Proteoforms of ß-Casein, α-s1-Casein, α-Lactalbumin, Serum Albumin, and Lactotransferrin Identified in Different Lactation Phases of Human Milk by Proteomics Approach.

SCOPE: Human milk (HM) has a wide range of proteins with biological and nutritional functions, essential for newborns. The roles of proteins and their proteoforms in HM are not fully understood. This study aims to assess, by 2-DE proteomics, the differential proteoforms in HM, present in colostrum (COL), transition (TRA), and mature milk (MAT), aiming to contribute to understanding neonates' protein needs. METHODS AND RESULTS: HM samples are collected from 39 healthy lactating women. COL presents the higher concentration of essential amino acids. After MALDI-MS/MS and bioinformatics analysis, proteoforms are differentially detected. Abundances of ß-casein (CSN2), α-s1 casein, and α-lactalbumin (LALBA) are higher in MAT; CSN2s are found in 11 spots and the isoforms increase in size as the pI becomes more acidic; regarding LALBA, two variant forms are found with different abundances in TRA and MAT; CSN2, LALBA, lactotransferrin (LTF), and serum albumin forms are present in all lactation phases. CONCLUSION: This study reveals differential proteoforms in COL involved in tissue growth and body development, besides essential amino acids, and, in MAT, involved in muscle mass gain, strengthening of the immune system, and energy production. The results provide new insight about proteoforms involved in maturation of the newborn's organs and systems.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters.

MOTIVATION: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. RESULTS: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AVAILABILITY: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Hevea brasiliensis latex proteomics: a review of analytical methods and the way forward.

Natural rubber or latex from the Hevea brasiliensis is an important commodity in various economic sectors in today's modern society. Proteins have been detected in latex since the early twentieth century, and they are known to regulate various biological pathways within the H. brasiliensis trees such as the natural rubber biosynthesis, defence against pathogens, wound healing, and stress tolerance. However, the exact mechanisms of the pathways are still not clear. Proteomic analyses on latex have found various proteins and revealed how they fit into the mechanisms of the biological pathways. In the past three decades, there has been rapid latex protein identification due to the improvement of latex protein extraction methods, as well as the emergence of two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS). In this manuscript, we reviewed the methods of latex protein extraction that keeps on improving over the past three decades as well as the results of numerous latex protein identification and quantitation.

Proteomics of Galápagos Marine Iguanas Links Function of Femoral Gland Proteins to the Immune System.

Communication between individuals via molecules, termed chemosignaling, is widespread among animal and plant species. However, we lack knowledge on the specific functions of the substances involved for most systems. The femoral gland is an organ that secretes a waxy substance involved in chemical communication in lizards. Although the lipids and volatile substances secreted by the femoral glands have been investigated in several biochemical studies, the protein composition and functions of secretions remain completely unknown. Applying a proteomic approach, we provide the first attempt to comprehensively characterize the protein composition of femoral gland secretions from the Galápagos marine iguana. Using samples from several organs, the marine iguana proteome was assembled by next-generation sequencing and MS, resulting in 7513 proteins. Of these, 4305 proteins were present in the femoral gland, including keratins, small serum proteins, and fatty acid-binding proteins. Surprisingly, no proteins with discernible roles in partner recognition or inter-species communication could be identified. However, we did find several proteins with direct associations to the innate immune system, including lysozyme C, antileukoproteinase (ALP), pulmonary surfactant protein (SFTPD), and galectin (LGALS1) suggesting that the femoral glands function as an important barrier to infection. Furthermore, we report several novel anti-microbial peptides from the femoral glands that show similar action against Escherichia coli and Bacillus subtilis such as oncocin, a peptide known for its effectiveness against Gram-negative pathogens. This proteomics data set is a valuable resource for future functional protein analysis and demonstrates that femoral gland secretions also perform functions of the innate immune system.

ß-glucosidase from Hevea brasiliensis seeds: Purification, homology modeling, and insights into the substrate-binding model.

The isolation of ß-glucosidase from Hevea brasiliensis (Hbglu) seeds was investigated and a homology model was built on the MODELLER software to understand the structure feature. The quality of the model was evaluated on PROCHEK. The refined model was used for molecular docking on AutoDock 4.2 to determine the substrate- binding sites and potential substrates based on their calculated binding affinities. The substrate specificity of Hbglu was verified through the kinetic measurement of hydrolytic activities. Molecular dynamic simulations of cyanogenic ß-glucosidase and ligand-bound complex showed that the free energy (ΔG) for the binding of p-nitrophenyl-ß-D-glucopyranoside and daidzein-7-O-ß-D-glucoside were -8.6 and -7.92 kcal/mol, respectively. Thus, daidzein-7-O-ß-D-glucoside is a potential substrate. Future studies on the physicochemical properties and catalytic mechanisms will provide information on the molecular biological properties of Hbglu. PRACTICAL APPLICATIONS: This study reported the 3D structural simulation of ß-glucosidase from Hbglu. The docking condition between Hbglu and various different substrates were assessed on Autodock. The results can be used as reference in designing enzymes, and improving the utilization of ß-glucosidases for the hydrolysis of flavor precursors from fruits, teas, and wines and for the production of flavonoid compounds and cyanogenic glycoside degradation.

The mRNA-bound Proteome of Leishmania mexicana: Novel Genetic Insight into an Ancient Parasite.

Leishmania parasite infections, termed the leishmaniases, cause significant global infectious disease burden. The lifecycle of the parasite embodies three main stages that require precise coordination of gene regulation to survive environmental shifts between sandfly and mammalian hosts. Constitutive transcription in kinetoplastid parasites means that gene regulation is overwhelmingly reliant on post-transcriptional mechanisms, yet strikingly few Leishmania trans-regulators are known. Using optimized crosslinking and deep, quantified mass spectrometry, we present a comprehensive analysis of 1400 mRNA binding proteins (mRBPs) and whole cell proteomes from the three main Leishmania lifecycle stages. Supporting the validity, although the crosslinked RBPome is magnitudes more enriched, the protein identities of the crosslinked and non-crosslinked RBPomes were nearly identical. Moreover, multiple candidate RBPs were endogenously tagged and found to associate with discrete mRNA target pools in a stage-specific manner. Results indicate that in L. mexicana parasites, mRNA levels are not a strong predictor of the whole cell expression or RNA binding potential of encoded proteins. Evidence includes a low correlation between transcript and corresponding protein expression and stage-specific variation in protein expression versus RNA binding potential. Unsurprisingly, RNA binding protein enrichment correlates strongly with relative replication efficiency of the specific lifecycle stage. Our study is the first to quantitatively define and compare the mRBPome of multiple stages in kinetoplastid parasites. It provides novel, in-depth insight into the trans-regulatory mRNA:Protein (mRNP) complexes that drive Leishmania parasite lifecycle progression.

Production optimization, purification, expression, and characterization of a novel α-L-arabinofuranosidase from Paenibacillus polymyxa

Background: α-L-Arabinofuranosidase (EC 3.2.1.55) catalyzes the hydrolysis of terminal α-L-1,2-, -1,3-, and -1,5- arabinofuranosyl residues in arabinose-containing polymers, and hence, it plays an important role in hemicellulose degradation. Herein, the bacterium Paenibacillus polymyxa, which secretes arabinofuranosidase with high activity, was selected for enzyme production, purification, and characterization. Results: Medium components and cultural conditions were optimized by the response surface method using shake flask cultures. Arabinofuranosidase production reached 25.2 U/mL under optimized conditions, which were pH 7.5, 28°C, and a basic medium supplemented with 1.5 g/L mannitol and 3.5 g/L soymeal. Furthermore, the arabinofuranosidase secreted by P. polymyxa, named as PpAFase-1, was partially purified from the supernatant using a DEAE Sepharose Fast Flow column and a hydroxyapatite column. The approximate molecular mass of the purified PpAFase-1 was determined as 56.8 kDa by SDS-PAGE. Protein identification by mass spectrometry analysis showed that the deduced amino acid sequence had significant similarity to the glycosyl hydrolase family 51. The deduced gene of 1515 bp was cloned and expressed in Escherichia coli BL21 (DE3) cells. Purified recombinant PpAFase-1 was active toward p-nitrophenyl-α-L-arabinofuranoside (pNPAraf). The Km and kcat values toward pNPAraf were 0.81 mM and 53.2 s−1 , respectively. When wheat arabinoxylan and oat spelt xylan were used as substrates, PpAFase-1 showed poor efficiency. However, a synergistic effect was observed when PpAFase-1 was combined with xylanase from Thermomyces lanuginosus. Conclusion: A novel GH51 enzyme PpAFase-1 was cloned from the genome of P. polymyxa and expressed in E. coli. This enzyme may be suitable for hemicellulose degradation on an industrial scale.

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. RESULTS: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. CONCLUSIONS: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

La proteomica n la era postgenómica / The Proteomics In The Postgenomic Era

El principal desafío de la biología moderna es entender la expresión, función y regulación del conjunto completo de proteínas codificadas por un organismo, lo cual describe el objetivo del nuevo campo de la proteómica. Las proteínas son las efectoras del trabajo celular, por ello el estudio de sus perfiles globales de expresión y de sus cambios bajo determinadas condiciones fisiológicas o patológicas, permite entender la red compleja de interacciones en que se basa el funcionamiento de una célula. La electroforesis en dos dimensiones (2D-PAGE) es la técnica central de la proteómica. En la actualidad no existe otro método con la capacidad para resolver simultáneamente miles de proteínas en un solo procedimiento y para detectar modificaciones post y co-traduccionales imposibles de predecir a partir de la secuencia genómica. Sus aplicaciones incluyen el análisis de proteomas, señalización, detección de marcadores de enfermedades y cáncer.

The main challenge of modern biology is to understand the expression, function and regulation of the whole set of proteins codified by an organism, which is the objective of the new field of proteomics. Proteins are the effectors of cellular work and the knowledge of their global expression profiles and changes under physiological and pathological conditions can help us to understand the complex network of interactions involved in cellular function. Two-dimensional electrophoresis (2-DE) is the central technology in proteomics. At present no other technique has the throughput and high resolution of 2-DE for the separation of thousands of proteins in one procedure and for the analysis of post-and co-translation modifications, not predictable from the genome sequence. The scope of applications extends from proteome analysis, to cell signaling, disease markers and cancer.