Calculation of the isoelectric point of tryptic peptides in the pH 3.5-4.5 range based on adjacent amino acid effects

Protein pI and Intracellular Localization

The protein isoelectric point (pI) can be calculated from an amino acid sequence using computational analysis in a good agreement with experimental data. Availability of whole-genome sequences empowers comparative studies of proteome-wide pI distributions. It was found that the whole-proteome distributions of protein pI values are multimodal in different species. It was further hypothesized that the observed multimodality is associated with subcellular localization-specific differences in local pI distributions. Here, we overview the multimodality of proteome-wide pI distributions in different organisms focusing on the relationships between protein pI and subcellular localization. We also discuss the probable factors responsible for variation of the intracellular localization-specific pI profiles.

IPC 2.0: prediction of isoelectric point and pKa dissociation constants

The isoelectric point is the pH at which a particular molecule is electrically neutral due to the equilibrium of positive and negative charges. In proteins and peptides, this depends on the dissociation constant (pK_a) of charged groups of seven amino acids and NH⁺ and COO⁻ groups at polypeptide termini. Information regarding isoelectric point and pK_a is extensively used in two-dimensional gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), crystallisation, and mass spectrometry. Therefore, there is a strong need for the in silico prediction of isoelectric point and pK_a values. In this paper, I present Isoelectric Point Calculator 2.0 (IPC 2.0), a web server for the prediction of isoelectric points and pK_a values using a mixture of deep learning and support vector regression models. The prediction accuracy (RMSD) of IPC 2.0 for proteins and peptides outperforms previous algorithms: 0.848 versus 0.868 and 0.222 versus 0.405, respectively. Moreover, the IPC 2.0 prediction of pK_a using sequence information alone was better than the prediction from structure-based methods (0.576 versus 0.826) and a few folds faster. The IPC 2.0 webserver is freely available at www.ipc2-isoelectric-point.org

Binding of SARS-CoV-2 to Cell Receptors: A Tale of Molecular Evolution

The magnified infectious power of the SARS-CoV-2 virus compared to its precursor SARS-CoV is intimately linked to an enhanced ability in the mutated virus to find available hydrogen-bond sites in the host cells. This characteristic is acquired during virus evolution because of the selective pressure exerted at the molecular level. We pinpoint the specific residue (in the virus) to residue (in the cell) contacts during the initial recognition and binding and show that the virus⋅⋅⋅cell interaction is mainly due to an extensive network of hydrogen bonds and to a large surface of noncovalent interactions. In addition to the formal quantum characterization of bonding interactions, computation of absorption spectra for the specific virus⋅⋅⋅cell interacting residues yields significant shifts of Δλmax =47 and 66 nm in the wavelength for maximum absorption in the complex with respect to the isolated host and virus, respectively.

Accurate Estimation of pKb Values for Amino Groups from Surface Electrostatic Potential (VS,min) Calculations: The Isoelectric Points of Amino Acids as a Case Study

Theoretical calculation of equilibrium dissociation constants is a very computationally demanding and time-consuming process since it requires an extremely accurate computation of the solvation free energy changes for each of the species involved. By correlating the minimum surface electrostatic potential (V_S,min) on the nitrogen atom of several aliphatic amino groups-calculated at the density functional theory (DFT) ωB97X-D/cc-pVDZ level of theory-we obtained regression models for each kind of substitution pattern from which we interpolate their corresponding pK_b values with remarkable accuracy: primary R² = 0.9519; secondary R² = 0.9112; and tertiary R² = 0.8172 (N = 20 for each family). These models were validated with tests sets (N = 5) with mean absolute error (MAE) values of 0.1213 (primary), 0.4407 (secondary), and 0.3057 (tertiary). Combining this ansatz with another previously reported by our group to estimate pK_a values [Caballero-García, G.; et al. Molecules 2019, 24(1), 79] we are able to reproduce the isoelectric points of 13 amino acids with no titrable side chains with MAE = 0.4636 pI units.

Structural Analysis of Biomolecules through a Combination of Mobility Capillary Electrophoresis and Mass Spectrometry

The 3D structures of biomolecules determine their biological function. Established methods in biomolecule structure determination typically require purification, crystallization, or modification of target molecules, which limits their applications for analyzing trace amounts of biomolecules in complex matrices. Here, we developed instruments and methods of mobility capillary electrophoresis (MCE) and its coupling with MS for the 3D structural analysis of biomolecules in the liquid phase. Biomolecules in complex matrices could be separated by MCE and sequentially detected by MS. The effective radius and the aspect ratio of each separated biomolecule were simultaneously determined through the separation by MCE, which were then used as restraints in determining biomolecule conformations through modeling. Feasibility of this method was verified by analyzing a mixture of somatostatin and bradykinin, two peptides with known liquid-phase structures. Proteins could also be structurally analyzed using this method, which was demonstrated for lysozyme. The combination of MCE and MS for complex sample analysis was also demonstrated. MCE and MCE-MS would allow us to analyze trace amounts of biomolecules in complex matrices, which has the potential to be an alternative and powerful biomolecule structure analysis technique.

Amino acids in cell wall of Gram-positive bacterium Micrococcus sp. hsn08 with flocculation activity on Chlorella vulgaris biomass

The aim of this work was to investigate the flocculation mechanism by Gram-positive bacterium, Micrococcus sp. hsn08 as a means for harvesting Chlorella vulgaris biomass. Bacterial cells of Micrococcus sp. hsn08 were added into algal culture to harvest algal cells through direct contacting with algae to form flocs. Viability dependence test confirmed that flocculation activity does not depend on live bacteria, but on part of the peptidoglycan. The further investigation has determined that amino acids in cell wall play an important role to flocculate algal cells. Positively charged calcium can combine bacterial and algal cells together, and form a bridge between them, thereby forming the flocs, suggesting that ions bridging is the main flocculation mechanism. These results suggest that bacterial cells of Micrococcus sp. hsn08 can be applied to harvest microalgae biomass with the help of amino acids in cell wall.

Design of a software for calculating isoelectric point of a polypeptide according to their net charge using the graphical programming language LabVIEW

A software to calculate the net charge and to predict the isoelectric point (pI) of a polypeptide is developed in this work using the graphical programming language LabVIEW. Through this instrument the net charges of the ionizable residues of the polypeptide chains of the proteins are calculated at different pH values, tabulated, pI is predicted and an Excel (-xls) type file is generated. In this work, the experimental values of the pIs (pI) of different proteins are compared with the values of the pIs (pI) calculated graphically, achieving a correlation coefficient (R) of 0.934746 which represents a good reliability for a p < 0.01. In this way the generated program can constitute an instrument applicable in the laboratory, facilitating the calculation to graduate students and junior researchers. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):39-46, 2018.

A comprehensive analysis and annotation of human normal urinary proteome

Biomarkers are measurable changes associated with the disease. Urine can reflect the changes of the body while blood is under control of the homeostatic mechanisms; thus, urine is considered an important source for early and sensitive disease biomarker discovery. A comprehensive profile of the urinary proteome will provide a basic understanding of urinary proteins. In this paper, we present an in-depth analysis of the urinary proteome based on different separation strategies, including direct one dimensional liquid chromatography–tandem mass spectrometry (LC/MS/MS), two dimensional LC/MS/MS, and gel-eluted liquid fraction entrapment electrophoresis/liquid-phase isoelectric focusing followed by two dimensional LC/MS/MS. A total of 6085 proteins were identified in healthy urine, of which 2001 were not reported in previous studies and the concentrations of 2571 proteins were estimated (spanning a magnitude of 10⁶) with an intensity-based absolute quantification algorithm. The urinary proteins were annotated by their tissue distribution. Detailed information can be accessed at the “Human Urine Proteome Database” (www.urimarker.com/urine).

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.

Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction.

Contact: yperez@ebi.ac.uk

Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR.

Supplementary information:Supplementary data are available at Bioinformatics online.

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics

We present a liquid chromatography-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

Pros and cons of peptide isolectric focusing in shotgun proteomics

In shotgun proteomics, protein mixtures are proteolytically digested before tandem mass spectrometry (MS/MS) analysis. Biological samples are generally characterized by a very high complexity, therefore a step of peptides fractionation before the MS analysis is essential. This passage reduces the sample complexity and increases its compatibility with the sampling performance of the instrument. Among all the existing approaches for peptide fractionation, isoelectric focusing has several peculiarities that are theoretically known but practically rarely exploited by the proteomics community. The main aim of this review is to draw the readers' attention to these unique qualities, which are not accessible with other common approaches, and that represent important tools to increase confidence in the identification of proteins and some post-translational modifications. The general characteristics of different methods to perform peptide isoelectric focusing with natural and artificial pH gradients, the existing instrumentation, and the informatics tools available for isoelectric point calculation are also critically described. Finally, we give some general conclusions on this strategy, underlying its principal limitations.

Isoelectric point optimization using peptide descriptors and support vector machines

IPG (Immobilized pH Gradient) based separations are frequently used as the first step in shotgun proteomics methods; it yields an increase in both the dynamic range and resolution of peptide separation prior to the LC-MS analysis. Experimental isoelectric point (pI) values can improve peptide identifications in conjunction with MS/MS information. Thus, accurate estimation of the pI value based on the amino acid sequence becomes critical to perform these kinds of experiments. Nowadays, pI is commonly predicted using the charge-state model [1], and/or the cofactor algorithm [2]. However, none of these methods is capable of calculating the pI value for basic peptides accurately. In this manuscript, we present an new approach that can significant improve the pI estimation, by using Support Vector Machines (SVM) [3], an experimental amino acid descriptor taken from the AAIndex database [4] and the isoelectric point predicted by the charge-state model. Our results have shown a strong correlation (R(2)=0.98) between the predicted and observed values, with a standard deviation of 0.32 pH units across the complete pH range.

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.

One-step sample concentration, purification, and albumin depletion method for urinary proteomics

Workflows in urinary proteomics studies are often complex and require many steps to enrich, purify, deplete, and separate the complex mixture. Many of these methods are laborious, are time-consuming, and have the potential for error. Although individual steps of these methods have been previously studied, their downstream compatibilities with fractionation technologies such as off-gel electrophoresis have not been investigated. We developed a one-step sample preparation workflow that simultaneously (i) concentrates proteins, (ii) purifies by removing salts and other low molecular weight compounds, and (iii) depletes (albumin) from urine samples. This simple and robust workflow can be multiplexed and is compatible with a diverse range of downstream multidimensional separation technologies. Additionally, because of its high reproducibility and flexibility in processing samples with different volumes and concentrations, it has the potential to be used for standardization of urinary proteomics studies, as well as for studying other body fluids of similar complexity.

Power and limitations of electrophoretic separations in proteomics strategies

Proteomics can be defined as the large-scale analysis of proteins. Due to the complexity of biological systems, it is required to concatenate various separation techniques prior to mass spectrometry. These techniques, dealing with proteins or peptides, can rely on chromatography or electrophoresis. In this review, the electrophoretic techniques are under scrutiny. Their principles are recalled, and their applications for peptide and protein separations are presented and critically discussed. In addition, the features that are specific to gel electrophoresis and that interplay with mass spectrometry (i.e., protein detection after electrophoresis, and the process leading from a gel piece to a solution of peptides) are also discussed.