Physicochemical Properties Of Peptides Research Articles

Peptide Optimization at the Drug Discovery-Development Interface: Tailoring of Physicochemical Properties Toward Specific Formulation Requirements

Physicochemical properties of peptides need to be compatible with the manufacturing process and formulation requirements to ensure developability toward the commercial drug product. This aspect is often disregarded and only evaluated late in discovery, imposing a high risk for delays in development, increased costs, and finally for the project in general. Here, we report a case study of early physicochemical peptide characterization and optimization of dual glucagon-like peptide 1/glucagon receptor agonists toward specific formulation requirements. Aggregation issues which were observed at acidic pH in the presence of phenolic preservatives could be eliminated by modification of the peptide sequence, and chemical stability issues were significantly improved by addition of stabilizing formulation excipients. We describe structural, analytical, and biophysical characterization in different compositions to analyze the effect of pH and formulation excipients on physical and chemical stability. Molecular models have been generated to rationalize peptide stability behavior based on computed physicochemical descriptors and interactions with excipients. To conclude these studies, a general roadmap is proposed how to assess and optimize early physicochemical peptide properties in a sophisticated way by combining experimental and in silico profiling to provide stable peptide drugs under relevant formulation conditions at the end of discovery.

Mining and Functional Verification of Calcitonin Genes from the Genome Database.

With more countries in the world entering elderly society, osteoporosis is a common disease among the elderly, especially middle-aged and elderly women. Although calcitonin is an effective drug used to treat osteoporosis in clinical practice, it also exists such problems as high cost, short half-life, and high immunogenicity. Therefore, to explore more efficient calcitonin has important clinical significance. Given the emergence of new-generation gene sequencing, numerous genome sequences of marine species have been revealed. This study aimed to identify new, highly active Calcitonins (CTs) from the gene database. Candidate CT sequences were obtained by BLAST and analyzed. The evolutionary tree of these sequences was constructed using the Neighbor-Joining method of MEGA 7 software. Secondary structures were analyzed by Circular Dichroism (CD). The biological activities of CTs were estimated using the standard of the rat hypocalcemic activity assay in vivo. The half-life and immunogenicity of CT sequences were determined by ELISA. The physicochemical properties of peptides were analyzed with ProtParam and HeliQuest. A total of 64 candidate CT gene and amino acid sequences from different species were obtained by BLAST using the salmon CT (sCT) sequence as the query sequence. These sequences were clustered to 27 different CT polypeptide sequences, and then the evolutionary tree was constructed. A total of 13 sequences were selected for chemical synthesis and activity assay. Results showed that although their secondary structures were similar, four types of candidate CTs had 30% higher activities than sCT, three other types had similar activities to sCT, and the remaining four types had much lower activities than sCT. Among the three designed CTs, the activities of CT-01 and CT-02 were at least 50% higher than those of sCT. Furthermore, all three CT sequences had a similar half-life to sCT and lower immunogenicity. CTs from Monodelphis domestica, Gallus gallus, Ornithorhynchus anatinus, and Carassius auratus had high activities. The exploration and mining of the marine-life genome database can be extremely valuable considering broad application prospect.

Exploring the peptide retention mechanism in molecularly imprinted polymers.

Molecularly imprinted polymers (MIPs) have been used as useful sorbents in solid-phase extraction for a wide range of molecules and sample matrices. Their unique selectivity can be fine-tuned in the imprinting process and is crucial for the extraction of macromolecules from complex matrices such as serum. A relevant example of this is the application of MIPs to peptides in diagnostic assays. In this article the selectivity of MIPs, previously implemented in a quantitative mass-spectrometric assay for the biomarker pro-gastrin-releasing peptide, is investigated. Partial least squares regression was used to generate models for the evaluation and prediction of the retention mechanism of MIPs. A hypothesis on interactions of MIPs with the target peptide was verified by ad hoc experiments considering the relevant peptide physicochemical properties highlighted from the multivariate analysis. Novel insights into and knowledge of the driving forces responsible for the MIP selectivity have been obtained and can be directly used for further optimization of MIP imprinting strategies. Graphical Abstract Applied analytical strategy: the Solid Phase Extraction (SPE) of digested Bovin Serum Albumin (BSA), using Molecularly Imprinted Polymers (MIP), is followed by the liquid chromatography-mass spectrometry (LC-MS) analysis for the identification of the retained peptides. The further application of multivariate analysis allows setting up a Partial Least Square (PLS) model, which describes the peptide retention into the MIP and gives additional knowledge to be used in the optimization of the MIP and the whole SPE method.

Synergystic Interaction of Common Bean (Phaseolus vulgaris L.) Peptides and Oxaliplatin on Proliferation of HCT116 Colorectal Cancer Cells through Mitochondrial Depolarization and DNA Damage

The objectives of this study were to evaluate the antiproliferative effect of common bean (Phaseolus vulgaris L.) peptides on HCT116 human colorectal cancer cells and their interaction with oxaliplatin; furthermore, to determine their potential mechanisms of action. Pure peptides GLTSK, LSGNK, GEGSGA, MTEEY and MPACGS and the peptides in the non‐digestible fraction from common bean cultivars Azufrado Higuera (AH) and Bayo Madero (BM) were tested for cytotoxic effects on HCT116, and also on CCD‐33Co human normal colon cells. None of the peptides were toxic to normal cells at concentrations up to 1 mM. GLTSK (IC50=134.6 μM). On the contrary, according to MTS assay, GEGSGA (IC50=156.7 μM) exerted antiproliferative effects on HCT116 cells.. Based on the combination indexes (γ) of the isobolographic analysis, GLTSK (γ=0.64) and GEGSGA (γ=0.78) interacted synergistically (p < 0.05) with oxaliplatin inhibiting HCT116 cells. Using flow cytometry, it was determined that GLTSK caused loss of mitochondrial membrane potential (Δψm) (16% of cells), increased (p < 0.05) in intracellular reactive oxygen species (ROS) (12.1‐fold compared with untreated cells), cell cycle arrest in G2 (54% of cells) and induced apoptosis (33% of cells). GEGSGA had a limited effect on Δψm and intracellular ROS, but caused arrest in G1 phase (64% of cell population) and promoted increase (p < 0.05) in p53 (290%) and cleavage of PARP (184%) compared to untreated cells, promoting DNA damage. The different mechanisms of action were further established by confocal microscopy. An increase (p < 0.05) in the expression and activation of nuclear p53 (145% of fluorescence intensity/μm2) was observed in response to GEGSGA treatment compared to untreated cells in a time‐frame of 3 to 8 h. Moreover, GLTSK depolarized the cells (38% decrease of fluorescence intensity/μm2) and increased (p < 0.05) intracellular ROS (180% of fluorescence intensity/μm2). In addition, peptide fractions from bean cultivar Azufrado Higuera interacted synergistically with oxaliplatin inducing apoptosis (49% of cells), loss of Δψm (21.7% of cells) and increase of intracellular ROS (13.1‐fold compared to untreated cells). This is the first report demonstrating that dietary peptides from common bean interact synergistically with oxaliplatin inhibiting HCT116 human cancer cells. These results support the hypothesis that physicochemical properties of peptides would determine their mechanism of action.Support or Funding InformationBasic Science, National Council for Science and Technology, Mexico CONACyT‐grant number 220924

NeuroPep: a comprehensive resource of neuropeptides

Neuropeptides play a variety of roles in many physiological processes and serve as potential therapeutic targets for the treatment of some nervous-system disorders. In recent years, there has been a tremendous increase in the number of identified neuropeptides. Therefore, we have developed NeuroPep, a comprehensive resource of neuropeptides, which holds 5949 non-redundant neuropeptide entries originating from 493 organisms belonging to 65 neuropeptide families. In NeuroPep, the number of neuropeptides in invertebrates and vertebrates is 3455 and 2406, respectively. It is currently the most complete neuropeptide database. We extracted entries deposited in UniProt, the database (www.neuropeptides.nl) and NeuroPedia, and used text mining methods to retrieve entries from the MEDLINE abstracts and full text articles. All the entries in NeuroPep have been manually checked. 2069 of the 5949 (35%) neuropeptide sequences were collected from the scientific literature. Moreover, NeuroPep contains detailed annotations for each entry, including source organisms, tissue specificity, families, names, post-translational modifications, 3D structures (if available) and literature references. Information derived from these peptide sequences such as amino acid compositions, isoelectric points, molecular weight and other physicochemical properties of peptides are also provided. A quick search feature allows users to search the database with keywords such as sequence, name, family, etc., and an advanced search page helps users to combine queries with logical operators like AND/OR. In addition, user-friendly web tools like browsing, sequence alignment and mapping are also integrated into the NeuroPep database.Database URL: http://isyslab.info/NeuroPep

Proteome Analysis of Mouse Adipose Tissue and Colon Tissue using a Novel Integrated Data Processing Pipeline

Abstract: Liquid chromatography based mass spectrometry (LC-MS) is a key technology for analyzing highly complex anddynamic proteome samples. With highly accurate and sensitive LC-MS analysis of complex proteome samples, efficient dataprocessing is another critical issue to obtain more information from LC-MS data. A typical proteomic data processing starts wit hprotein database search engine which assigns peptide sequences to MS/MS spectra and finds proteins. Although several searchengines, such as SEQUEST and MASCOT, have been widely used, there is no unique standard way to interpret MS/MS spectraof peptides. Each search engine has pros and cons depending on types of mass spectrometers and physicochemical properties ofpeptides. In this study, we describe a novel data process pipeline which identifies more peptides and proteins by correcting pre-cursor ion mass numbers and unifying multi search engines results. The pipeline utilizes two open-source software, i PE-MMRfor mass number correction, and iProphet to combine several search results. The integrated pipeline identified 25% more pro-teins in mouse epididymal adipose tissue compared with the conventional method. Also the pipeline was validated using controland colitis induced colon tissue. The results of the present study shows that the integrated pipeline can efficiently identifyincreased number of proteins compared to the conventional method which can be a breakthrough in identification of a potentialbiomarker candidate.Key words: iPE-MMR, iProphet, Q-TOF, TPP

Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.

Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications. We present a novel probabilistic scoring algorithm called Context-Sensitive Peptide Identification (CSPI) based on highly flexible Input-Output Hidden Markov Models (IO-HMM) that capture the influence of peptide physicochemical properties on their observed MS/MS spectra. We use several local and global properties of peptides and their fragment ions from literature. Comparison with two popular algorithms, Crux (re-implementation of SEQUEST) and X!Tandem, on multiple datasets of varying complexity, shows that peptide identification scores from our models are able to achieve greater discrimination between true and false peptides, identifying up to ∼25% more peptides at a False Discovery Rate (FDR) of 1%. We evaluated two alternative normalization schemes for fragment ion-intensities, a global rank-based and a local window-based. Our results indicate the importance of appropriate normalization methods for learning superior models. Further, combining our scores with Crux using a state-of-the-art procedure, Percolator, we demonstrate the utility of using scoring features from intensity-based models, identifying ∼4-8 % additional identifications over Percolator at 1% FDR. IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data.

Peptide sequences mediating tropism to intact blood–brain barrier: An in vivo biodistribution study using phage display

Peptide motifs that demonstrate tropism for the blood brain barrier (BBB) are of real translational value in developing innovative delivery strategies for biological brain targeted therapies. In vivo peptide-phage display affords peptide selection against the full complement of biological markers within the correct cellular macro- and micro-environments. Here a stringent in vivo biopanning protocol was employed in the rat aimed at identifying cyclic 7-mer peptide motifs that mediate tropism to brain microvasculature. Five rounds of biopanning identified 349 unique peptide motifs in the brain tissue gray matter compartment (microvasculature and parenchyma). While in general no consensus was evident linking peptide physico-chemical properties and brain tropism, peptides bearing c-SxTSSTx-c or c-xxxSSTx-c motifs were found to be present in high abundance. Based on amino acid frequency distribution of the 349 unique peptides sequences a theoretical ‘idealized’ peptide pattern, c-PP(S/P)SSST-c, could be derived. For the most abundant experimental peptide sequence found in brain tissue, c-SYTSSTM-c, an in vivo pharmacokinetic and whole body tissue biodistribution study was performed. Based upon tissue exposure data (i.e. tissue AUC(0–infinity)) the sequence c-SYTSSTM-c efficiently retargeted phage virions to the brain providing an approximate 5-fold greater (P<0.05) accumulation in brain over control phage; in all other organs no significant (P>0.05) difference in tissue tropism between c-SYTSSTM-c and control phages were evident. This peptide and more generally the peptide motifs, -SxTSSTx- or -xxxSSTx-, warrant further investigation as agents mediating sequence-dependent tropism to brain microvasculature potentially able to deliver biologic cargo to the CNS.

Physicochemical characterization of GBV-C E1 peptides as potential inhibitors of HIV-1 fusion peptide: Interaction with model membranes

Four peptide sequences corresponding to the E1 protein of GBV-C: NCCAPEDIGFCLEGGCLV (P7), APEDIGFCLEGGCLVALG (P8), FCLEGGCLVALGCTICTD (P10) and QAGLAVRPGKSAAQLVGE (P18) were studied as they were capable of interfering with the HIV-1 fusion peptide (HIV-1 FP). In this work, the surface properties of the E1 peptide sequences are investigated and their physicochemical characterization is done by studying their interaction with model membranes; moreover, their mixtures with HIV-1 FP were also studied in order to observe whether they are capable to modify the HIV-1 FP interaction with model membranes as liposomes or monolayers. Physicochemical properties of peptides (pI and net charge) were predicted showing similarities between P7 and P8, and P10 and HIV-1 FP, whereas P18 appears to be very different from the rest. Circular dichroism experiments were carried out showing an increase of the percentage of α-helix of P7 and P8 when mixed with HIV-1 FP corroborating a conformational change that could be the cause of their inhibition ability. Penetration experiments show that all the peptides can spontaneously insert into phospholipid membranes. Analysis of compression isotherms indicates that the peptides interact with phospholipids and the E1 peptides modify the compression isotherms of HIV-1 FP, but there is one of the peptides that excelled as the best candidate for inhibiting the activity of HIV-1 FP, P7, and therefore, that could be potentially used in future anti-HIV-1 research.

TumorHoPe: A Database of Tumor Homing Peptides

BackgroundCancer is responsible for millions of immature deaths every year and is an economical burden on developing countries. One of the major challenges in the present era is to design drugs that can specifically target tumor cells not normal cells. In this context, tumor homing peptides have drawn much attention. These peptides are playing a vital role in delivering drugs in tumor tissues with high specificity. In order to provide service to scientific community, we have developed a database of tumor homing peptides called TumorHoPe.DescriptionTumorHoPe is a manually curated database of experimentally validated tumor homing peptides that specifically recognize tumor cells and tumor associated microenvironment, i.e., angiogenesis. These peptides were collected and compiled from published papers, patents and databases. Current release of TumorHoPe contains 744 peptides. Each entry provides comprehensive information of a peptide that includes its sequence, target tumor, target cell, techniques of identification, peptide receptor, etc. In addition, we have derived various types of information from these peptide sequences that include secondary/tertiary structure, amino acid composition, and physicochemical properties of peptides. Peptides in this database have been found to target different types of tumors that include breast, lung, prostate, melanoma, colon, etc. These peptides have some common motifs including RGD (Arg-Gly-Asp) and NGR (Asn-Gly-Arg) motifs, which specifically recognize tumor angiogenic markers. TumorHoPe has been integrated with many web-based tools like simple/complex search, database browsing and peptide mapping. These tools allow a user to search tumor homing peptides based on their amino acid composition, charge, polarity, hydrophobicity, etc.ConclusionTumorHoPe is a unique database of its kind, which provides comprehensive information about experimentally validated tumor homing peptides and their target cells. This database will be very useful in designing peptide-based drugs and drug-delivery system. It is freely available at http://crdd.osdd.net/raghava/tumorhope/.

CPPsite: a curated database of cell penetrating peptides

Delivering drug molecules into the cell is one of the major challenges in the process of drug development. In past, cell penetrating peptides have been successfully used for delivering a wide variety of therapeutic molecules into various types of cells for the treatment of multiple diseases. These peptides have unique ability to gain access to the interior of almost any type of cell. Due to the huge therapeutic applications of CPPs, we have built a comprehensive database ‘CPPsite’, of cell penetrating peptides, where information is compiled from the literature and patents. CPPsite is a manually curated database of experimentally validated 843 CPPs. Each entry provides information of a peptide that includes ID, PubMed ID, peptide name, peptide sequence, chirality, origin, nature of peptide, sub-cellular localization, uptake efficiency, uptake mechanism, hydrophobicity, amino acid frequency and composition, etc. A wide range of user-friendly tools have been incorporated in this database like searching, browsing, analyzing, mapping tools. In addition, we have derived various types of information from these peptide sequences that include secondary/tertiary structure, amino acid composition and physicochemical properties of peptides. This database will be very useful for developing models for predicting effective cell penetrating peptides.Database URL: http://crdd.osdd.net/raghava/cppsite/.

Zwitterionic Hydrophilic Interaction Liquid Chromatography (ZIC-HILIC and ZIC-cHILIC) Provide High Resolution Separation and Increase Sensitivity in Proteome Analysis

The complexity of peptide mixtures that are analyzed in proteomics necessitates fractionation by multidimensional separation approaches prior to mass spectrometric analysis. In this work, we introduce and evaluate hydrophilic interaction liquid chromatography (HILIC) based strategies for the separation of complex peptide mixtures. The two zwitterionic HILIC materials (ZIC-HILIC and ZIC-cHILIC) chosen for this work differ in the spatial orientation of the positive and negative charged groups. Online experiments revealed a pH-independent resolving power for the ZIC-cHILIC resin while ZIC-HILIC showed a decrease in resolving power at an acidic pH. Subsequently, we extensively evaluated the performances of ZIC-HILIC and ZIC-cHILIC as first dimension in an off-line two-dimensional liquid chromatography (2D-LC) strategy in combination with reversed phase (RP), with respect to peptide separation efficiency and how the retention time correlates with a number of peptide physicochemical properties. Both resins allowed the identification of more than 20,000 unique peptides corresponding to over 3500 proteins in each experimental condition from a remarkably low (1.5 μg) amount of starting material of HeLa lysate digestion. The resulting data allows the drawing of a comprehensive picture regarding ZIC- and ZIC-cHILIC peptide separation characteristics. Furthermore, the extent of protein identifications observed from such a level of material demonstrates that HILIC can rival or surpass traditional multidimensional strategies employed in proteomics.

Relationship between Sample Loading Amount and Peptide Identification and Its Effects on Quantitative Proteomics

The relationship between sample loading amount and peptide identification is crucial for the optimization of proteomics experiments, but few studies have addressed this matter. Herein, we present a systematic study using a replicate run strategy to probe the inherent influence of both peptide physicochemical properties and matrix effects on the relationship between peptide identification and sample loading amounts, as well as its applications in protein quantification. Ten replicate runs for a series of laddered loading amounts (ranging between 0.01 approximately 10 microg) of total digested proteins from Saccharomyces cerevisiae were performed with nanoscale liquid chromatography coupled with linear ion trap/Fourier transform ion cyclotron resonance (nanoLC-LTQ-FT) to obtain a nearly saturated peptide identification. This permitted us to differentiate the linear correlativity of peptide identification by the commonly used peptide quantitative index, the area of constructed ion chromatograms (XIC) (SA, from MS and tandem MS data) in the given experiments. The absolute loading amount of a given complex sample affected the final qualitative identification result; thus, optimization of the sample loading amount before every proteomics study was essential. Peptide physicochemical properties had little effect on the linear correlativity between SA-based peptide quantification and loading amount. The matrix effects, rather than the static physicochemical properties of individual peptides, affect peptide measurability. We also quantified the target protein by selecting peptides with good parallel linear correlativity based upon SA as signature peptides and revised the data by multiplying by the reciprocal of the slope coefficient. We found that this optimized the linear protein abundance relativity at every amount range and thus extended the linear dynamic range of label-free quantification. This empirical rule for linear peptide selection (ERLPS) can be adopted to correct comparison results in proteolytic peptide-based quantitative proteomics, such as accurate mass tag (AMT) and targeted quantitative proteomics, as well as in tag-labeled comparative proteomics.

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

BackgroundMass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called Oi value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (Oi). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances.ResultsThe APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis.ConclusionThe APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.

Correlation between simulated physicochemical properties and hemolycity of protegrin-like antimicrobial peptides: Predicting experimental toxicity

The therapeutic, antibiotic potential of antimicrobial peptides can be prohibitively diminished because of the cytotoxicity and hemolytic profiles they exhibit. Quantifying and predicting antimicrobial peptide toxicity against host cells is thus an important goal of AMP related research. In this work, we present quantitative structure activity relationships for toxicity of protegrin-like antimicrobial peptides against human cells (epithelial and red blood cells) based on physicochemical properties, such as interaction energies and radius of gyration, calculated from molecular dynamics simulations of the peptides in aqueous solvent. The hypothesis is that physicochemical properties of peptides, as manifest by their structure and interactions in a solvent and as captured by atomistic simulations, are responsible for their toxicity against human cells. Protegrins are beta-hairpin peptides with high activity against a wide variety of microbial species, but in their native state are toxic to human cells. Sixty peptides with experimentally determined toxicities were used to develop the models. We test the resulting relationships to determine their ability to predict the toxicity of several protegrin-like peptides. The developed QSARs provide insight into the mechanism of cytotoxic action of antimicrobial peptides. In a subsequent blind test, the QSAR correctly ranked four of five protegrin analogues newly synthesized and tested for toxicity.

Structure–permeation relationships for the non-invasive transdermal delivery of cationic peptides by iontophoresis

Transdermal iontophoresis enables the controlled, non-invasive administration of peptide therapeutics. The aims of this study were (i) to evaluate the effect of amino acid sequence and the spatial distribution of peptide physicochemical properties on electrotransport, and (ii) to develop a quantitative model to predict peptide transport rates. Experimental results showed that the distribution of molecular properties over the peptide surface significantly affected iontophoretic delivery: different arrangements of the same residues resulted in different transport behavior. Computational studies generated three-dimensional quantitative structure–permeation relationships (3D-QSPR) based on 3D descriptors. The model predicted that iontophoresis was favored by peptide hydrophilicity but hindered by voluminous, localized hydrophobicity. Molecular characteristics that favor electrotransport are the converse of those required for passive diffusion across biological membranes. The data represent the first analysis of peptide electrotransport in terms of the spatial distribution of molecular properties and provide insight into the ab initio prediction of transdermal iontophoretic peptide delivery.

Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.

Structure-Activity Relationship Study of Antimicrobial Dermaseptin S4 Showing the Consequences of Peptide Oligomerization on Selective Cytotoxicity

To understand how peptide organization in aqueous solution might affect the activity of antimicrobial peptides, the potency of various dermaseptin S4 analogs was assessed against human red blood cells (RBC), protozoa, and several Gram-negative bacteria. Dermaseptin S4 had weak antibacterial activity but potent hemolytic or antiprotozoan effects. K(4)K(20)-S4 was 2-3-fold more potent against protozoa and RBC, yet K(4)K(20)-S4 was more potent by 2 orders of magnitude against bacteria. K(4)-S4 had similar behavior as K(4)K(20)-S4, but K(20)-S4 and analogous negative charge substitutions were as active as dermaseptin S4 or had reduced activity. Binding experiments suggested that potency enhancement was not the result of increased affinity to target cells. In contrast, potency correlated well with aggregation properties. Fluorescence studies indicated that K(20)-S4 and all negative charge substitutions were as aggregated as dermaseptin S4, whereas K(4)-S4 and K(4)K(20)-S4 were clearly less aggregated. Overall, the data indicated that N-terminal domain interaction between dermaseptin S4 monomers is responsible for the peptide's oligomerization in solution and, hence, for its limited spectrum of action. Moreover, bell-shaped dose-response profiles obtained with bacteria but not with protozoa or RBC implied that aggregation can have dramatic consequences on antibacterial activity. Based on these results, we tested the feasibility of selectivity reversal in the activity of dermaseptin S4. Tampering with the composition of the hydrophobic domains by reducing hydrophobicity or by increasing the net positive charge affected dramatically the peptide's activity and resulted in various analogs that displayed potent antibacterial activity but reduced hemolytic activity. Among these, maximal antibacterial activity was displayed by a 15-mer version that was more potent by 2 orders of magnitude compared with native dermaseptin S4. These results emphasize the notion that peptide-based antibiotics represent a highly modular synthetic antimicrobial system and provide indications of how the peptide's physico-chemical properties affect potency and selectivity.

Stationary phases for peptide analysis by high performance liquid chromatography: a review

A survey is given of modern stationary phases employed in high performance liquid chromatography (HPLC) analysis of peptides. The physico-chemical properties of peptides and their consequences for the selection and optimization of the separation system are briefly discussed, followed by a summary of the approaches to the selection and characterization of stationary phases. The properties and applicability of various stationary phases are then critically reviewed, including aspects such as size-exclusion, ion-exchange, reversed-phase, hydrophobic-interaction, affinity and chiral systems, as well as some specialized separation techniques. Emphasis is placed on the most recent literature.

Physico-chemical properties of peptides and their solutions

Complex investigations of a series of dipeptides and their solutions in H 2O were conducted by the methods of differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), densimetry, and IR and Raman spectroscopy. The results of investigations by DSC and TGA show that the differences in the spatial structure of solid dipeptides of a homologous series sharply influences the character of phase transformations. Linear correlations between the values of the heat capacity of crystalline amino acids and peptides and the number of atoms, number and length of interatomic bonds. The dipeptides studied are classified into three groups: those with predominantly hydrophilic, those with predominantly hydrophobic and those with hydrophobic-hydrophilic (at different concentrations) characters of hydration in aqueous solutions. The ratio between hydrophilic and hydrophobic contributions to the hydration of dipeptides, and role of separate fragments of the molecules is considered.