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Protein tertiary structure

Protein tertiary structure refers to the complete three-dimensional conformation of a protein, encompassing its polypeptide backbone and all side chains, which arises from the folding of the chain into a specific spatial arrangement.^[1] This level of structure is essential for determining a protein's functional properties, as it positions key residues to enable interactions with other molecules, such as substrates in enzymes or ligands in receptors.^[1] Unlike primary structure, which is the linear sequence of amino acids, or secondary structure, which involves local folding patterns like alpha helices and beta sheets, tertiary structure integrates these elements into a global fold stabilized by non-covalent and covalent interactions.^[1] The formation of tertiary structure is primarily dictated by the protein's amino acid sequence, with folding occurring spontaneously under physiological conditions, often assisted by molecular chaperones to prevent aggregation.^[1] Key stabilizing interactions include hydrophobic interactions, where nonpolar side chains cluster in the protein's interior away from water; hydrogen bonds between polar groups; ionic bonds or salt bridges between oppositely charged residues; van der Waals forces providing weak attractions between closely packed atoms; and disulfide bonds, which are covalent linkages between cysteine residues that lock distant parts of the chain together.^[1] These forces collectively minimize free energy, driving the polypeptide to adopt its native, functional conformation, which can be reversible— as demonstrated by experiments where denatured proteins like ribonuclease refold correctly upon removal of disrupting agents.^[1] Environmental factors such as pH, temperature, and ionic strength can influence this folding process.^[2] Tertiary structure is crucial for protein stability and biological activity, with disruptions often leading to loss of function or pathological states.^[1] For instance, globular proteins like hemoglobin exhibit compact tertiary folds that facilitate oxygen transport, while fibrous proteins such as collagen form elongated structures for tensile strength.^[3] Clinically, mutations altering the primary sequence can misfold the tertiary structure, contributing to diseases including cystic fibrosis (due to defective CFTR channel folding), Alzheimer's disease (via amyloid-beta aggregation), and prion disorders (from conformational changes in PrP protein).^[1]^[4] Experimental determination of tertiary structures relies on techniques like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy, which have revealed atomic-level details for thousands of proteins.^[5]

Overview

Definition

Protein tertiary structure refers to the three-dimensional spatial arrangement of a polypeptide chain, achieved through folding that brings distant amino acid residues into close proximity via interactions between their side chains, known as R-groups.^[1] This level of structure builds upon secondary elements, such as alpha helices and beta sheets, which serve as the foundational motifs that pack together to form the overall fold.^[5] The stability of tertiary structure is maintained by several key non-covalent and covalent interactions. Hydrophobic effects drive non-polar R-groups to cluster in the protein's interior, away from water, while hydrogen bonds form between polar groups, ionic bonds (or salt bridges) occur between oppositely charged residues, disulfide bridges provide covalent links between cysteine residues, and van der Waals forces contribute weak attractions between closely packed atoms.^[1] These interactions collectively minimize the protein's energy state, enabling a compact conformation essential for function.^[6] In many proteins, secondary structure motifs fold into a compact globule, exemplified by globular proteins like hemoglobin, which adopt spherical shapes for solubility and enzymatic activity in aqueous environments. In contrast, fibrous proteins, such as collagen, exhibit elongated, rope-like tertiary structures suited for structural support and tensile strength.^[7] The folding process is thermodynamically driven by the minimization of Gibbs free energy, described by the equation

\Delta G = \Delta H - T\Delta S

where \Delta G is the change in free energy, \Delta H is the enthalpy change, T is the temperature, and \Delta S is the entropy change; a negative \Delta G favors the folded state as the most stable conformation.^[6]

Comparison with other structural levels

Protein tertiary structure represents the three-dimensional folding of a single polypeptide chain, building directly upon the primary structure, which is defined as the linear sequence of amino acids connected by covalent peptide bonds. This foundational sequence, first elucidated for insulin by Frederick Sanger in the early 1950s, dictates the potential for higher-order folding through the chemical properties of the amino acid side chains. In contrast to the one-dimensional nature of primary structure, tertiary structure achieves a compact, globular form that positions distant residues in space for functional interactions. Secondary structure encompasses local conformational motifs within the polypeptide chain, such as α-helices and β-sheets, primarily stabilized by hydrogen bonds between the backbone carbonyl oxygen and amide hydrogen atoms. Proposed by Linus Pauling and Robert Corey in 1951, these elements represent repetitive patterns that emerge early in folding but do not capture the full spatial organization of the protein. Tertiary structure, however, integrates these secondary motifs into a global architecture, where interactions between non-adjacent side chains—such as hydrophobic clustering or occasional disulfide bridges—bridge separate secondary elements to define the overall shape. Quaternary structure arises from the non-covalent or covalent association of two or more polypeptide subunits, each of which typically possesses its own tertiary structure, to form a multi-chain complex essential for certain proteins like hemoglobin. Unlike quaternary assemblies, which involve inter-chain contacts across multiple folded units, tertiary structure is confined to the intramolecular folding of an individual chain, though that chain may later participate in quaternary interactions. This distinction highlights how tertiary structure provides the stable subunit scaffold necessary for higher-level oligomerization. The hierarchy of protein structures is often depicted in schematic diagrams progressing from the linear primary sequence (1D) through local secondary folds (2D-like elements) to the fully realized tertiary 3D conformation of a single chain, and culminating in quaternary multi-subunit arrangements (beyond 3D). These visualizations underscore the sequential dependency, where alterations at the primary level can propagate through secondary and tertiary levels to affect quaternary assembly.

Historical development

Early theories

In the 1920s and 1930s, protein structure was often interpreted through the lens of colloid chemistry, which viewed globular proteins as compact, spherical molecular colloids with defined molecular weights, as demonstrated by Theodor Svedberg's ultracentrifugation studies that established proteins like hemoglobin as discrete molecules rather than indefinite aggregates. This perspective emphasized the compactness of globular proteins to maintain solubility and stability in aqueous environments, influenced by colloidal principles that highlighted surface tension and solvation effects. A key early conceptual advance came from Hsien Wu's 1931 theory of protein denaturation, which posited that native globular proteins adopt a highly compact configuration where nonpolar (hydrophobic) side chains are internalized, shielded from water, while denaturation disrupts this arrangement and exposes these groups, leading to aggregation or solubility changes. Dorothy Maud Wrinch proposed the cyclol theory in the late 1930s, suggesting that globular proteins form rigid, cage-like polyhedral structures through covalent cyclol bonds—resulting from dehydration reactions between peptide bonds and side-chain carboxyl or hydroxyl groups—creating a fabric of cross-linked hexagons and pentagons that encapsulate the protein's interior.^[8] This model aimed to explain the stability and globular shape of proteins like insulin, predicting flat, basket-like architectures without relying on non-covalent interactions. However, the theory was largely disproven by the late 1930s due to inconsistencies with bond energy calculations and emerging X-ray diffraction data, as Linus Pauling and Carl Niemann demonstrated that cyclol linkages would be thermodynamically unstable compared to standard peptide bonds. Linus Pauling contributed foundational ideas in 1951 by proposing the alpha helix and beta-pleated sheet as regular secondary structures for polypeptide chains, allowing for compact folding while satisfying hydrogen bonding and stereochemical constraints, thereby laying groundwork for understanding how linear amino acid sequences might achieve tertiary compactness.^[9]^[10] These early theories, while innovative, were constrained by the absence of atomic-resolution X-ray crystallographic data, limiting models to speculative geometries based on chemical intuition and low-resolution fiber diffraction patterns, which could not yet distinguish precise side-chain arrangements or confirm hydrophobic burial.

Key milestones

In 1960, John Kendrew and colleagues determined the first high-resolution three-dimensional structure of a protein, sperm whale myoglobin, using X-ray crystallography at 2 Å resolution, revealing a compact globular fold dominated by an alpha-helical bundle comprising about 75% of the polypeptide chain with eight helices arranged around the heme prosthetic group.^[11] This breakthrough provided the initial empirical visualization of protein tertiary structure, demonstrating how the amino acid chain folds into a functional domain without beta-sheets and highlighting the role of hydrophobic interactions in stabilizing the core.^[11] The 1969 formulation of Levinthal's paradox by Cyrus Levinthal underscored the computational improbability of random searching for the native tertiary structure, estimating that a 100-residue protein with approximately three possible conformations per residue could adopt around $10^{27} states, which at a rate of $10^{13} conformations per second would require over $10^{27} years to explore—far exceeding the age of the universe—thus implying guided folding pathways rather than exhaustive trials.^[12] Christian Anfinsen's experiments in the early 1960s, culminating in his 1972 Nobel Prize in Chemistry, demonstrated through in vitro refolding of denatured ribonuclease A that the enzyme spontaneously regained full activity after removal of denaturants and reoxidation of its four disulfide bonds, with only one of 105 possible pairings yielding the native structure, establishing the thermodynamic hypothesis that the primary amino acid sequence encodes all information necessary for achieving the lowest free-energy tertiary fold under physiological conditions.^[13] This principle was further articulated in Anfinsen's 1973 overview, emphasizing that tertiary structure emerges from sequence-directed minimization of Gibbs free energy without requiring additional genetic instructions.^[14] During the 1980s and 1990s, advances in disulfide bond mapping, pioneered by Thomas Creighton's studies on bovine pancreatic trypsin inhibitor (BPTI), elucidated oxidative folding pathways by isolating productive intermediates with specific disulfide pairings—such as the initial 30-51 and 5-55 bonds—revealing that tertiary structure formation couples sequentially with covalent cross-linking to avoid kinetic traps in vivo. Concurrently, the discovery of molecular chaperones transformed understanding of assisted folding; R. John Ellis coined the term in 1987 to describe proteins like Rubisco-binding protein that prevent unproductive aggregation during assembly, while Sean Hemmingsen's 1988 identification of bacterial GroEL (now a chaperonin) as a homolog essential for oligomeric protein maturation highlighted their role in facilitating tertiary structure attainment in crowded cellular environments.^[15]^[16] These developments bridged Anfinsen's in vitro insights with in vivo mechanisms, showing chaperones such as Hsp70 and GroEL/GroES actively guide nascent chains toward native folds without specifying the final structure.^[16]

Formation and stability

Folding mechanisms

Protein folding mechanisms encompass the kinetic pathways through which a linear polypeptide chain assembles into its compact tertiary structure, driven by the interplay of conformational entropy and intramolecular interactions. Small proteins, typically under 100 residues, often fold via a two-state model, characterized by a highly cooperative transition from a heterogeneous unfolded ensemble to the unique native state, with no detectable stable intermediates on the folding timescale.^[17] This process reflects rapid equilibration among unfolded conformations prior to barrier crossing, ensuring efficient folding without kinetic partitioning into off-pathway states.^[17] Larger or multi-domain proteins, however, generally exhibit multi-state folding kinetics, involving populated intermediates that bridge the unfolded and native states, allowing sequential assembly of structural modules.^[18] These pathways arise from the increased conformational complexity, where partial folding of subdomains precedes global compaction, reducing the risk of aggregation but extending overall folding times.^[18] The energy landscape theory conceptualizes these mechanisms as navigation of a rugged, funnel-shaped free energy surface, where the native state forms a deep global minimum and progressive structure formation narrows the accessible conformations, guiding the chain downhill despite local roughness that can create kinetic traps.^[19] This statistical view emphasizes evolutionary selection for minimally frustrated landscapes that minimize trapping and promote rapid folding to the thermodynamically stable native basin.^[19] A prominent intermediate in multi-state folding is the molten globule, a compact state with significant native-like secondary structure but fluctuating tertiary contacts and exposed hydrophobic regions, serving as a transient platform for subsequent tight packing.^[20] This ensemble lacks a fixed side-chain topology yet retains a globular shape, bridging early collapse and late-stage refinement in the folding funnel.^[20] Stopped-flow fluorescence spectroscopy provides direct experimental evidence for these mechanisms, monitoring tryptophan burial or environmental changes on millisecond timescales to resolve folding phases, such as submillisecond collapse to molten globule-like states followed by slower native rearrangements in proteins like cytochrome c.^[21] These techniques reveal chevron plots and relaxation kinetics that validate two-state cooperativity in small proteins and intermediate accumulation in larger ones.^[21] In cellular environments, molecular chaperones briefly assist these pathways by preventing unproductive aggregates.

Physicochemical determinants

The tertiary structure of proteins is primarily stabilized by non-covalent and covalent interactions between amino acid side chains, which collectively minimize free energy and drive the polypeptide chain to adopt its native conformation. These physicochemical determinants include the hydrophobic effect, hydrogen bonding, electrostatic interactions, disulfide bonds, and van der Waals forces, each contributing to the burial of nonpolar residues in the core and the precise packing of the structure. The hydrophobic effect serves as the dominant force, while the others provide specificity and additional stability, particularly in the protein interior where water is excluded.^[22] The hydrophobic effect arises from the tendency of nonpolar amino acid side chains, such as those of leucine, valine, and phenylalanine, to aggregate in the protein's interior, away from the aqueous environment. This burial releases structured water molecules surrounding hydrophobic groups, leading to an increase in solvent entropy that thermodynamically favors the folded state. Originally proposed by Kauzmann in 1959 as a key driver of protein folding, this effect accounts for much of the free energy change during collapse of the polypeptide chain, with estimates suggesting it contributes 20-30 kJ/mol per residue pair in the core. In globular proteins like myoglobin, nonpolar residues occupy over 50% of the buried surface area, exemplifying how this entropic gain enforces compact tertiary structures.^[22]^[23] Hydrogen bonding between polar side chains, such as those of serine, threonine, and asparagine, supplements the backbone hydrogen bonds of secondary structures and enhances tertiary stability. These bonds form when a hydrogen atom attached to an electronegative atom (donor) interacts with another electronegative atom (acceptor), typically with energies of 10-40 kJ/mol, though their net contribution to folding is context-dependent due to competition with water solvation. In the protein interior, side-chain hydrogen bonds are stronger by 1-5 kJ/mol compared to solvent-exposed ones, as desolvation penalties are offset by the low dielectric environment. For instance, in ribonuclease A, side-chain hydrogen bonds between glutamine and aspartate residues help lock distant parts of the chain together, contributing to overall rigidity.^[24]^[25] Electrostatic interactions, particularly salt bridges between oppositely charged residues like aspartate or glutamate (negative) and lysine or arginine (positive), provide long-range stabilization that is sensitive to pH and ionic strength. A salt bridge involves both ionic attraction and hydrogen bonding components, with typical interaction energies of 5-15 kJ/mol in low-dielectric protein interiors, though solvation effects can reduce this to near-neutral in some cases. These bridges are more prevalent on protein surfaces but can anchor core elements; their disruption often lowers melting temperatures by 2-10°C, as seen in engineered variants of barnase where removing a key Asp-Lys bridge destabilizes the structure. pH changes that protonate or deprotonate residues can modulate these interactions, influencing tertiary conformation in responsive proteins.^[26]^[27] Covalent disulfide bonds, formed by the oxidation of thiol groups between cysteine residues, impart exceptional stability to tertiary structures, especially in extracellular proteins exposed to oxidative environments. These S-S linkages, with bond energies around 200 kJ/mol, covalently cross-link distant parts of the chain, reducing entropy loss upon unfolding and preventing misfolding. Disulfide bonds are rare in cytosolic proteins due to reducing conditions but are abundant in secreted ones, where they can constitute up to 10% of cysteines. A classic example is insulin, an extracellular hormone stabilized by three disulfide bonds: two intra-chain in the A subunit and one inter-chain linking A and B subunits, which are essential for its compact tertiary fold and biological activity; mutation of these cysteines leads to diabetes-associated instability.^[28]^[29] Van der Waals interactions and pi-stacking contribute to the tight packing of the hydrophobic core, where atoms approach within 3-4 Å to enable weak attractive forces. Van der Waals forces, encompassing London dispersion and other non-specific attractions, each provide 1-5 kJ/mol but accumulate significantly in densely packed interiors, filling voids and optimizing surface complementarity; in lysozyme, core packing efficiency reaches 75% via thousands of such contacts. Pi-stacking occurs between aromatic rings of phenylalanine, tyrosine, or tryptophan, involving parallel or offset overlaps stabilized by dispersion forces rather than electrostatics, with energies up to 10 kJ/mol per pair. These interactions are common in protein cores, as in the beta-barrel of triosephosphate isomerase, where stacked aromatics enhance stability without covalent links.^[1]^[30]

Role of molecular chaperones

Molecular chaperones play a crucial role in protein tertiary structure formation by facilitating the correct folding of nascent or unfolded polypeptides, shielding hydrophobic regions to prevent off-pathway aggregation, and providing a controlled environment for conformational rearrangements. These ATP-dependent or independent proteins do not impart the final fold but instead mitigate kinetic traps during folding, ensuring efficient attainment of the native tertiary structure. The Hsp70 family of chaperones, including DnaK in bacteria and Hsp70/Hsc70 in eukaryotes, binds transiently to exposed hydrophobic segments of unfolded proteins via an ATP-regulated substrate-binding domain. This binding, which occurs in an ATP-dependent manner, stabilizes extended conformations and prevents intermolecular hydrophobic interactions that lead to aggregation, thereby allowing time for productive folding. ATP hydrolysis locks the substrate in a high-affinity state, while nucleotide exchange promotes release, cycling the chaperone for reuse; co-chaperones like DnaJ stimulate ATPase activity to enhance efficiency.^[31] In bacteria, the GroEL/GroES chaperonin system provides an isolated, cage-like compartment for folding. GroEL forms a cylindrical double-ring structure with a central cavity that accommodates unfolded substrates, binding them to hydrophobic residues lining the inner wall. Upon ATP binding and GroES capping, the cavity expands and becomes hydrophilic, releasing the substrate into a sequestered environment where it can fold without interference from cellular crowding or aggregation; subsequent ATP hydrolysis and GroES dissociation release the folded protein. This system is essential for approximately 10-15% of bacterial proteins that obligately require chaperonin assistance for tertiary structure attainment.^[32] Trigger factor serves as a ribosome-associated chaperone in bacteria, aiding co-translational folding of nascent chains emerging from the ribosomal exit tunnel. It docks via the ribosomal protein L23 and extends over the tunnel exit to form a cradle-like structure that confines the nascent polypeptide, preventing premature domain folding or aggregation by countering local compaction and unfolding stable secondary structures as needed. This ribosome-bound action ensures nascent chains adopt compatible conformations early in synthesis, reducing misfolding risks during tertiary structure development.^[33] In eukaryotic cells, calnexin and calreticulin form a lectin-based chaperone system in the endoplasmic reticulum (ER) specialized for glycoprotein folding. These chaperones bind monoglucosylated N-linked glycans on nascent glycoproteins, retaining them in the ER to allow iterative cycles of folding and quality control; calnexin is membrane-anchored, while calreticulin is soluble, but both recruit folding enzymes like protein disulfide isomerase. This glycan-mediated interaction promotes proper tertiary domain assembly and oligomerization, preventing export of misfolded glycoproteins.^[34] Dysfunction or mutations in molecular chaperones underscore their essential role, as evidenced in folding diseases; for instance, in cystic fibrosis caused by CFTR mutations like ΔF508, impaired chaperone interactions (e.g., reduced Hsp70 or calnexin binding) fail to rescue the misfolded protein from ER degradation, leading to loss of functional tertiary structure and ion channel activity. Similarly, mutations in Hsp70 family members have been linked to neurodegenerative disorders where protein aggregation overwhelms chaperone capacity.^[35]

Environmental factors

The cellular environment significantly influences the formation and stability of protein tertiary structures through various abiotic factors. Macromolecular crowding in the cytoplasm, where protein concentrations can reach 200–300 mg/mL, promotes compact folding by excluded volume effects, which entropically favor the native state over extended conformations.^[36]^[37] This congestion restricts the accessible volume for unfolded chains, accelerating folding rates and stabilizing tertiary interactions without altering the underlying energy landscape.^[38] pH and ionic strength modulate electrostatic interactions critical to tertiary structure, as changes in protonation states of residues like histidines and aspartates alter salt bridges and repulsion forces. In hemoglobin, the Bohr effect exemplifies this: at lower pH, protonation of specific residues stabilizes the deoxy (T-state) quaternary and tertiary conformation, reducing oxygen affinity and facilitating cooperative binding.^[39] Higher ionic strength screens these charges, weakening electrostatic contributions and potentially shifting equilibrium toward less stable conformers.^[40] Temperature elevations increase thermal motion, disrupting weak non-covalent interactions, while chemical denaturants like urea and guanidinium chloride (GdnHCl) specifically target hydrogen bonds in the protein backbone and side chains, leading to unfolding. Urea penetrates the hydration shell and forms direct hydrogen bonds with peptide groups, solvating the unfolded state more favorably than the native fold.^[41] GdnHCl, in contrast, disrupts via stronger ionic interactions but similarly destabilizes tertiary structure by weakening intramolecular hydrogen bonding networks.^[42] The redox potential of the cellular compartment controls disulfide bond formation, which covalently stabilizes tertiary structures in many extracellular proteins. In the oxidizing periplasm of bacteria, enzymes like DsbA facilitate disulfide pairing, promoting correct folding of secreted proteins.^[43] Conversely, the reducing cytoplasm, maintained by thioredoxins and glutaredoxins, prevents aberrant disulfide formation, ensuring cytoplasmic proteins remain in reduced, non-covalent tertiary states.^[44] Recent studies highlight osmolytes such as trimethylamine N-oxide (TMAO) in counteracting environmental stress on tertiary structure. Under osmotic or thermal stress, TMAO preferentially excludes from the protein surface, enhancing water-mediated interactions and stabilizing the native fold against denaturation.^[45] In plants and marine organisms, TMAO accumulation protects proteomes during abiotic challenges, with in situ analyses showing it stabilizes over 60% of proteins by reinforcing hydrophobic cores.^[46]

Experimental determination

X-ray crystallography

X-ray crystallography is a primary experimental technique for determining the three-dimensional tertiary structure of proteins at atomic resolution, relying on the diffraction of X-rays by ordered protein crystals to reconstruct electron density maps. The process begins with the purification and crystallization of the protein, where a concentrated protein solution is subjected to various conditions such as pH, temperature, and precipitating agents to form well-ordered crystals, typically containing multiple copies of the protein molecule in a repeating lattice.^[47] Once crystals are obtained, they are exposed to a beam of X-rays, which interact with the electrons in the protein atoms, producing a diffraction pattern of spots on a detector; the intensity of these spots provides information about the amplitudes of the structure factors, but the phases must be determined separately to compute the electron density.^[48] The phase problem is solved using methods such as multiple isomorphous replacement (MIR), where heavy atoms like mercury or platinum are introduced into the protein to create derivative crystals, allowing phase calculation from differences in diffraction intensities between native and derivative datasets, or molecular replacement (MR), which uses a known homologous structure as a search model to estimate initial phases.^[49] After phasing, the electron density map is fitted with an atomic model, which is iteratively refined to minimize discrepancies between observed and calculated structure factors.^[50] The technique achieved its first major milestones in protein structure determination with the elucidation of myoglobin in 1958 by John Kendrew and colleagues, who reported a 2 Å resolution model revealing the protein's predominantly helical fold and heme-binding pocket, marking the initial atomic-level view of a globular protein. This was followed in 1960 by Max Perutz's team solving the structure of hemoglobin at 5.5 Å resolution, later improved, which demonstrated the quaternary arrangement of its four subunits and laid the groundwork for understanding allosteric regulation in proteins. These pioneering works, for which Kendrew and Perutz shared the 1962 Nobel Prize in Chemistry, established X-ray crystallography as indispensable for tertiary structure analysis.^[51] Protein structures determined by X-ray crystallography typically achieve resolutions of 1-2 Å, enabling precise placement of atoms and identification of side-chain conformations in small to medium-sized proteins up to about 100 kDa, though larger complexes can be studied with advancements in beamline technology. A key advantage is its ability to provide high-resolution static snapshots of the protein in a crystalline environment, offering detailed insights into active sites, ligand binding, and folding motifs that are crucial for functional studies.^[52] However, the method's disadvantages include the necessity for high-quality crystals, which can be challenging to obtain for flexible or membrane proteins, and the provision of only frozen, non-dynamic views that may not fully represent solution-state conformations.^[53] Refinement of the atomic model is often performed using software like PHENIX, which automates coordinate, occupancy, and thermal parameter adjustments while incorporating geometric restraints to ensure stereochemical accuracy.^[54] This technique is frequently complemented by other methods for validating dynamic aspects of tertiary structure.^[52]

Nuclear magnetic resonance spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy determines protein tertiary structures in solution by measuring nuclear spin interactions that report on atomic distances, bond angles, and chemical environments. Key techniques include Nuclear Overhauser Effect Spectroscopy (NOESY), which detects through-space dipole-dipole interactions between protons typically separated by less than 5 Å, providing distance restraints essential for modeling three-dimensional folds.^[55] Rotating-frame Overhauser Effect Spectroscopy (ROESY) complements NOESY for larger proteins by mitigating artifacts from molecular tumbling rates, yielding similar short-range distance information. Chemical shift indexing, such as the Chemical Shift Index (CSI) method, assigns secondary and tertiary structural elements by comparing observed NMR chemical shifts of backbone atoms (e.g., ^{13}C^\alpha, ^{13}C^\beta, ^{15}N^\mathrm{H}, ^{1}H^\mathrm{N}) to random coil values, with deviations indicating \alpha-helices, \beta-sheets, or turns.^[56] These assignments integrate with distance restraints to refine tertiary topology using structure calculation software like CYANA or ARIA.^[57] Isotope labeling with ^{13}C and ^{15}N is crucial for overcoming spectral overlap in proteins, enabling multidimensional experiments (e.g., 3D ^{1}H-^{15}N HSQC, ^{13}C-edited NOESY) that correlate resonances across nuclei for unambiguous assignment and restraint collection.^[58] Uniform labeling incorporates these isotopes into all relevant sites during recombinant expression, while selective labeling targets specific residues to simplify spectra.^[59] This approach extends the practical size limit of solution NMR to proteins up to approximately 50 kDa, beyond which signal broadening from slow tumbling limits resolution, though deuteration (^2H) further reduces relaxation and enables studies of systems approaching 100 kDa.^[60] NMR uniquely captures dynamic aspects of tertiary structure, revealing conformational flexibility in loops, hinges, or domain interfaces through measurements of longitudinal (R_1) and transverse (R_2) relaxation rates of ^{15}N nuclei.^[61] Elevated R_2 rates indicate millisecond-to-microsecond motions, such as loop fluctuations that modulate active sites, while order parameters (S^2) from model-free analysis quantify picosecond-to-nanosecond dynamics, showing restricted motion in core regions versus higher amplitude in solvent-exposed loops.^[62] These data complement static structures by illustrating how tertiary architecture accommodates functional motions, as in enzyme catalysis or ligand binding.^[63] An early landmark in NMR-based tertiary structure determination was the solution structure of ubiquitin, a 76-residue protein, solved in the mid-1980s using 2D NOESY and sequential assignment strategies, which revealed its compact \alpha/\beta fold with a mixed parallel/antiparallel \beta-sheet and \alpha-helix.^[64] This work, building on resonance assignments from ^{1}H NMR, demonstrated NMR's feasibility for globular proteins and established ubiquitin as a benchmark for validation against X-ray crystallography.^[65] Recent advances in solid-state NMR (ssNMR) have expanded applications to larger systems and challenging targets like membrane proteins, where solution methods falter due to insolubility or aggregation.^[66] Techniques such as magic-angle spinning and dynamic nuclear polarization enhance sensitivity, allowing atomic-resolution structures of proteins exceeding 50 kDa in lipid bilayers, including interhelical distances via ^{13}C-^{13}C correlations and torsion angles from chemical shifts.^[67] For instance, ssNMR has elucidated the tertiary arrangement of bacterial transporters in native-like membranes, capturing lipid-protein interactions absent in detergent-solubilized solution studies.^[68] These developments integrate solution and solid-state data for hybrid models, providing comprehensive views of tertiary structure in heterogeneous environments.^[69]

Cryo-electron microscopy

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the three-dimensional tertiary structures of proteins, particularly those forming large macromolecular complexes or embedded in membranes. The process begins with the preparation of protein samples, which are applied to a grid and flash-frozen in liquid ethane to form a thin layer of vitreous ice, preserving the native conformation without crystallization.^[70] These frozen samples are then imaged using a transmission electron microscope, where an electron beam passes through the specimen to generate two-dimensional projections. Through single-particle reconstruction, thousands of such projections are computationally aligned and averaged to reconstruct a high-resolution three-dimensional model of the protein's tertiary structure.^[71] Significant advancements in cryo-EM resolution were achieved in the 2010s with the introduction of direct electron detectors, which capture images with reduced motion blur and higher sensitivity, enabling routine atomic-level resolutions below 3 Å. This "resolution revolution" was recognized by the 2017 Nobel Prize in Chemistry awarded to Jacques Dubochet, Joachim Frank, and Richard Henderson for their foundational contributions to cryo-EM methodology, including vitreous ice embedding and image processing algorithms.^[72] Cryo-EM excels in visualizing protein tertiary structures that exhibit conformational heterogeneity, as it can classify and reconstruct multiple states from a single dataset, and it is particularly advantageous for membrane proteins, which often resist crystallization required by X-ray methods.^[73] For instance, in 2020, cryo-EM structures of the SARS-CoV-2 spike protein at resolutions around 3 Å revealed distinct prefusion and postfusion conformational states, providing critical insights into viral entry mechanisms and guiding vaccine design.^[74] As of 2025, the integration of artificial intelligence has further enhanced cryo-EM workflows, particularly through AI-assisted particle picking, which automates the identification of protein particles in noisy micrographs, significantly improving throughput and accuracy for complex datasets.^[75] Cryo-EM maps can also be briefly integrated with X-ray crystallography data to produce hybrid models that refine local tertiary features.^[76]

Other biophysical methods

Dual polarization interferometry (DPI) is a surface-sensitive optical technique that measures the thickness, refractive index, and mass density of protein layers adsorbed onto waveguides in real time, providing insights into conformational changes and tertiary structure during adsorption or binding events.^[77] For instance, DPI has been used to detect structural rearrangements in proteins like β-lactoglobulin at oil-water interfaces, where changes in layer density indicate shifts from native to partially unfolded tertiary states.^[78] This method is particularly valuable for studying protein-membrane interactions, as demonstrated in analyses of peptide foldamers binding to lipid bilayers, where DPI quantifies conformational adaptations at the nanoscale.^[79] Fluorescence resonance energy transfer (FRET) probes protein tertiary structure by measuring distances (typically 1-10 nm) between donor and acceptor fluorophores attached to specific residues, allowing inference of folding dynamics and domain orientations in solution.^[80] In seminal single-molecule FRET studies, folded and unfolded subpopulations of proteins like cold shock protein B were distinguished during diffusion, revealing heterogeneity in tertiary structure formation.^[81] FRET has also mapped conformational changes in multidomain proteins, such as voltage-gated calcium channels, by quantifying energy transfer efficiency between labeled cytoplasmic domains.^[82] Circular dichroism (CD) spectroscopy assesses protein tertiary structure through near-UV (250-300 nm) spectral signatures arising from the asymmetric environments of aromatic residues (tryptophan, tyrosine, phenylalanine) and disulfide bonds, complementing far-UV measurements of secondary structure.30317-1) For example, perturbations in near-UV CD bands indicate tertiary disruptions during protein engineering or stability assessments, as seen in studies of engineered antibodies where spectral shifts correlate with folding integrity.^[83] The technique is widely applied to monitor unfolding transitions, providing quantitative data on the stability of tertiary interactions in solution.^[84] Small-angle X-ray scattering (SAXS) yields low-resolution (1-2 nm) envelopes of protein tertiary structure in solution by analyzing X-ray scattering patterns from hydrated samples, capturing overall shape, flexibility, and conformational ensembles without crystallization.^[85] It excels for flexible or multidomain proteins, such as those modeled via ensemble optimization methods, where SAXS data refines predictions of native tertiary arrangements by comparing scattering profiles to theoretical models.^[86] Similarly, small-angle neutron scattering (SANS) provides analogous low-resolution structural information but leverages neutron contrast variation (e.g., via deuteration) to isolate tertiary features in complexes or crowded environments.^[87] SANS has elucidated protein-detergent interactions, revealing how surfactants preserve native tertiary folds in membrane proteins.^[88] These methods offer indirect probes of tertiary structure, often at lower resolution than primary techniques, and typically require integration with high-resolution data from NMR or cryo-EM for validation and atomic-level interpretation.^[89]

Computational prediction

Traditional algorithms

Traditional algorithms for protein tertiary structure prediction rely on physics-based simulations, template matching, and fragment-based assembly to model three-dimensional conformations from amino acid sequences, predating data-driven machine learning approaches. These methods emerged in the 1980s and 1990s, addressing the challenge of simulating folding pathways or inferring structures from evolutionary relatives.^[90] They typically achieve moderate success for proteins with detectable homologs but struggle with novel folds due to computational limitations.^[91] Ab initio methods simulate protein folding from first principles using empirical force fields to minimize potential energy. These approaches represent the protein as a collection of atoms or residues and apply physics-based potentials to explore conformational space, aiming to identify the native state as the global energy minimum. For instance, the CHARMM (Chemistry at HARvard Macromolecular Mechanics) force field models bonded and non-bonded interactions, including van der Waals, electrostatics, and hydrogen bonding terms, to drive molecular dynamics or minimization simulations of folding trajectories.^[92] Early implementations, such as those in the 1990s, successfully predicted small proteins (<100 residues) with root-mean-square deviation (RMSD) values around 5-10 Å from experimental structures, but required significant computational resources for larger systems.^[93] Homology modeling, also known as comparative modeling, constructs tertiary structures by aligning a target sequence to homologous proteins with known structures in the Protein Data Bank (PDB). The process involves identifying templates via sequence similarity searches (e.g., using BLAST), threading the target sequence onto the template backbone to account for insertions/deletions, and refining side-chain placements and loops using energy minimization. Threading, a key variant for low-homology cases, evaluates sequence-structure compatibility with statistical potentials derived from known folds, as pioneered in the early 1990s. This method excels when sequence identity exceeds 30%, yielding models with RMSD <2 Å, but accuracy drops for distant homologs due to alignment errors.^[94] Fragment assembly methods, exemplified by the Rosetta algorithm, build structures by combining short segments (3-9 residues) from PDB fragments that match local sequence patterns. These fragments are assembled via Monte Carlo sampling, guided by a scoring function that includes statistical terms for backbone torsion angles, hydrogen bonding, and solvation, alongside physics-inspired energy evaluations.^[95] Rosetta iteratively perturbs the chain and accepts low-energy conformations, effectively sampling folding funnels to generate decoys for clustering into native-like models. For small proteins, this approach produced topologies correct within 5-6 Å RMSD in the early 2000s.^[96] The Critical Assessment of Structure Prediction (CASP) competitions, initiated in 1994, provided blind benchmarks for these algorithms through the 2010s. In CASP1-CASP5 (1990s), homology and ab initio methods achieved Global Distance Test Total Score (GDT-TS) values around 50 for easy targets with clear templates, while hard targets scored below 30.^[97] By CASP9-CASP11 (2010-2014), refinements improved easy-target GDT-TS to 70-80, but free-modeling categories for novel folds remained at 20-40, highlighting persistent gaps.^[98] A core challenge for these methods is the Levinthal paradox, which posits that exhaustive sampling of conformational space (∼10^{300} states for a 100-residue protein) would take longer than the universe's age, yet proteins fold in milliseconds. Solutions involve biased sampling techniques like Monte Carlo methods, which explore energy landscapes with funnels leading to the native state, reducing the effective search space to ∼10^{10} semi-compact conformations via rapid collapse and multiple transition pathways.^[99] Lattice models demonstrated this resolution, showing folding times scale with chain length rather than exponentially.^[100]

AI and machine learning approaches

Artificial intelligence and machine learning have revolutionized protein tertiary structure prediction by leveraging large-scale datasets of known structures and evolutionary information to achieve unprecedented accuracy, often surpassing traditional methods that rely heavily on homology modeling. These approaches employ deep neural networks trained end-to-end on massive protein sequence and structure data, enabling predictions directly from amino acid sequences without requiring experimental templates in many cases. Seminal advancements, such as those from DeepMind and other research groups, have democratized access to high-fidelity models, accelerating research across biology and medicine. AlphaFold, developed by DeepMind, represents a landmark in this field, with its second iteration (AlphaFold 2) introduced in 2020 and detailed in 2021. The architecture features an Evoformer module that processes multiple sequence alignments (MSAs) and pairwise residue representations through attention-based layers to capture evolutionary and spatial relationships, followed by structure modules that iteratively refine atomic coordinates via invariant point attention. In the Critical Assessment of Structure Prediction (CASP14) competition held in 2020, AlphaFold 2 achieved median backbone root-mean-square deviation (RMSD) accuracies below 1 Å for many targets, outperforming all competitors and demonstrating near-atomic precision even for proteins without close homologs. This performance stemmed from training on over 100,000 experimentally determined structures from the Protein Data Bank (PDB), combined with vast genomic sequence data. Building on similar principles, RoseTTAFold, released in 2021 by the Baker lab at the University of Washington, employs a three-track neural network that jointly models sequence, structural distance, and orientation information, allowing for efficient prediction and enabling de novo protein design through diffusion-based sampling of novel folds. Unlike earlier template-dependent methods, RoseTTAFold's end-to-end learning facilitates rapid generation of diverse structures, with accuracies comparable to AlphaFold 2 on CASP14 benchmarks while requiring less computational resources, making it accessible for broader use in structure-based design. Meta AI's ESMFold, introduced in 2022, advances single-sequence prediction by integrating a protein language model (ESM-2) with a folding head that directly outputs 3D coordinates, bypassing the need for time-consuming MSAs. Trained on over 250 million protein sequences from metagenomic data, ESMFold achieves median GDT-TS scores of around 70 on diverse test sets, enabling predictions in seconds per protein—orders of magnitude faster than MSA-based systems—while maintaining high accuracy for monomeric structures. This approach highlights the power of transformer-based language models in capturing sequence patterns for folding without alignment dependencies. AlphaFold 3, unveiled by DeepMind in 2024, extends these capabilities to multimodal predictions, incorporating ligands, nucleic acids, and modified residues into joint complex modeling using a diffusion-based architecture that refines atomic positions progressively. It achieves up to 50% higher accuracy than prior tools on protein-ligand benchmarks, such as PoseBusters, facilitating predictions of biomolecular interactions critical for understanding cellular processes. Subsequent evaluations, including in CASP16 (2024), confirmed AlphaFold 3's leading performance, with high GDT-TS scores across diverse targets, further solidifying deep learning's role in the field.^[101] In October 2025, the OpenFold Consortium released OpenFold3, an open-source implementation inspired by AlphaFold 3, enhancing accessibility for non-commercial research and enabling custom fine-tuning for specialized applications.^[102] The collective impact of these AI methods has been profound: as of November 2025, AlphaFold has generated predictions for over 200 million protein structures, covering nearly all known sequences in UniProt and vastly expanding the PDB's utility beyond its ~200,000 experimental entries. These predictions have accelerated drug design by enabling virtual screening of protein-ligand interactions, with applications in identifying novel inhibitors for targets like kinases and proteases, reducing the time and cost of lead optimization in pharmaceutical pipelines.

Biological significance

Functional implications

The tertiary structure of proteins plays a crucial role in forming active sites that enable enzymatic catalysis by precisely positioning catalytic residues in three-dimensional space. In serine proteases, such as chymotrypsin and trypsin, the tertiary fold brings together a catalytic triad—consisting of serine, histidine, and aspartate residues—into a configuration that facilitates nucleophilic attack on peptide bonds, allowing substrate-specific hydrolysis. This spatial arrangement, stabilized by hydrogen bonds and hydrophobic interactions, ensures efficient proton transfer and transition state stabilization, underscoring how tertiary structure directly dictates enzymatic function.^[5] Allostery represents another key functional implication of tertiary structure, where ligand binding at one site induces conformational changes that propagate through the protein to modulate activity at a distant site, enabling regulatory control and signal transduction. In G-proteins, such as heterotrimeric GTPases, GTP binding triggers a tertiary rearrangement in the α-subunit, shifting switch regions to disrupt interactions with the βγ-subunits and activate downstream effectors like adenylyl cyclase, thereby amplifying cellular signals in pathways like vision and olfaction. This mechanism highlights the dynamic nature of tertiary folds, which allow proteins to act as molecular switches for precise physiological responses.^[103] In multi-domain proteins, tertiary structure manifests as independent folds for each domain, promoting modularity that enhances functional versatility through combinatorial assembly and specialized interactions. Domains typically range from 40 to 350 amino acids and fold autonomously, enabling proteins to perform multiple tasks; for instance, the PDZ domain in signaling proteins binds C-terminal peptides via a conserved carboxylate-binding loop, facilitating targeted protein-protein associations in pathways like synaptic transmission. This modularity allows evolutionary tinkering at domain interfaces while preserving core functions, contributing to the complexity of eukaryotic proteomes where approximately 67% of proteins are multi-domain.^[104]^[105] Evolutionary conservation of tertiary structures across homologous proteins ensures the preservation of functional scaffolds, even when primary sequences diverge significantly, thereby maintaining core activities over long timescales. Structural analyses reveal that related proteins often retain similar folds—such as Rossmann or TIM barrel motifs—despite low sequence identity, which supports conserved mechanisms like nucleotide binding in dehydrogenases; this conservation arises from purifying selection on structurally critical residues, linking evolutionary history directly to functional reliability.^[106] A prominent example of tertiary structure's functional role is seen in the variable regions of antibodies, where the immunoglobulin fold of VH and VL domains positions six complementarity-determining regions (CDRs) to form a paratope that binds antigens with high specificity and affinity. The tertiary arrangement creates a binding groove or pocket, with CDR-H3 providing the most variability for accommodating diverse epitopes, as in monoclonal antibodies targeting viral proteins; this structural plasticity, including elbow hinge flexibility, enables induced fit adjustments for effective immune recognition and neutralization.^[107]

Role in diseases

Disruptions in protein tertiary structure often underlie a range of diseases characterized by misfolding, aggregation, and loss of function, leading to cellular toxicity and organ dysfunction. These pathological conformations deviate from the native fold, promoting aberrant interactions that propagate disease states. In prion diseases, including Creutzfeldt-Jakob disease in humans and bovine spongiform encephalopathy in cattle, the cellular prion protein (PrP^C) undergoes a conformational shift from its predominantly α-helical tertiary structure to the β-sheet-enriched PrP^Sc isoform. This misfolded PrP^Sc forms amyloid aggregates that template further conversion of PrP^C, resulting in neuronal damage and spongiform encephalopathy.^[108]^[109] Alzheimer's disease features the accumulation of β-amyloid (Aβ) plaques, where soluble Aβ monomers misfold into β-sheet-dominated fibrillar structures that disrupt synaptic function and trigger neuroinflammation. Familial mutations in the amyloid precursor protein accelerate this tertiary misfolding, exacerbating plaque formation and cognitive decline.^[110]^[111] Cystic fibrosis arises primarily from the ΔF508 mutation in the cystic fibrosis transmembrane conductance regulator (CFTR), which destabilizes the nucleotide-binding domain 1 (NBD1) tertiary structure, causing global misfolding and retention in the endoplasmic reticulum. This prevents CFTR trafficking to the plasma membrane, impairing chloride ion transport and leading to mucus accumulation in lungs and pancreas.^[112]^[113] Serpinopathies, such as alpha-1-antitrypsin deficiency, involve mutations like the Z variant in alpha-1-antitrypsin (A1AT) that trap the protein in a polymerogenic intermediate conformation, characterized by aberrant β-sheet insertion and loop displacement in its tertiary structure. These polymers accumulate in hepatocytes, causing liver damage, while reduced circulating A1AT levels predispose to emphysema.^[114]^[115] Therapeutic strategies targeting tertiary structure disruptions include chemical chaperones, such as 4-phenylbutyrate, which stabilize folding intermediates to enhance mutant protein trafficking in cystic fibrosis, and proteostasis regulators that modulate chaperone activity or degradation pathways to reduce aggregates in amyloid diseases. Molecular chaperones like Hsp70 can briefly mitigate misfolding in these contexts by assisting refolding, though their overload in disease states often exacerbates pathology.^[116]^[117]

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The intact antibody molecule shown in Figure 1 has three functional components, two Fragment antigen binding domains (Fabs) and the fragment crystallizable (Fc) ...
[99]
Toward a mechanism of prion misfolding and structural models of ...
Physicochemical variables known to influence misfolding are reviewed to help elucidate the mechanism of prionogenesis and identify salient features of PrP(Sc), ...
[100]
New insights into structural determinants of prion protein folding and ...
Prions are the etiological agent of fatal neurodegenerative diseases called prion diseases or transmissible spongiform encephalopathies.
[101]
Biophysical Studies of the Amyloid β-peptide: Interactions With Metal ...
Sep 23, 2013 · Alzheimer's disease is the most common of the protein misfolding ("amyloid") diseases. The deposits in the brains of afflicted patients ...
[102]
Effects of Familial Alzheimer's Disease Mutations on the Folding ...
Oct 6, 2022 · Familial Alzheimer's disease (FAD) mutations of the amyloid β-peptide (Aβ) are known to lead to early onset and more aggressive Alzheimer's disease.
[103]
Requirements for efficient correction of ΔF508 CFTR ... - PubMed - NIH
Jan 20, 2012 · Misfolding of ΔF508 cystic fibrosis (CF) transmembrane conductance regulator (CFTR) underlies pathology in most CF patients.
[104]
Restoration of NBD1 thermal stability is necessary and ... - PubMed
Jan 16, 2015 · The ΔF508 mutation has two major impacts on CFTR: reduced thermal stability of NBD1 and disruption of its interface with membrane-spanning domains (MSDs).
[105]
Deficiency Mutations of Alpha-1 Antitrypsin. Effects on Folding ...
Misfolding, polymerization, and defective secretion of functional alpha-1 antitrypsin underlies the predisposition to severe liver and lung disease in alpha-1 ...<|control11|><|separator|>
[106]
Molecular pathogenesis of alpha-1-antitrypsin deficiency - PubMed
The mechanism of α1-AT deficiency provides a paradigm for a wider group of conditions that we have termed the serpinopathies. We also examine the strategies ...<|control11|><|separator|>
[107]
Innovative strategies to treat protein misfolding in inborn errors of ...
Apr 1, 2014 · Innovative strategies to treat protein misfolding in inborn errors of metabolism: pharmacological chaperones and proteostasis regulators.
[108]
Biological and chemical approaches to diseases of proteostasis ...
We review how small molecules can enhance proteostasis by binding to and stabilizing specific proteins (pharmacologic chaperones) or by increasing the ...