Protein stability prediction

What is protein stability?

Protein stability is the tendency of a protein to remain in its native, folded, functional state rather than unfold or populate non-native states. At equilibrium, thermodynamic stability is determined by the Gibbs free-energy difference between the folded and unfolded ensembles. Folding is favored when the folded state has the lower free energy under the specified conditions.

That free-energy balance emerges from several cooperating effects:

• Hydrophobic effect: Nonpolar side chains tend to become buried away from water, helping form the protein core.
• Hydrogen bonds: Backbone and side-chain hydrogen bonds help satisfy polar groups and organize helices, sheets, turns, and tertiary contacts.
• Electrostatic interactions: Attractions between charged groups, including salt bridges, can stabilize particular conformations. Their effects depend strongly on distance, solvent exposure, ionic strength, and pH.
• Van der Waals interactions: Close packing produces many individually weak contacts that collectively stabilize a well-packed structure.
• Disulfide bonds: Covalent links between cysteine residues can restrict the unfolded chain and stabilize the folded state, especially in extracellular proteins.

These favorable effects compete with the greater conformational entropy of the unfolded chain and with interactions between the protein and its solvent. A mutation can therefore strengthen one interaction while weakening the protein overall through poor packing, strain, lost solvation, or increased flexibility.

Protein stability always depends on conditions. Temperature, pH, ionic strength, solvent, cofactors, disulfide connectivity, oligomerization, and post-translational modifications can shift the folded-unfolded balance. Thermal stability, commonly reported as melting temperature ($T_m$), kinetic stability, aggregation resistance, and storage stability are related but distinct properties.

This tool calculates sequence-derived properties associated with several of those effects. It reports each property separately because instability index, aliphatic index, hydropathy, aromaticity, and charge do not measure the same physical endpoint.

How to compare protein sequence stability indicators

Paste one protein sequence or a multi-record FASTA file, choose the pH for the charge estimate, and select Calculate. The result table reports independent sequence descriptors. It does not combine them into an absolute stability score because instability index, thermostability tendency, hydropathy, and electrostatic charge describe different properties.

For this input at pH 7:

>canonical_20
ACDEFGHIKLMNPQRSTVWY

the result table displays these values, rounded for readability:

The classification belongs only to the Guruprasad instability index. It is not a general classification of thermodynamic, thermal, formulation, or shelf-life stability.

Input

Whitespace and lowercase letters are accepted. Ambiguous or nonstandard residue codes such as B, J, O, U, X, and Z are rejected because the published scales do not define every required value for them.

Line breaks within plain text wrap a single sequence. For example:

ACDEFGHIKL
MNPQRSTVWY

produces one 20-residue result named Sequence_1, with the same indicator values as the canonical_20 example above.

To compare multiple proteins, give each sequence its own FASTA header:

>protein_1
ACDEFGHIKLMNPQRSTVWY
>protein_2
KKKKDD

This produces two rows: protein_1 with 20 residues, followed by protein_2 with six residues. Each sequence is analyzed independently, including its own terminal groups in the charge estimate.

Setting

Results

Saved result data and workflow exports retain the full calculated precision. The table formats values to fewer decimal places for readability. For example, the canonical_20 charge at pH 7 is stored as -0.12492899297528393 and displayed as -0.12. Result metadata also records the Biopython version used.

How to interpret each indicator

Eight real sequences at pH 7 show how the columns behave. The first four are well-characterized soluble or membrane proteins that the tool labels Stable. The last four score above the instability cutoff. Sequences come from PDB (1LYZ, 1UBQ, 1C3W) and UniProt (full-length entries, including any signal peptide or initiator methionine).

Each indicator is read on its own scale, so one protein can look unremarkable on one column and extreme on another. Bacteriorhodopsin has the highest aliphatic index and the only strongly positive GRAVY, yet its instability index is low. The sections below explain what each column does and does not say.

Instability index

The Guruprasad instability index assigns weights to adjacent amino acid pairs:

where $L$ is sequence length and $DIWV$ is the published instability weight for each of the 400 dipeptides.

The weights come from a statistical comparison of 12 unstable and 32 stable proteins, where stability meant in vivo stability inside cells (Guruprasad et al., 1990). The ExPASy ProtParam documentation states the cutoff: below 40 is predicted stable, and above 40 the protein may be unstable (Gasteiger et al., 2005). The instability_classification column applies the same rule, with exactly 40.00 labeled Unstable.

The index is open-ended and has no unit, so the distance from 40 matters more than the label. Thioredoxin (8.15) and lysozyme (16.09) sit far below the cutoff. Human p53 (73.59), c-Fos (78.82), and ornithine decarboxylase (45.64) sit above it, and all three are proteins that cells turn over quickly. Preproinsulin at 40.33 is Unstable by 0.33, while ubiquitin at 36.06 is Stable; those two are not meaningfully different.

This is an empirical dipeptide-composition indicator derived from a small set of proteins. It is not a calculation of folding free energy, melting temperature, or degradation half-life under a specified experimental condition. ProtParam also reports an N-end rule half-life for human, yeast, and E. coli; this tool does not output one.

The index averages over the whole chain, so a short peptide has few dipeptide terms and can cross 40 on one or two heavy pairs. It scores whatever sequence is pasted, so a precursor with a signal peptide, a tag, or an initiator methionine gives a different value than the mature protein. Use the dedicated instability index calculator when this is the only metric needed.

Aliphatic index

The Ikai aliphatic index is:

where each $X$ is mole percent. Ikai found that the aliphatic index of proteins from thermophilic bacteria is significantly higher than that of ordinary proteins, and proposed it as a positive factor for the thermostability of globular proteins (Ikai, 1980). The index does not map directly to a melting temperature, and proteins can be stabilized by mechanisms that it does not represent.

The index is not a percentage and can exceed 100, because Val and Ile/Leu are weighted by 2.9 and 3.9. Bacteriorhodopsin scores 124.73 largely because its transmembrane helices are rich in Leu, Ile, Val, and Ala, which says nothing about thermostability by itself.

The index is most useful for comparing homologous proteins, such as an ortholog from a thermophile and one from a mesophile, and is poorly suited to ranking unrelated proteins. The aliphatic index calculator provides the same calculation by itself.

GRAVY and aromaticity

GRAVY is the arithmetic mean of the Kyte-Doolittle hydropathy values across the sequence:

Positive values indicate greater average hydrophobicity on this scale, while negative values indicate greater average hydrophilicity. Among the examples above, bacteriorhodopsin (a seven-helix membrane protein) reaches 0.888, while the soluble proteins range from -0.756 (p53) to 0.025 (thioredoxin).

A GRAVY near zero or above in a protein expected to be soluble is a reason to check for a transmembrane region or an exposed hydrophobic patch. GRAVY does not identify whether hydrophobic residues are buried, solvent exposed, or part of a transmembrane segment. Use the GRAVY calculator or hydropathy plot for focused hydropathy analysis.

Aromaticity is $(F + W + Y) / L$, the relative frequency of aromatic residues as defined by Lobry and Gautier. It is a composition statistic, not a count of aromatic contacts or a prediction of core packing. The value is a fraction from 0 to 1: ubiquitin, with three aromatic residues in 76, scores 0.039, and bacteriorhodopsin scores 0.144.

Estimated charge

The charge estimate applies the Bjellqvist pKa model implemented by Biopython 1.85. It uses the Henderson-Hasselbalch equation for ionizable side chains and sequence-specific terminal pKa adjustments. Actual charge can differ because local structure, solvent accessibility, nearby residues, ligands, and post-translational modifications shift pKa values.

The estimate scales with length: c-Fos at -18.05 over 380 residues and ubiquitin at -0.40 over 76 are not comparable without dividing by length. A value near zero means the chosen pH is near the protein's isoelectric point, where many proteins are least soluble. The isoelectric point calculator finds that pH directly.

The model treats every Cys as a free thiol with a pKa of 9.0. Hen lysozyme has eight Cys, all in disulfide bonds. At pH 10 the tool reports -4.88 for lysozyme, while the same sequence with its cysteines made non-ionizable gives +2.39, so the sign flips. Charges above about pH 8 are unreliable for disulfide-rich proteins, while at pH 4 the two calculations agree (+14.43).

charged_residue_fraction is a separate composition metric. Oppositely charged residues do not cancel in that fraction, while they do contribute with opposite signs to estimated net charge.

Common misreadings

What this calculator does not predict

Protein stability can refer to several different experimental quantities:

• Folding free energy, $\Delta G_{fold}$
• Mutation-induced stability change, $\Delta\Delta G$
• Melting temperature, $T_m$
• Chemical-denaturation midpoint
• Aggregation, proteolysis, formulation, or storage stability
• Folding and unfolding kinetics

These outputs do not predict any of those quantities, melting temperature included. N-end rule half-lives, which ProtParam reports, depend on the N-terminal residue and on the cell type, and this tool does not calculate them. The outputs are useful for describing and comparing sequences, identifying unusually different compositions, and choosing candidates for more specific analysis.

For mutation-specific $\Delta\Delta G$ prediction from a protein structure, use ThermoMPNN. For a broader physicochemical report, use Protein Parameters. Structural stability over a modeled trajectory requires a method such as OpenMM followed by careful convergence and replicate analysis.

FAQ

Can these indicators rank protein variants by stability?

They can rank variants by each reported descriptor, but there is no supported basis for combining those rankings into a single general stability order. A one-residue mutation may have a strong structural effect that composition-based indicators miss.

Does an instability index below 40 mean the protein is thermostable?

No. The instability-index classification and thermostability are different concepts. Aliphatic index provides a separate composition-level association with thermostability, but it does not predict $T_m$.

Why can two proteins have similar results but different experimental stability?

The indicators do not include three-dimensional packing, hydrogen-bond networks, salt bridges, cofactors, oligomerization, disulfide connectivity, solvent conditions, or the unfolded-state ensemble. Those effects can dominate experimental stability.

Why does estimated net charge change with pH?

Ionizable groups gain or lose protons as pH changes. The calculation models that behavior with fixed pKa values, so it is an estimate rather than a structure-specific charge calculation.

Sources

• Guruprasad K, Reddy BVB, Pandit MW. "Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence." Protein Engineering 4(2), 155-161 (1990). doi:10.1093/protein/4.2.155
• Ikai A. "Thermostability and aliphatic index of globular proteins." Journal of Biochemistry 88(6), 1895-1898 (1980). doi:10.1093/oxfordjournals.jbchem.a133168
• Kyte J, Doolittle RF. "A simple method for displaying the hydropathic character of a protein." Journal of Molecular Biology 157(1), 105-132 (1982). doi:10.1016/0022-2836(82)90515-0
• Lobry JR, Gautier C. "Hydrophobicity, expressivity and aromaticity are the major trends of amino-acid usage in 999 Escherichia coli chromosome-encoded genes." Nucleic Acids Research 22(15), 3174-3180 (1994). doi:10.1093/nar/22.15.3174
• Bjellqvist B, et al. "The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences." Electrophoresis 14(10), 1023-1031 (1993). doi:10.1002/elps.11501401163
• Gasteiger E, et al. "Protein identification and analysis tools on the ExPASy server." In The Proteomics Protocols Handbook, Walker JM (ed.), Humana Press, 571-607 (2005). ProtParam documentation