Macromolecular complexes and
interactions
Macromolecular complexes
Structure of complexes
Prediction of 3D structures of complexes
Analysis of macromolecular complexes
Outline
2Macromolecular complexes and interactions
Protein – small molecule 
Protein – protein 
Protein – nucleic acids 
Nucleic acids – small molecule 
3Macromolecular complexes
What is a macromolecular complex?
Protein-protein complexes
 Two or more polypeptide chains (protomers) may associate
into an oligomer
 Protein-protein and protein-nucleic acid interactions are
essential for every cellular process
 Metabolism
 Transport
 Signal transduction
 Genetic activity (transcription, translation, replication, repair, ...)
 Membrane trafficking
 Mobility
 …
Macromolecular complexes 4
 Obligate complexes
 Protomers (individual polypeptides) do not function as independent
structures, only when associated
 Examples: GABA receptors, ATP synthase,
many ion channels, ribosome, etc.
 Non-obligate complexes
 Protomers can exist and be functional as independent structures
 Examples: hemoglobin, beta-2 adrenergic receptor, insulin
receptor, etc.
Protein-protein complexes
5Macromolecular complexes – protein-protein complexes
GABAB receptor
Protein-protein complexes
6Macromolecular complexes – protein-protein complexes
Do individual subunits retain some activity?
YES NO
Non-obligate Obligate
 Oligomerization is common
 75 % of proteins in a cell are oligomers
 Homo-oligomers are the most common
 Some proteins exists solely in the oligomeric state
 Often symmetric
 Oligomerization interfaces are complementary
 Favored by evolution
Protein oligomerization
7Macromolecular complexes – protein-protein complexes
Why do proteins form oligomers?
Advantages of oligomerization
8Macromolecular complexes – protein-protein complexes
 Morphology
 More complex structures are often required for multiple functions
(e.g. membrane pores)
 Cooperativity
 Allostery (modulation of biological activity)
 Multivalent binding
 Stability against denaturation
 Smaller surface area
 Redundancy and error control
 E.g. protein translation control
Advantages of oligomerization
9Macromolecular complexes – protein-protein complexes
 Characteristics of oligomeric interface
 Large surface area (> 1400 Å2)
 Tendency to circular and planar shape (not for obligates)
 Some residues protrude from the surface
 More non-polar residues (about 2/3) than in other parts of surface
 More polar residues (about 1/5) than in protein cores
 About 1 H-bond per 200 Å2
 “Hot-spot” residues
 Responsible for most of the oligomeric interactions
 More evolutionary conserved than other surface residues
 Frequently polar residues, located about the center of the interface
Oligomerization interface
10Macromolecular complexes – protein-protein complexes
Examples of macromolecular complexes
Metabolism
Hemoglobin
Bringas et al., 2017, Scientific Reports
11
Tetramer
made of 2*2 subunits (α and β)
Green: heme
Examples of macromolecular complexes
Metabolism
Oxidative phosphorylation complexes (mitochondria)
Granata et al., 2015, Nutrition & Metabolism
12
Inner membrane space
Mitochondrial matrix
Examples of macromolecular complexes
Transport
Ferritine
Knovich et al., 2009, Blood Rev
13
Hetero-oligomer
Examples of macromolecular complexes
Signal transduction
EGFR/RAS/RAF/MEK/ERK pathway
14
Roberts and Der, 2007, Oncogene
Examples of macromolecular complexes
Genetic activity
Ribosome
Anger et al., 2013, Nature
15
Examples of macromolecular complexes
Palfreyman and Jorgensen, 2010, Molecular
mechanisms of Neurotransmitter Release
16
Membrane trafficking
SNARE proteins
Perada, 2014, Nature Reviews Neuroscience
Examples of macromolecular complexes
Palfreyman and Jorgensen, 2010, Molecular
mechanisms of Neurotransmitter Release
17
Membrane trafficking
SNARE proteins
Perada, 2014, Nature Reviews Neuroscience
Examples of macromolecular complexes
Palfreyman and Jorgensen, 2010, Molecular
mechanisms of Neurotransmitter Release
18
Membrane trafficking
SNARE proteins
Perada, 2014, Nature Reviews Neuroscience
Examples of macromolecular complexes
Yang et al., 2019, AMB Express
19
1 µm
Mobility
Flagella (of Salmonella)
Examples of macromolecular complexes
Yang et al., 2019, AMB Express
20
1 µm
Chevance and Hughes, 2008, Nature Reviews Microbiology
Mobility
Flagella (of Salmonella)
Examples of macromolecular complexes
21
Protein-lipid nanoparticle
ApoE4
ApoE4 nanodisc
Antibody
Density map from
cryo-electron microscopy
Strickland et al., 2024, Neuron
Examples of macromolecular complexes
22
Protein-lipid nanoparticle
ApoE4
Cartoon model
ApoE4 nanodisc
Antibody
Homodimer
Phospholipids
Density map from
cryo-electron microscopy
Strickland et al., 2024, Neuron
Oligomerization vs Aggregation
23Macromolecular complexes – protein-protein complexes
Oligomerization
 Oligomers are soluble
 Precise fold
 Proteins are native
(not denatured)
 Reversible (sometimes)
Aggregation
 Aggregates are insoluble
 Can be heterogenous
 Denatured proteins aggregate
(temperature, pH, salt…)
 Irreversible
The function of some proteins is to aggregate.
Aggregates ≠ pathology
Non-pathological aggregates
24
6JFV6EC0
Keratin filaments
(hair, skin, nails)
PDB code:
HET-s
(fungal reproduction and apoptosis)
Daskalov et al., 2021, Front. Mol. Neurosci.
Pathological aggregates
25
Amyloid β from human brain
(involved in Alzheimer’s disease)
Kollmer et al., 2019, Nat Commun
50 nm
Two different morphologies (I and II)
* Transition from I to II
β-solenoid
Pathological aggregates
26
Amyloid β from human brain
(involved in Alzheimer’s disease)
Bishop and Robinson, 2024, Drugs Aging.
Wang et al., 2021, Front. Cell. Neurosci.
Has non-pathological functions too!
 Blood-brain barrier maintenance
 Anti-microbial peptide
 Synapse function
 …
 Protein-nucleic acid interactions
 Non-specific – electrostatic interactions with negative charge on
the backbone of nucleic acid -> Lys and Arg residues
 Specific – recognition of particular nucleotide sequences
 Major groove – B-DNA
 Minor groove – A-DNA or A-RNA
 Single strand RNA
 Typical interfaces/motifs
 DNA binding proteins
 RNA binding proteins
Protein-nucleic acids complexes
27Macromolecular complexes – protein-nucleic acids complexes
 DNA binding proteins
 Helix-turn-helix
• (+)-sidechains
• ≈ perpendicular helices
• Recognises major groove
 Zinc finger
• Zn2+ stabilized by Cys and His residues
• Zn2+ is essential for folding
• Zn2+ mediates DNA binding
Protein-nucleic acids complexes
28Macromolecular complexes – protein-nucleic acids complexes
Protein-nucleic acids complexes
29
RNA recognition motif
(RRM)
K-homology (KH) domain Pumilio repeat domain
(PUF)
Macromolecular complexes – protein-nucleic acids complexes
 RNA binding proteins
 RRM: βαββαβ barrel-like arrangement, sequence-specific RNA recognition
 KH domain: ssRNA/DNA binding through H-bonds, electrostatic and shape
complementarity
 PUF domain: each helix recognizes a single base
30
How to detect
macromolecular complexes?
How to detect macromolecular complexes
31
 Physics-based methods
 Size
 Molecular mass
 Binding to a surface containing immobilised partner
 Temperature shift upon binding
 Binding of a fluorescent indicator
 Complementation of biological activity
 Each partner has one half of a protein
 If both partners interact, both halves also interact
 Restoration of activity (e.g. critical enzyme for organism growth, fluorescence)
 Imaging
 Fluorescence (need fluorescent tag)
 Atomic force microscopy
 Electron microscopy
32
How to resolve
macromolecular complexes?
How to resolve macromolecular complexes
33
Electron microscopy
Nuclear magnetic resonance (NMR)
X-ray crystallography
Electron microscopy
34
Cartoon model
ApoE4 nanodisc
Antibody
Homodimer
Phospholipids
Density map from
cryo-electron microscopy
Strickland et al., 2024, Neuron
NMR(Chemical Shift Perturbation, CSP)
35
Ma et al., 2023, Nucleic Acids Res.
1 peak = 1 protein residue
Protein: ProXp-ala
+ tRNA: green or blue
NMR(Chemical Shift Perturbation, CSP)
36
Ma et al., 2023, Nucleic Acids Res.
1 peak = 1 protein residue
Protein: ProXp-ala
+ tRNA: green or blue
Upon interaction with tRNA,
peaks are perturbed
NMR(Chemical Shift Perturbation, CSP)
37
Ma et al., 2023, Nucleic Acids Res.
1 peak = 1 protein residue
Protein: ProXp-ala
+ tRNA: green or blue
Upon interaction with tRNA,
peaks are perturbed
Mapping of interactions
Affinity measurement
X-ray crystallography
38
https://www.ebi.ac.uk/training/online/courses/protein-interactions-and-their-importance/where-do-the-data-come-from/x-ray-crystallography/
In Protein Data Bank (PDB, rcsb.org),
83% of structures come from X-ray crystallography.
 Asymmetric unit (ASU)
 Macromolecular structures from X-ray crystallography deposited to
PDB as a single asymmetric unit
 The smallest portion of a crystal structure to which symmetry
operations can be applied in order to generate the unit cell
 Unit cell (crystal unit)
 The basic unit of a crystal that, when repeated in three dimensions,
can generate the entire crystal
Quaternary structure in PDB database
39Structure of complexes – quaternary structure in PDB database
Quaternary structure in PDB database
40Structure of complexes – quaternary structure in PDB database
 Crystal contacts
 Intermolecular contacts solely due to protein crystallization
 Causes artifacts of crystallization
 Crystal packing - complicates identification of native quaternary
structure
Crystalline environment
41Structure of complexes – quaternary structure in PDB database
Asymmetric unit (ASU)
Crystal Unit (CU)
 Artifacts of crystallization
 Concerns about conformation of some surface regions
 Often loops or side chains are affected
 Can complicate the evaluation of the effects of mutations
Crystalline environment
42Structure of complexes – quaternary structure in PDB database
 Biological unit
 The functional form of a protein in nature
 Also called: functional unit, biological assembly, quaternary structure
 Can depend on the environment, post-translational modifications
of proteins and their mutations
Quaternary structure in PDB database
43Structure of complexes – quaternary structure in PDB database
Hemoglobin
heterotetramer
 Biological unit can consist of:
 Multiple copies of the ASU
 One copy of the ASU
 A portion of the ASU
Biological versus asymmetric unit
44Structure of complexes – quaternary structure in PDB database
ASU Biol. U
 Large assemblies
 Viral capsid
 Filamentous bacteriophage PF1
Biological versus asymmetric unit
45Structure of complexes – quaternary structure in PDB database
ASU Biol. U
ASU Biol. U
 Problem
 Most proteins in the PDB have three or more crystal contacts that
sum up to 30% of the protein solvent accessible surface area
 How to recognize biologically relevant contacts from crystal one?
Complex or artifact?
46Structure of complexes – complex or artifact?
 Experimental knowledge of oligomeric state helps with
identifying of the structure of native complex
 Search literature
 Experimental methods
 Gel filtration, static or dynamic light scattering, analytical
ultracentrifugation, native electrophoresis, …
 How to get the structure of a biological unit?
 Author-specified assembly
 Databases
 Predictive tools
Complex or artifact?
47Structure of complexes – complex or artifact?
 REMARK 350 in headers of PDB file
 Contains symmetry operations to reconstruct biological unit, but…
 Verify author-proposed biological unit by other means
 Sometimes the specific oligomers were not known at the time
the ASU was published
 Some authors may have failed to specify the biological unit
even when it was known
 Rarely, the specified biological unit might be incorrect
 Employed by
 RCSB PDB and other tools
Author-specified assembly
48Structure of complexes – complex or artifact?
 RCSB PDB
Author-specified assembly
49Structure of complexes – complex or artifact?
 PyMOL
 Generate > Symmetry mates  to visualize nearest partners
Crystal lattice
50Structure of complexes – complex or artifact?
Prediction of 3D structure of complexes
Discovering and characterising macromolecular complexes
requires heavy experimentation
How can we predict macromolecular complexes?
Prediction of 3D structure of complexes 51
Prediction of 3D structure of complexes
Homology-based predictions
Machine learning-based predictions
Macromolecular docking
Prediction of 3D structure of complexes 52
Homology based methods
 A protein complex is built based on a similar protein complex
with a known 3D structure
 Assumes that the interaction information can be
extrapolated from one complex structure to close homologs
of interacting proteins
 Close homologs (≥ 40% sequence identity) almost always interact in
the same way (if they interact with the same partner)
 Sequence similarity is only rarely associated with a
similarity in interactions
 Limited applicability (low number of templates)
Prediction of 3D structure of complexes – homology based methods 53
Homology based methods
 HOMCOS (Homology Modeling of Complex Structure)
 https://homcos.pdbj.org/
 Predicts 3D structure of homodimers and heterodimers by homology
modeling
 Optionally, identifies potentially interacting proteins
 Steps:
1. BLAST search to identify homologous templates
2. Evaluation of the model validity by combination of sequence similarity
and knowledge-based contact potential energy
3. Generation of a full atomic model by MODELLER
Prediction of 3D structure of complexes – homology based methods 54
Homology based methods
Prediction of 3D structure of complexes – homology based methods 55
Machine learning-based predictions
 AlphaFold-Multimer
 Variant of AlphaFold 2
 Predicts 3D structure of multimers
 AlphaFold 3 equivalent just came out (Abramson et al., 2024, Nature)
Prediction of 3D structure of complexes – homology based methods 56
Experimental
AlphaFold-multimerRMSD (Ca) = 0.81 Å
Macromolecular docking
 Prediction of the best bound state for given 3D structures of
two or more macromolecules
 Difficult task
 Large search space - many potential ways in which macromolecules
can interact
 Flexibility of the macromolecular surface and conformational
changes upon binding
 Can be facilitated by prior knowledge
 Ex: known binding site → significant restriction of the search space
 Distance constraints on some residues
Prediction of 3D structure of complexes – macromolecular docking 57
Macromolecular docking
 3 main parameters:
 Macromolecule representation
 Search algorithm
 Scoring function
Prediction of 3D structure of complexes – macromolecular docking 58
Macromolecule representation
 Representation of the macromolecular surface (applicable
to both receptor and ligand)
 Geometrical descriptors of shape (set of spheres, surface normals,
vectors radiating from the center of the molecule,...)
 Discretization of space: grid representation
Prediction of 3D structure of complexes – macromolecular docking 59
Macromolecule representation
 Macromolecule flexibility
 Fully rigid approximation
 Soft docking – employs tolerant “soft” potential scoring functions to
simulate plasticity of otherwise rigid molecule
 Explicit side-chain flexibility – optimization of residues by rotating
part of their structure or rotation of whole side-chains using
predefined rotamer libraries
 Docking to molecular ensemble of protein structure – composed
from multiple crystal structures, from NMR structure determination
or from trajectory produced by MD simulation
Prediction of 3D structure of complexes – macromolecular docking 60
Macromolecule representation
 Macromolecule flexibility
 Rigid body docking – basic model that considers the two
macromolecules as two rigid solid bodies
 Semiflexible docking – one of the molecules is rigid, and one is
flexible (typically the smaller one)
 Flexible docking – both molecules are considered flexible
Prediction of 3D structure of complexes – macromolecular docking 61
Macromolecular docking - search
 Generally based on the idea of complementarity between
the interacting molecules (geometric, electrostatic or
hydrophobic contacts)
 The main problem is the dimension of the conformational
space to be explored:
 Rigid docking: 6D (hard)
 Flexible docking: 6D + Nfb (impossible!)
 Information on the rough location of the binding surface
(experimental or predicted) → reduction of the search space
Prediction of 3D structure of complexes – macromolecular docking 62
Macromolecular docking - search
 Exhaustive search
 Full search of the conformational space: try every possible relative
orientation of the two molecules
 Computationally very expensive – 6 degrees of freedom for rigid
molecules (translations + rotations)
 Grid approaches
Prediction of 3D structure of complexes – macromolecular docking 63
Macromolecular docking - search
 Stochastic methods
 Monte Carlo
 Genetic algorithms
 Brownian dynamics
 ...
Prediction of 3D structure of complexes – macromolecular docking 64
Macromolecular docking - scoring
 Scoring functions
 Evaluation of a large number of putative solutions generated by the
search algorithms
 Methods often use a two-stage ranking
1. Approximate and fast-to-compute function – used to eliminate very
unlikely solutions
2. More accurate function – used to select the best among the
remaining solutions
Prediction of 3D structure of complexes – macromolecular docking 65
Macromolecular docking - scoring
 Scoring functions
 Empirical
 Knowledge-based
 Force field-based
 Clustering-based – the presence of many similar solutions is taken as
an indication of correctness (all solutions are clustered, and the size
of each cluster is used as a scoring parameter)
Prediction of 3D structure of complexes – macromolecular docking 66
 Good scores – a combination of several parameters:
 Low free energy or pseudo-energy based on force field functions
 Large buried surface area
 Good geometric complementarity
 Many H-bonds
 Good charge complementarity
 Polar/polar contacts favored
 Polar/non-polar contacts are disfavored
 Many similar solutions (large clusters)
 ...
Prediction of 3D structure of complexes – macromolecular docking
Macromolecular docking - scoring
67
Macromolecular docking - programs
Prediction of 3D structure of complexes – macromolecular docking 68
Macromolecular docking - programs
 ClusPro 2.0
 http://cluspro.bu.edu/
 Performs a global soft rigid-body search using PIPER docking
program; employs knowledge-based potential
 The top 1,000 structures are retained and clustered to isolate highly
populated low-energy binding modes
 A special mode for prediction of molecular assemblies of
homo-oligomers
Prediction of 3D structure of complexes – macromolecular docking 69
Macromolecular docking - programs
 PatchDock
 http://bioinfo3d.cs.tau.ac.il/PatchDock/index.html
 Performs a geometry-based search for docking transformations that
yield good molecular shape complementarity (driven by local feature
matching rather than brute force searching of the 6D space):
1. The molecular surface is divided into concave, convex and flat patches
2. Complementary patches are matched → candidate transformations
3. Evaluation of each docking candidate by a scoring function considering
both geometric fit and atomic desolvation energy
4. Clustering of the candidate solutions to discard redundant solutions
 Results can be redirected to FireDock for refinement and re-scoring
Prediction of 3D structure of complexes – macromolecular docking 70
Macromolecular docking - programs
 PatchDock
Prediction of 3D structure of complexes – macromolecular docking 71
Macromolecular docking - programs
 FireDock
 http://bioinfo3d.cs.tau.ac.il/FireDock/index.html
 Refines and re-scores solutions produced by fast rigid-body docking
algorithms
 Optimizes the binding of each candidate by allowing flexibility in the
side-chains and adjustments of the relative orientation of the
molecules
 Scoring of the refined candidates is based on softened van der Waals
interactions, atomic contact energy, electrostatic, and additional
binding free energy estimations
Prediction of 3D structure of complexes – macromolecular docking 72
Analysis of macromolecular complexes
 Binding energy
 Macromolecular interface
 Interaction hot spots
Analysis of macromolecular complexes 73
Binding energy
 FastContact
 http://structure.pitt.edu/servers/fastcontact/
 Rapidly estimates the electrostatic and desolvation components of
the binding free energy between two proteins
 Additionally, evaluates the van der Waals interactions using
CHARMM and reports contribution of individual residues and pairs
of residues to the free energy → highlight the interaction hot spots
Analysis of macromolecular complexes – binding energy 74
Macromolecular interface
 The region where two protein chains or protein and nucleic
acid chain come into contact
 Can be identified by the analysis of the 3D structure of the
macromolecular complex
Analysis of macromolecular complexes – interface analysis 75
Interface analysis
 Provides information about basic features of macromolecular
complexes interactions (e.g., shape complementarity,
chemical complementarity,...)
 Provides information about interface residues
 Acquired information is useful for a wide range of applications
 Design of mutants for experimental verification of the interactions
 Development of drugs targeting macromolecular interactions
 Understanding the mechanism of the molecular recognition
 Computational prediction of interfaces and complex 3D structures
 ...
Analysis of macromolecular complexes – interface analysis 76
Interface analysis
 Most common approaches for the definition of interfaces:
 Methods based on the distance between interacting residues
 Methods based on the change in the solvent accessible surface area
(ASA) upon complex formation
 Computational geometry methods (using Voronoi diagrams)
 All three approaches provide very similar results
Analysis of macromolecular complexes – interface analysis 77
Interface analysis - databases
 PDBsum (Pictorial database of 3D structures in the Protein
Data Bank)
 http://www.ebi.ac.uk/pdbsum/
 Provides numerous structural analyses for all PDB structures and
AlphaFold DB (human proteins), including information about
protein-protein and protein-nucleic acid interfaces
 Protein-protein interactions – schematic diagrams of all proteinprotein
interfaces and corresponding residue-residue interactions
 Protein-nucleic acid interactions – schematic diagrams of proteinnucleic
acid interactions generated by NUCPLOT
Analysis of macromolecular complexes – interface analysis 78
Interface analysis - databases
 PDBsum
Analysis of macromolecular complexes – interface analysis 79
Interface analysis - databases
 PDBsum
Analysis of macromolecular complexes – interface analysis 80
Interface analysis - tools
 Analyze interface of a given macromolecular complex
 PISA (Protein Interfaces, Surfaces and Assemblies)
 MolSurfer
 Contact Map WebViewer
 PIC (Protein Interaction Calculator)
 …
Analysis of macromolecular complexes – interface analysis 81
Interface analysis - tools
 PISA (Protein Interfaces, Surfaces and Assemblies)
 www.pdbe.org/pisa
 An interactive tool for the exploration of macromolecular interfaces
(protein, DNA/RNA and ligands), prediction of probable quaternary
structures, database searches of structurally similar interfaces and
assemblies
 Overview and detailed characteristics of all interfaces found within
a given structure (including those generated by symmetry
operations)
 Provides interface area, ΔiG, potential hydrogen bonds and salt
bridges, interface residues and atoms, ...
Analysis of macromolecular complexes – interface analysis 82
Interface analysis - tools
 MolSurfer
 http://projects.villa-bosch.de/dbase/molsurfer/index.html
 Visualization of 2D projections of protein-protein and proteinnucleic
acid interfaces as maps showing a distribution of interface
properties (atomic and residue hydrophobicity, electrostatic
potential, surface-surface distances, atomic distances,...)
 2D maps are linked with the 3D view of a macromolecular complex
 Facilitates the study of intermolecular interaction properties and
steric complementarity between macromolecules
Analysis of macromolecular complexes – interface analysis 83
Interface analysis - tools
 MolSurfer
Analysis of macromolecular complexes – interface analysis 84
Interface analysis - tools
 Contact Map WebViewer
 http://cmweb.enzim.hu/
 Represents residue-residue contacts within a protein or between
proteins in a complex in the form of a contact map
 PIC (Protein Interaction Calculator)
 http://pic.mbu.iisc.ernet.in/
 Identifies various interactions within a protein
or between proteins in a complex
Analysis of macromolecular complexes – interface analysis 85
Interaction hotspots
 Hot spots: the residues contributing the most to the binding
free energy of the complex
 Knowledge of hot spots has important implications to:
 Understand the principles of protein interactions (an important step
to understand recognition and binding processes)
 Design of mutants for experimental verification of the interactions
 Development of drugs targeting macromolecular interactions
 ...
Analysis of macromolecular complexes – interaction hotspots 86
Interaction hotspots
 Hot spots are usually conserved and appear to be clustered
in tightly packed regions in the center of the interface
 Experimental identification by alanine scanning mutagenesis
 if a residue has a significant drop in binding affinity when
mutated to alanine it is labeled as a hot spot
 Experimental identification of hot spots is costly and
cumbersome → the computational predictions of hot spots
can help!
Analysis of macromolecular complexes – interaction hotspots 87
Prediction of hotspots - tools
 Most of the available methods are based on the 3D structure
of the complex
 Knowledge-based methods
 Combination of several physicochemical features
 Evolutionary conservation, ASA, residue propensity, structural
location, hydrophobicity,...)
 Energy-based methods
 Calculation of the change in the binding free energy (∆∆Gbind) of the
complex upon in silico modification of a given residue to alanine
Analysis of macromolecular complexes – interaction hotspots 88
Prediction of hotspots - tools
 Robetta
 http://old.robetta.org/alascansubmit.jsp
 Energy-based method
 Performs in silico alanine scanning mutagenesis of protein-protein or
protein-DNA interface residues
1. The side chain of each interface residue is mutated to alanine
2. All side chains within 5 Å radius sphere of the mutated residue are
repacked; the rest of the protein remains unchanged
3. For each mutant, ∆∆Gbind is calculated (residues with predicted
∆∆Gbind ≥ +1 kcal/mol = hot spot)
Analysis of macromolecular complexes – interaction hotspots 89
Prediction of hotspots - tools
 Robetta
Analysis of macromolecular complexes – interaction hotspots 90
Prediction of hotspots - tools
 KFC2 (Knowledge-based FADE and Contacts)
 https://mitchell-web.ornl.gov/KFC_Server/
 Knowledge-based method utilizing machine learning
 Predicts hot spots in protein-protein interfaces by recognizing
features of important binding contacts – solvent accessibility, residue
position within the interface, packing density, residue size, flexibility
and hydrophobicity of residues around the target residue
 Optionally, user can provide data to improve the prediction (ConSurf
conservation scores, Rosetta alanine scanning results or
experimental data)
91
Prediction of hotspots - tools
 KFC2 (Knowledge-based FADE and Contacts)
92
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assisted folding. Journal of Molecular Recognition 14: 42-61.
 Ali, M. H. & Imperiali, B. (2005) Protein oligomerization: How and why. Bioorganic &
Medicinal Chemistry 13: 5013-5020.
 Jahn, T. R. & Radford, S. E. (2008) Folding versus aggregation: Polypeptide conformations
on competing pathways. Archives of Biochemistry and Biophysics 469: 100-117.
 Csermely, P. et al. (2010) Induced fit, conformational selection and independent dynamic
segments: an extended view of binding events. Trends in Biochemical Sciences 35: 539-
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