Protein Science

The molar absorption coefficient, ε, of a protein is usually based on concentrations measured by dry weight, nitrogen, or amino acid analysis. The studies reported here suggest that the Edelhoch method is the best method for measuring ε for a protein. (This method is described by Gill and von Hippel [1989, Anal Biochem 182:319–326] and is based on data from Edelhoch [1967, Biochemistry 6:1948–1954].) The absorbance of a protein at 280 nm depends on the content of Trp, Tyr, and cystine (disulfide bonds). The average ε values for these chromophores in a sample of 18 well‐characterized proteins have been estimated, and the ε values in water, propanol, 6 M guanidine hydrochloride (GdnHCl), and 8 M urea have been measured. For Trp, the average ε values for the proteins are less than the ε values measured in any of the solvents. For Tyr, the average ε values for the proteins are intermediate between those measured in 6 M GdnHCl and those measured in propanol. Based on a sample of 116 measured ε values for 80 proteins, the ε at 280 nm of a folded protein in water, ε(280), can best be predicted with this equation

ϵ(280) (M⁻¹ cm⁻¹) = (#Trp)(5,500) + (#Tyr)(1,490) + (#cystine)(125).

These ε(280) values are quite reliable for proteins containing Trp residues, and less reliable for proteins that do not. However, the Edelhoch method is convenient and accurate, and the best approach is to measure rather than predict ε.

A novel method for differentiating between correctly and incorrectly determined regions of protein structures based on characteristic atomic interactions is described. Different types of atoms are distributed nonrandomly with respect to each other in proteins. Errors in model building lead to more randomized distributions of the different atom types, which can be distinguished from correct distributions by statistical methods.

Atoms are classified in one of three categories: carbon (C), nitrogen (N), and oxygen (O). This leads to six different combinations of pairwise noncovalently bonded interactions (CC, CN, CO, NN, NO, and OO). A quadratic error function is used to characterize the set of pairwise interactions from nine‐residue sliding windows in a database of 96 reliable protein structures. Regions of candidate protein structures that are mistraced or misregistered can then be identified by analysis of the pattern of nonbonded interactions from each window.

Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance‐dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non‐native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo‐electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER‐8.

Thioflavine T (ThT) associates rapidly with aggregated fibrils of the synthetic β/A4‐derived peptides β(1–28) and β(1–40), giving rise to a new excitation (ex) (absorption) maximum at 450 nm and enhanced emission (em) at 482 nm, as opposed to the 385 nm (ex) and 445 nm (em) of the free dye. This change is dependent on the aggregated state as monomeric or dimeric peptides do not react, and guanidine dissociation of aggregates destroys the signal. There was no effect of high salt concentrations. Binding to the β(1–40) is of lower affinity, K_d 2 μM, while it saturates with a K_d of 0.54 μM for β(1–28). Insulin fibrils converted to a β‐sheet conformation fluoresce intensely with ThT. A variety of polyhydroxy, polyanionic, or polycationic materials fail to interact or impede interaction with the amyloid peptides. This fluorometric technique should allow the kinetic elucidation of the amyloid fibril assembly process as well as the testing of agents that might modulate their assembly or disassembly.

Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM‐22 force field, statistical preferences for the main‐chain and side‐chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4–, 8–, and 12–residue loop predictions, respectively, had <2 Å RMSD error for the mainchain N, Ca, C, and O atoms; the average accuracies were 0.59 6 0.05, 1.16 6 0.10, and 2.61 6 0.16 Å, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main‐chain stem atoms is 2.5 Å, the average loop prediction error increased by 180, 25, and 3% for 4–, 8–, and 12–residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.

The experimental material accumulated in the literature on the conformational behavior of intrinsically unstructured (natively unfolded) proteins was analyzed. Results of this analysis showed that these proteins do not possess uniform structural properties, as expected for members of a single thermodynamic entity. Rather, these proteins may be divided into two structurally different groups: intrinsic coils, and premolten globules. Proteins from the first group have hydrodynamic dimensions typical of random coils in poor solvent and do not possess any (or almost any) ordered secondary structure. Proteins from the second group are essentially more compact, exhibiting some amount of residual secondary structure, although they are still less dense than native or molten globule proteins. An important feature of the intrinsically unstructured proteins is that they undergo disorder–order transition during or prior to their biological function. In this respect, the Protein Quartet model, with function arising from four specific conformations (ordered forms, molten globules, premolten globules, and random coils) and transitions between any two of the states, is discussed.

Denaturant m values, the dependence of the free energy of unfolding on denaturant concentration, have been collected for a large set of proteins. The m value correlates very strongly with the amount of protein surface exposed to solvent upon unfolding, with linear correlation coefficients of R = 0.84 for urea and R = 0.87 for guanidine hydrochloride. These correlations improve to R = 0.90 when the effect of disulfide bonds on the accessible area of the unfolded protein is included. A similar dependence on accessible surface area has been found previously for the heat capacity change (ΔC_p), which is confirmed here for our set of proteins. Denaturant m values and heat capacity changes also correlate well with each other. For proteins that undergo a simple two‐state unfolding mechanism, the amount of surface exposed to solvent upon unfolding is a main structural determinant for both m values and ΔC_p.

PDBsum is a web server providing structural information on the entries in the Protein Data Bank (PDB). The analyses are primarily image‐based and include protein secondary structure, protein‐ligand and protein‐DNA interactions, PROCHECK analyses of structural quality, and many others. The 3D structures can be viewed interactively in RasMol, PyMOL, and a JavaScript viewer called 3Dmol.js. Users can upload their own PDB files and obtain a set of password‐protected PDBsum analyses for each. The server is freely accessible to all at: http://www.ebi.ac.uk/pdbsum.

A new method based on protein fragmentation and directly coupled microbore high‐performance liquid chromatography–fast atom bombardment mass spectrometry (HPLC‐FABMS) is described for determining the rates at which peptide amide hydrogens in proteins undergo isotopic exchange. Horse heart cytochrome c was incubated in D₂O as a function of time and temperature to effect isotopic exchange, transferred into slow exchange conditions (pH 2–3, 0 °C), and fragmented with pepsin. The number of peptide amide deuterons present in the proteolytic peptides was deduced from their molecular weights, which were determined following analysis of the digest by HPLC‐FABMS. The present results demonstrate that the exchange rates of amide hydrogens in cytochrome c range from very rapid (k > 140 h⁻¹) to very slow (k < 0.002 h⁻¹). The deuterium content of specific segments of the protein was determined as a function of incubation temperature and used to indicate participation of these segments in conformational changes associated with heating of cytochrome c. For the present HPLC‐FABMS system, approximately 5 nmol of protein were used for each determination. Results of this investigation indicate that the combination of protein fragmentation and HPLC‐FABMS is relatively free of constraints associated with other analytical methods used for this purpose and may be a general method for determining hydrogen exchange rates in specific segments of proteins.

Although it is usually possible to achieve a favorable yield of a recombinant protein in Escherichia coli, obtaining the protein in a soluble, biologically active form continues to be a major challenge. Sometimes this problem can be overcome by fusing an aggregation‐prone polypeptide to a highly soluble partner. To study this phenomenon in greater detail, we compared the ability of three soluble fusion partners—maltose‐binding protein (MBP), glutathione S‐transferase (GST), and thioredoxin (TRX)—to inhibit the aggregation of six diverse proteins that normally accumulate in an insoluble form. Remarkably, we found that MBP is a far more effective solubilizing agent than the other two fusion partners. Moreover, we demonstrated that in some cases fusion to MBP can promote the proper folding of the attached protein into its biologically active conformation. Thus, MBP seems to be capable of functioning as a general molecular chaperone in the context of a fusion protein. A model is proposed to explain how MBP promotes the solubility and influences the folding of its fusion partners.