it is proposed that proper conformations are “picked” by a ligand
from the ensembles of rapidly interconverting conformational species of the unbound
Introduction 2
molecules. This is supported by experimental evidence for the presence of
30,31conformational variability of binding partners prior to their association.
Furthermore, it explains as to why a single protein can bind multiple unrelated ligands
32at the same site.
Despite the conceptual differences between “induced fit” and “conformational
selection”, it should be noted that both models at least agree with regard to the
statement that in every complex, the conformation of both binding partners has to be a
33336specific one for both to fit. It has also been suggested that conformational
selection and induced fit are not two mutually exclusive processes and that induced
25fit requires some prior molecular match to provide sufficient affinity, which is likely
provided by a conformational selection mechanism. The question is then to assess the
extent of each mechanism. A recent study in this direction investigates the interplay
between the two mechanisms and concludes that strong and long3range ligand3protein
interactions favor induced3fit mechanism whereas weaker and short3range interactions
37favor a conformational selection mechanism.
The understanding of ligand binding and mechanisms of conformational changes is
38340important in the development of structure3based drug design (SBDD). Initially,
41
SBDD approaches relied on the validity of the “lock and key” model, although this
40,42,43assumption leads to clear limitations. There are considerable efforts nowadays
to incorporate the influence of (changes of) protein flexibility and mobility into recent
38,39,44drug design approaches. These efforts are grounded on the “induced3fit” and
“conformational selection” models of ligand binding to proteins. In these lines,
incorporating protein mobility information, in terms of multiple structures from X3
ray, NMR or MD simulations, has been proven to enhance protein3protein
40,45,46 47349 50docking, protein3ligand docking and pharmacophore models.
It is important to mention that one needs to distinguish between two different but
related concepts, i.e., flexibility and mobility, in order to understand and model
conformational changes. Flexibility is a static property that only determines the
51possibility of a motion, whereas nothing actually moves. Mobility in turn describes
actual movements in terms of directions and amplitudes. Flexibility is not necessarily
a prerequisite for mobility, as rigid parts of a biomolecule (e.g., domains or helixes)
Introduction 3
can well move as a whole when connected by hinges. However, mobility provides the
origin for receptor plasticity, which enables binding partners to conformationally
adapt to each other.
Knowledge about protein mobility can be obtained from different experimental
52approaches. X3ray crystallography is the major source of structural information;
53however, it provides the static picture of a single conformation. The underlying
protein dynamics can be interpreted using B3factor values or using multiple
conformations crystallized in different conformational states. This is, however,
restricted to a limited conformational space due to a limited number of available
54conformations. By contrast, NMR spectroscopy usually provides more direct
dynamics information, for example in terms of order parameters and relaxation rates;
55however, it is restricted to proteins of a limited size.
Different computational approaches targeting the modelling of protein flexibility and
56358
plasticity are promising in this context. Molecular dynamics (MD) simulation is
one of the most widely applied and accurate computational techniques currently being
used. However, despite immense increase in computer power, MD simulations are
computationally expensive and explore limited conformational space due to slow
59,60
barrier crossing on the rugged energy landscape of macromolecules. Therefore,
the MD approach provides only a restricted solution to the challenges posed by
protein plasticity in SBDD, for example in generating multiple conformations for
40,61flexible docking or high throughput docking approaches.
Hence, there have been efforts to develop alternative approaches that are
computationally efficient in exploring conformational space. For example, a simple
geometry3based approach CONCOORD generates conformations by satisfying
62,63distance constraints derived from a stating structure of proteins. Another,
geometry3based approach FRODA generates conformations by diffusive motions of
64flexible regions and rigid clusters of proteins. In contrast to MD, these approaches
do not provide the time evolution of the molecular movements. However, these
65,66approaches are promising due to their efficiency and applicability in SBDD. So
far, these geometry3based approaches do not use any directional guidance for
Introduction 4
sampling the biologically relevant conformations, which can be helpful, taking into
account the complexity of conformational space available to macromolecules.
Coarse3grained normal mode (CGNM) approaches, e.g., elastic network model
(ENM) and rigid cluster normal mode analysis (RCNMA), have emerged recently.
They provide the directions of intrinsic mobility of biomolecules in terms of harmonic
67,68modes (also called normal modes). These normal modes can be viewed as
possible deformations of proteins and can be sorted by their energetic costs of
deformations. More importantly, in agreement with the “conformational selection”
model, the conformational changes upon ligand binding of many proteins have been
found to occur along a few low3energy modes of unbound proteins calculated using
67371CGNM approaches. For example, the directions of conformational changes in
tyrosine phosphatase and adenylate kinase upon ligand binding overlap with one of
the low3energy modes of the corresponding unbound conformations calculated by the
68RCNMA approach, as shown in Figure 1.1. Furthermore, the calculations of these
modes only take seconds for these proteins and, therefore, can be applied to large
macromolecules as well as can be applied iteratively. Realizing the potential of these
CGNM approaches, different approaches have utilized these directional information,
72374e.g., in steering MD simulations, incorporating receptor flexibility in docking
75377 78381approaches, flexible fitting of molecular structures, and efficient generation
82384of pathways of conformational changes.



Introduction 5

a) b)

Figure 1.1: Superimposition of open (blue) and closed (green) conformations of
tyrosine phosphatase (panel a) and adenylate kinase (panel b). In addition,
the amplitudes and directions of motions as predicted by the modes most
involved in the conformational changes, respectively, are depicted as red
arrows. In both cases, the amplitudes of the motions were scaled for best
68
graphical representation (Figure adopted from Ahmed et al. ).
Assuming that the low3energy deformation directions of proteins obtained from these
CGNM approaches can be helpful in exploring the intrinsic mobility of proteins, the
following aims were set for this thesis:
• To validate the directional information obtained from the CGNM approaches
on a large dataset of proteins and to study the strengths and limitations of these
approaches in capturing the essential motions of proteins.
• To design and develop an efficient geometry3based approach (termed
NMSim), utilizing the directional information from a CGNM approach for
exploring the intrinsic mobility of proteins.
• To compare and study the usefulness and limitations of different geometry3
based approaches, i.e., NMSim, FRODA, and CONCOORD.
• To study the usability of the NMSim approach in exploring the intrinsic
mobility of proteins, and in describing ligand induced conformational changes
and conformational change pathways.
Introduction 6
Keeping these aims in perspective, a large3scale comparative study is performed
85,86between principal directions of proteins observed in MD simulations and normal
modes obtained from CGNM approaches for a large dataset of 335 diverse proteins in
section 5.1. A multi3scale approach, termed Normal Mode based Simulation
(NMSim), is then developed in this study (chapter 3). The idea behind is to
incorporate directional information in a geometry3based simulation technique, in
order to sample biologically relevant conformational space, which distinguishes this
approach from the previously reported geometry3based simulation approaches
62 64CONCOORD and FRODA. In order to analyze the usefulness and the limitations
of the different geometry3based approaches, in general, and the NMSim approach, in
particular, a methodological comparative study is performed on hen egg white
lysozyme in section 5.2. The applicability of the NMSim approach for describing
ligand3induced conformational changes is presented in section 5.3. Furthermore,
NMSim3generated conformational change pathways from the apo structure to the
87389ligand bound structure of adenylate kinase are compared with previous studies
and the different crystal structures which lie along the generated pathway are
identified in section 5.4.


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