Assignment of protein disulphides by a computer method using mass spectrometric data

Comprehensive identification of protein disulfide bonds with pepsin/trypsin digestion, Orbitrap HCD and Spectrum Identification Machine

Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools. Here we present a new protocol tailored toward mapping SS; it uses readily available reagents, instruments, and software. For sample preparation, a 4-h pepsin digestion at pH 1.3 followed by an overnight trypsin digestion at pH 6.5 can maximize the release of SS-containing peptides from non-reduced proteins, while minimizing SS scrambling. For LC/MS/MS analysis, SS-containing peptides can be efficiently fragmented with HCD in a Q Exactive Orbitrap mass spectrometer, preserving SS for subsequent identification. Our bioinformatics protocol describes how we tailored our freely downloadable and easy-to-use software, Spectrum Identification Machine for Cross-Linked Peptides (SIM-XL), to minimize false identification and facilitate manual validation of SS-peptide mass spectra. To substantiate this optimized method, we've comprehensively identified 14 out of 17 known SS in BSA. SIGNIFICANCE: Comprehensive and accurate identification of SS in proteins is critical for elucidating protein structures and functions. Yet, it is far from routine to accomplish this task in many analytical or core laboratories. Numerous published methods require complex sample preparation methods, specialized mass spectrometers and cumbersome or proprietary software tools, thus cannot be easily implemented in unspecialized laboratories. Here, we describe a robust and rapid SS mapping approach that utilizes readily available reagents, instruments, and software; it can be easily implemented in any analytical core laboratories, and tested for its impact on the research community.

Identification and characterization of disulfide bonds in proteins and peptides from tandem MS data by use of the MassMatrix MS/MS search engine

A new database search algorithm has been developed to identify disulfide-linked peptides in tandem MS data sets. The algorithm is included in the newly developed tandem MS database search program, MassMatrix. The algorithm exploits the probabilistic scoring model in MassMatrix to achieve identification of disulfide bonds in proteins and peptides. Proteins and peptides with disulfide bonds can be identified with high confidence without chemical reduction or other derivatization. The approach was tested on peptide and protein standards with known disulfide bonds. All disulfide bonds in the standard set were identified by MassMatrix. The algorithm was further tested on bovine pancreatic ribonuclease A (RNaseA). The 4 native disulfide bonds in RNaseA were detected by MassMatrix with multiple validated peptide matches for each disulfide bond with high statistical scores. Fifteen nonnative disulfide bonds were also observed in the protein digest under basic conditions (pH = 8.0) due to disulfide bond interchange. After minimizing the disulfide bond interchange (pH = 6.0) during digestion, only one nonnative disulfide bond was observed. The MassMatrix algorithm offers an additional approach for the discovery of disulfide bond from tandem mass spectrometry data.

CysMap and CysJoin: Database and tools for protein disulphides localisation

We have developed a computer program able to make user-customised databases derived from the public PIR non-redundant reference protein database. When the database of interest has been created, the user will generate the map of all the possible linear peptides containing one and two cysteines for each protein and combine them to calculate the mass of all the possible clusters of linear peptides linked by a disulphide bridge with a cysteine pair. It is also possible to create selected maps corresponding to peptides formed by the action of specific proteases. In this way, mass spectrometric data obtained from the hydrolysis of proteins of unknown sequence can be related to that contained in the database for quick disulphide assignment and protein identification. To confirm signal attribution, the program will also furnish the expected mass of cluster peptides after performing a cycle of Edman degradation. The utility of the program is discussed and examples of application are given.

Analytical techniques and computer algorithms combined for the rapid characterization of structural peptide and protein features

The most recent algorithms based on the use of modern analytical techniques for the assessment of structural peptide and protein features have been reviewed. No algorithm devoted to the realization of predictive models or statistical analysis has been discussed, but only methods furnishing information on the real structure of the molecules. In particular, the procedures designed for handling sequence and mass spectrometric data obtained from the analysis of unfractionated digestion mixtures allow the user to get rapid information on the structure of the target polypeptide. Two classes of methods are illustrated: the first regards the determination of the amino acid sequence, whereas the second used its knowledge to supply data on the localization, function and three-dimensional structure of disulphides.

A computer program to compare sequence fingerprints of homologous proteins for the rapid assessment of their primary structure differences

We have developed a computer program for the rapid assessment of the primary structure differences between a protein of unknown sequence and a homologous known protein. Both proteins are reduced, alkylated, and digested with the same hydrolytic agent. The unfractionated peptide mixtures are submitted to automatic sequence analysis. Based on the knowledge of the reference sequence, the program utilizes the analysis data to identify all the potential peptides present in the two mixtures, determining their primary structure, homology degree, and molecular weight calculated both as integer MH+ and average mass variables. These fingerprints allow the user to easily identify the structural differences between the two proteins and clarify possible doubts by a mass spectrometric analysis of the two mixtures. In order to verify the utility of the program, we provide an application example using the already reported data of two homologous proteins.

The disulphide bond pattern of bitistatin, a disintegrin isolated from the venom of the viper Bitis arietans

The disulphide bond pattern of the long disintegrin bitistatin (83 amino acids, 14 cysteines) was established using structural information gathered by amino acid analysis, N-terminal sequencing, and molecular mass determination of fragments isolated by reversed-phase HPLC after polypeptide degradation with trypsin and oxalic acid. A computer program was used to calculate all possible combinations of disulphide-bonded peptides matching the mass spectrometric data, and the output was filtered using compositional and sequence data. Disulphide bonds between cysteines 16-34, 18-29, 28-51, 42-48, 47-72, and 60-79 are conserved in medium-long disintegrins flavoridin and kistrin (70 amino acids, 12 cysteines), and the two cysteine residues at positions 5 and 24 found in bitistatin but not in other disintegrin molecules are disulphide-bridged. This linkage creates an extra, large loop, which, depending on whether the NMR structure of flavoridin or kistrin is used for modelling the structure of bitistatin, lies opposite or nearly parallel, respectively, to the biologically active RGD-containing loop.