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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5606_Библиотеки_им_академика_М_И_Перельмана.pdf
14 Exploring the Significance of Experimental and Computational Methods .. . 409
Given the challenges and advances, experimental and computational methods are
essential for understanding protein structures. Techniques like homology and ab
initio modeling drive innovation in structural bioinformatics, offering insights with
therapeutic potential. These methods not only deepen our understanding of protein
structures but also inspire hope for the development of novel therapeutic strategies.
The following sections will explore these promising avenues of modeling
approaches.
2.1 Homology Modeling
Protein modeling by homology is one of the most common approaches for modeling
protein structure and is based on molecular evolution. Protein evolution has involved
complex mechanisms related to gene duplication and random DNA mutations
[59]. These molecular changes have generated slightly different amino acid
sequences, forming families of structurally related proteins [60]. Proteins with a
common ancestry are called homologous [61]. The concept of homology encompasses two related definitions: orthology and paralogy. Orthologous genes and
proteins share a common ancestry and diverge due to speciation events, while
paralogous genes and proteins differentiate due to gene duplication [62]. The similarity between amino acid sequences in homologous proteins, as expressed by the
degree of identity, is less conserved than the similarity of their 3D structures
[63]. Additionally, the similarity of the protein backbone increases with relative
sequence identity. An identity above 50% is considered high, resulting in a small
difference in RMSD (Å) between the 3D structures, while an identity below 30% is
considered low, which significantly impacts homology modeling [64]. Some historical aspects of homology modeling are shown in Fig. 14.1 and discussed in several
reviews, highlighting the information acquired in recent years [65].
Homology modeling uses previous information from structural similarities with
proteins from the same family [66]. The following steps (Fig. 14.2) are generally
employed:
Selection of the protein primary sequence; Identification of structural homologies in
the PDB or another database system, such as the universal protein database
(UniProt), currently the world’s largest database with information on proteins
[67];
Alignment of the target protein with the homologous protein sequences: Sequence
alignment software such as Clustal [68], Omega [69], or MUSCLE [70] can be
used;
Modelling structured regions: The structure is obtained by homology using the
alignment as a starting point to build the target protein model. This procedure
can be performed using molecular modeling software such as MODELLER [63]
or SWISS-MODEL [ 71 ];
410 A. H. Moraes et al.
Fig. 14.1 Historical timeline of major developments in homology modeling, CASP Critical
Assessment of Protein Structure Prediction, GPU Graphics Processing Unit, TPU Tensor
Processing Unit. (Figure adapted from reference [66])
Fig. 14.2 The seven classical steps of homology modeling. The shown structures exemplify events
that influence and are critical for modeling: (1) Selection of the protein primary sequence;
(2) Alignment correction; (3) Modeling structured regions; (4) Loop modeling; (5) Side chains
modeling; (6) Optimization, and (7) Validation. (Figure adapted from reference [66])
14 Exploring the Significance of Experimental and Computational Methods .. . 411
Loop modeling: Loop region modeling can be done through database search
approaches or conformational analysis methods (ab initio). This is a critical
step because loop coordinates ca n be crucial for protein functions;
Side chains modeling: The conformations of the side chains are also modeled based
on steric and energetic data from similar structures obtained experimentally. This
process uses querying rotamer libraries, a scoring function, and a scanning
method. Several software programs have been developed to sample rotational
angles of the side chains, such as OPUS-Rota2 [72], SCWRL [73], and FASPR
[74];
Optimization and refinement: energy minimization process using molecular
mechanics force fields is applied to eliminate atomic overlaps and collisions.
MD and Monte Carlo simulations can be performed as additional optimization
measures [1];
Evaluation and Validation: Cross-validation methods and available tools can be
used, such as Distance-matrix alignment (DALI, http://ekhidna2.biocenter.
helsinki.fi/dali/) or Verify3D (https://servicesn.mbi.ucla.edu/Verify3D/).
It is worth noting that most online servers and programs for homology modeling
automate all steps from the primary sequence to the 3D structure without user control
over parameters. Other tools allow manual inspection, requiring more user knowledge about the system and program theories [66].
Approximately 75% of proteins are formed by multiple domains (about 2.1
domains in eukaryotic systems and 1.5 domains in prokaryotic systems) [75]. Understanding the 3D structure of proteins with multiple domains can provide insights into
several mechanisms, including the impact of mutations on the functions of multiple
domains and their relationship with different types of diseases. The re are tools for
modeling the structure of proteins with multiple domains. For example, an automated method for homology modeling was developed using probabilistic data from
multiple domains of primary sequence [76].
The following inherent aspects of homology modeling can be highlighted: (1) it
offers several significant advantages and is efficient in generating 3D structures of
proteins more quickly than through experimental techniques such as X-ray crystallography or NMR; (2) it allows an expansion of the range of structural coverage, as it
allows the prediction of structures that have not yet been determined experimentally;
(3) homology-generated models can be adapted for molecular docking simulations,
enabling theor etical investigation of protein-substrate interactions in a variety of
biological contexts. On the other hand, we can also highlight the following problems
when using protein homology modeling: (a) model s may have limited accuracy
since they are estimates based on structures of homologous proteins, mainly in
regions of low primary sequence sim ilarity; (b) the quality of the structural model
prediction depends on the quality of the alignment of the primary amino acid
sequences used as a reference; (c) problems with modeling highly variable regions
such as loops and side chain residues are very dependent on rotamer angle information, resulting in inaccu rate structures in these regions [66, 77, 78].
412 A. H. Moraes et al.
2.2 Ab Initio Modeling
It is possible to predict a protein’s 3D structure from its primary sequence using ab
initio protein modeling. This method relies on conformational analyses guided by a
potential energy function based on the structural environment [54]. In ab initio
modeling, a series of possible conformations (decoy structures) are generated and
subsequently ranked and selected using conformational energy evaluation methodologies. The success of ab initio modeling is related to three factors: (1) a function
that accurately describes the potential energy of the protein’s native structure and
that leads to the most stable thermodynamic state through comparison with all
possibilities of decoy structures; (2) an efficient search algorithm that can quickly
characterize the lowest energy states through conformational analysis; and (3) strategies that select near-native structures from a set of decoy structures. It is important
to highlight that ab initio modeling requires broader conformational sampling due to
the complexity of structural space and the diversity of energy landscapes associated
with different protein folding mechanisms [79 ].
2.3 New Approaches
Deep learning-based approaches such as AlphaFold [58] and trRosetta [80] were
introduced in the CASP editions CASP14 and CASP15. These artificial intelligence
approaches utilize protein structure data available in the PDB. The computational
experiments conducted during these CASP editions demonstrated that these
approaches outperform traditional methods, such as genetic algorithms, graph theory, ML, and neural networks in protein folding [81]. AlphaFold2, developed by
Google’s DeepMind, showcased exceptional performance at CASP14 [82]. Concomitantly, RoseTTAFold was developed, whose results were presented at CASP15 and
incorporated into the Continuous Automated Model Evaluation (CAMEO) experiment [83]. CAMEO blindly evaluates structure prediction servers like the programs
tested in the CASP editions using protein structures available in the PDB.
AlphaFold was the first computational method capable of predicting protein
structures with atomic-level accuracy, even for cases without known homologous
structures exist [58, 84]. The method was validated during the CASP14 and is
currently in its second version. AlphaFold3: The basis of this new version of
AlphaFold is the machine learning approach incorporating physical and biological
information of protein structures, using alignments of multiple primary sequences. A
two-way network architecture is employed, in which information from the sequence
and the 2D distance map levels is transformed and transmitted interactively. The
method implemented end-to-end learning, from the final 3D coordinates generated
along all network layers to the input sequence [58].
On the other hand, RoseTTAFold is based on a three-way network architecture,
which processes sequence alignment information and 2D distance matrix
14 Exploring the Significance of Experimental and Computational Methods .. . 413
information in parallel, with better predictions than those provided by trRosetta, the
second overall-best ranked method after AlphaFold2 in CASP14 [85]. The proposed
three-way information architecture network might improve performance using an
architecture operating in the 3D coordinate space. This innovation has provided a
closer connection between sequence, distances between residues, and atomic orientations and coordinates. In this architecture model, structure prediction arises from
the relationships between the amino acid sequence information, the 2D distance
map, and the 3D coordinates. This allows the network to collectively reason about
relationships within and between sequences, distances, and coordinates. This interactive mechanism, therefore, contrasts with the processing of 3D atomic coordinates
in the two-network architecture of AlphaFold2, which occurs after the processing of
sequence and distance maps is completed. One of the limitations, due to computer
hardware memory problems, was the impossibility, at the time, of training the model
for large protein systems [80].
3 Conformational Diversity of Proteins
Proteins do not exist in a single, fixed conformation. Instead, they are best described
as conformational ensembles, wherein multiple conformations coexist in equilibrium
[86, 87]. Among these conformations, some states are more frequently populated
than others. The extent of conformational diversity varies among proteins. While
some proteins exhibit only minor conformational changes, others, known as intrinsically disordered proteins (IDPs), occupy many conformations, making them highly
flexible and dynamic [88, 89].
In one-domain proteins, the movements responsible for conformational diversity
include the rotation of amino acid residue side chains, loops, and the collective
movement of connected parts [87]. The rotation of amino acid side chains plays a
crucial role in the protein folding process and the transmission of information
through allostery [90–92]. Loops, which can be well-defined or disordered randomcoil-like structures, generally serve as flexible connectors between more regular
secondary structures (α-helices and β-strands). However, it is important to highlight
that loops are more than connecting elements as they interact with the solvent,
ligands, and other biomolecules. They also participate in the conformational transitions involved in protein regulation [93].
The different modules comprising multidomain proteins are commonly
interconnected by short or lengthy sequences of amino acids, typically ranging
from approximately 5 to 25, known as linkers. Variations in linker sequences
influence their conformational dynamics, potentially affecting cooperative adjustments in interdomain and protein–protein interactions. These changes can facilitate
long-range communication between different functional modules in multidomain
protein [93]. Rigid domains can also be connected by flexible joints known as
hinges. During hinge bending motions, structural units shift relative to each other,
414 A. H. Moraes et al.
disrupting the interfacing packing while preserving the compact arrangement within
the protein subunit [87].
3.1 Characterization of Protein Conformational States
Proteins can adopt various conformational states in solution, which may differ from
those observed in the crystal lattice. This discrepancy arises because crystall ization
conditions can impose constraints and stabilize specific conformations, potentially
altering the native structural dynamics of the protein [94]. Various ensemble techniques have been developed to investigate protein conformations and dynamics
using experimental and computational methodologies. Prominent experimental
approaches include NMR spectroscopy, SAXS, single molecule spectroscopy, and
Cryo-EM. These experimental techniques are frequently complemented by computational methods that use empirical data as structural constraints. The computational
methods include REMD simulations, metadynamics, steered MD, accelerated MD,
and Markov state models (Fig. 14.3)[87].
3.2 Experimental Methods to Study Protein Dynamics and Conformations
Experimental determination methods can be employed to determine the conformations of proteins. In X-ray crystallography, conformational heterogeneity hinders
crystal formation. Even when crystals are successfully grown, this variability can
cause disorder in regions of the structure that are more mobile [95]. Conversely, in
cryo-EM structural determination, computational techniques often aid in identifying
such heterogeneity. This may involve focusing analyses solely on structurally
homogeneous segments of the complex or deriv ing an ensemble of structures
when discrete subpopulations are present. Importantly, delineating conformational
diversity often provides invaluable insights into the underlying functional
mechanisms [96].
NMR offers various experimental strategi es to examine protein kinetics and
interactions across various time scales and binding affinities [97]. For instance, the
dissociation constants (K
femtomolar for proteases, RNases, and DNases to millimolar for proteincarbohydrate molecular recognition [20, 98]. NMR is a potent tool for interface
mapping, facilitating the investigation of weak and transient complexes that may
pose challenges for other experimental methodologies [99].
Unlike NMR and Cryo-EM, small-angle X-ray scattering (SAXS) does not offer
atomic resolution. It is typically considered a low-resolution structural techni que for
investigating conformational changes, ligand binding, or protein interactions over
time. Nevertheless, SAXS can provide valuable insights into protein behavior,
) associated with protein interactions range from
d
14 Exploring the Significance of Experimental and Computational Methods .. . 415
Fig. 14.3 Timescale of dynamic processes in proteins and the experimental methods that can detect
fluctuations on each time scale. Local motions, including bond vibrations, methyl rotations, loop
motions, and sidechain rotamers, occur on timescales ranging from femtoseconds (fs) to nanoseconds (ns). Global motions, such as larger domain movements, range from nanoseconds (ns) to
seconds (s). The timeline depicted spans from fs to s. The methods used to investigate these motions
include MD simulations (fs–ms), NMR (ps–s), smFRET (ns–ms), AFM/OT (μs–ms), hydrogendeuterium exchange (ms–s), SAXS (μs–s), Cryo-EM (ms–s), IR/Raman (fs–s), and fluorescence
(fs–s). The figure highlights the temporal ranges of these motions, and the corresponding techniques
used for their study
including subtle domain movements [100]. Notably, SAXS is a rapid structural
approach that can be directly applied to solutions of macromolecules under various
conditions, including quasi-native environments [101]. SAXS provides a convenient
means to evaluate alterations in the extension or flexibility of a protein. Among its
most impactful applications is the assessment of conformational transitions triggered
by ligand or protein partner binding, allowing for the characterization of the overall
shape and oligomerization state of the biological macromolecule [101, 102]. Timeresolved probing of conformational changes can be achieved through various mixing
methods (e.g., stopped-flow, laminar flow, chaotic flow) and pump-probe
techniques [100].
SAXS is a well-establ ished technique for structural characterization of samples,
offering resolutions ranging from 5 nm to 1000 nm. Notably, SAXS exhibits
sensitivity to both ordered and disordered features within the sample and circumvents the need for crystallization, fixation, or vitrification procedures. In a typical
SAXS experiment, a collimated, monochromatic X-ray beam is directed onto the
sample, with the radiation scattered at low angles (usually a few degrees) being
416 A. H. Moraes et al.
captured by a detector [103]. Due to the generally random orientations of particles
within the solution, a rotational averaging occurs, resulting in a one-dimensional
scattering profile expressed as I(q) versus q, where I represents the net intensity and
q denotes the momentum transfer variable. This variable is defined as q = (4πsinϴ)/
radiation. Interpreting the solution-scattering profile in terms of average global
parameters, such as radius of gyration (Rg), molecular volume, and the interatomic
distance distribution P(r) versus r (which is related to I(q) through a Fourier
transform), is relatively straightforward. However, contemporary approaches
increasingly involve interpreting SAXS data using 3D models in shapes or atomistic
representations. This needs careful consideration of how to accurately constrain the
structural landscape to find the correct solution while acknowledging the inherent
ambiguity if more than one 3D model can fit the same one-dimensional scattering
profile [104].
Single-molecule spectroscopy methods include force-based techniques such as
atomic force microscopy (AFM) [105] and optical tweezers [106], as well as
fluorescence-based techniques, such as single-molecule fluorescence resonance
energy transfer (smFRET) [107 ]. These methodologies have gained widespread
use in quantifying the conformational heterogeneity and structural dynamics of
biomolecules, both in vitro and in vivo. They enable the observation of transient
intermediates and the characterization of static and dynamic heterogeneity [87, 108].
Several instances highlight the efficacy of these single-molecule techniques in
elucidating the dynamics of interactions among biological molecules. Notably,
riboswitches — RNA structures that trigger conformational and functional changes
upon ligand binding — have been investigated using these methods. Optical tweezers have facilitated the study of numerous riboswitches, demonstrating how ligand
adenine binding orchestrates the coordination of domains within the three-loop
structure [109, 110]. This observation is supported by steered MD simulations
[111]. Furthermore, single-molecule Förster resonance energy transfer (smFRET)
has enhanced our understanding of membrane transporter dynamics [112], including
ATP-binding cassette (ABC) transporters [113
, 114 ].
3.3 Computational Methods for Exploring Protein
Conformations
Currently, the computational study of macromolecules at the molecular level
involves steps and methodologies [115, 116]. Among the most used methods, we
can mention molecular docking, classical MD, hybrid methods quantum-mechanics/
molecular-mechanics (QM/MM) [117], and quantum-mechanics/molecularmechanics/molecular-dynamics (QM/MM/M D) [118], which can be used to investigate the interactions between ligands and proteins, between proteins, proteins and
membranes, proteins and DNA, and RNA.
14 Exploring the Significance of Experimental and Computational Methods .. . 417
3.4 Molecular Dynamics Simulation
MD simulations use the equations of motion from classical mechanics to describe
molecular events by analyzing conformational changes over time. Variations in this
methodology have been successfully employed to study: (1) protein stability,
(2) conformational changes, (3) interactions with natural biological cofactors,
(4) aiding in the rational design of new drugs, (5) ab initio structure prediction of
peptides and small proteins, (6) refinement of protein structure models, (7) and
structure modeling assisted by experimental or bioinformatics data [119].
MD simulation, developed in the late 1970s, has advanced from initially being
restricted to molecules with hundreds of atoms to systems with millions, such as
proteins [120 ]. Nowadays, it is possible to study whole proteins in solution using
explicit solvation, membrane proteins, and large macromolecular complexes such as
nucleosomes and ribosomes. Computational simulations of systems with
~50,000–100,000 atoms are now routine, and simulations of approxi mately
500,000 atoms are common when proper data processing computational facilities
are available [121]. Thus, significant computational development and major
advances in MD algorithms have been made in recent decades, providing greater
efficiency in molecular simulations. This notable improvement is primarily a consequence of using high-performance parallel computing architectures (HPC) and the
simplicity of the MD algorithm. Recently, progress has been made in developing
software allowing routine MD simulations on graphics processing units (GPUs). MD
simulations of hundreds of nanoseconds (ns) per day can now be easily achieved for
small protein systems in explicit or implicit solvents.
Some of the most used software in MD are AMBER [122, 123], CHARMM
[124], OPLS-AA [125], and GROMACS [118]. The MD is based on the principles
of classical mechanics, where each atom is considered a point mass body whose
motion is influenced by the forces acting on it from all other atoms. These forces
acting on each body are calculated at each MD step using force fields. Force fields
are compl ete interaction potentials between atoms that combine to comprise the
system’s total energy. These mathematical models include potentials for bonded
terms (bond lengths, bond angles, dihedral angles) and non-bonded terms (van der
Waals and Coulomb interactions) [54]
→
UR
V
n
1 þ cos nϕ - δðÞðÞþ
dihedrals
2
The system’s total energy UR
=
bonds
Krr - r
ðÞ2þ
0
non - bonded terms
→
is obtained by summing the energies of the
K0ðÞ
angles
bonded and non-bonded terms. In this equation, K
r
2
þ
A
ij
-
12
r
ij
, K
, and Vnare the force
qiq
B
ij
j
þ
6
ϵR
r
ij
ij
constants for bond stretching, angle bending, and torsional potential, respectively.
418 A. H. Moraes et al.
r, θ, and ϕ are bond length, bond angle, and dihedral angle. The subscripted zero
terms stand for the equilibrium values for the individual variable. For example, the
AMBER program calculates the force field, chemical bond stretching, and angular
strain terms using equations with harmonic potentials described by quadratic terms.
The terms related to torsional potentials are determined by truncated Fourier series;
Van der Waals interactions are approximated by the 12–6 Lennard–Jones
(LJ) potential. Coulomb’s equation is used to calculate the electrostatic interaction
potential. The types of charges vary with the force field version, but for most
AMBER force fields, partial charges are obtained using the RESP (Restrained
Electrostatic Potential) method. Some force fields include other terms to obtain
better agreement with vibrational spectra, for example, terms that better specify
hydrogen bonds or the coupling of oscillations between bond angles and bond
lengths [126].
The choice of the force field gene rally depends on the properties and systems to
be studied, making it an important step in MD simulation. In the AMBER, there are a
variety of force fields available. For instance, lipid14 is indicated to study lipid
bilayers [127], GLYCAM_06j for carbohydrates [128], ff14SB for proteins [123],
OL15 for DNA [129], and OL3 for RNA [130]. The solvent effect of water plays a
fundamental role in the protein folding process and in determining the structure and
movement of protein molecules . Water force field models such as the TIP3P [131]
and TIP4P [132] have accurately described various physicochemical characteristics
of the solvent medium and continue to be widely used in MD simulations with
explicit solvents. These different force fields can be used together to describe
different systems’ components. For example, in a simulation of a protein in water
as the solvent and with an organic ligand, it is usual to combine the following force
fields: ff14SB (for the protein), GAFF [133] (for the organic compound), and TIP3P
(for water). In these explicit solvent simulations, much of the computational
resources are consumed in calculating the forces acting on water molecules. Methods
were developed to implicitly treat the water solvent to reduce the computational cost
with the generalized Born (GB) solvation model [134]. An additional energy term
related to the protein’s solvent-accessible surface areas (SA) can approximate the
nonpolar contributions to solvation.
High-frequency motions can limit the size of the integration step in molecular
dynamics (MD) simulations, but these motions g enerally do not significantly impact
the system’s overall behavior. In molecular systems, particle motions occur on
different time scales, influenced by intermolecular and intramolecular forces. The
SHAKE algorithm is the most widely used methodology for maintaining or
constraining geometry in MD simulations. This method modifies the equations of
motion for atoms of interest (such as those involved in chemical bonds with
hydrogen atoms) by introducing force constraints that preserve the lengths of these
bonds. It is typically applied to constrain hydrogen bonds with other atoms in the
system [135].