A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

Uncovering potential host proteins and pathways that may interact with eukaryotic short linear motifs in viral proteins of MERS, SARS and SARS2 coronaviruses that infect humans.

A coronavirus pandemic caused by a novel coronavirus (SARS-CoV-2) has spread rapidly worldwide since December 2019. Improved understanding and new strategies to cope with novel coronaviruses are urgently needed. Viruses (especially RNA viruses) encode a limited number and size (length of polypeptide chain) of viral proteins and must interact with the host cell components to control (hijack) the host cell machinery. To achieve this goal, the extensive mimicry of SLiMs in host proteins provides an effective strategy. However, little is known regarding SLiMs in coronavirus proteins and their potential targets in host cells. The objective of this study is to uncover SLiMs in coronavirus proteins that are present within host cells. These SLiMs have a high possibility of interacting with host intracellular proteins and hijacking the host cell machinery for virus replication and dissemination. In total, 1,479 SLiM hits were identified in the 16 proteins of 590 coronaviruses infecting humans. Overall, 106 host proteins were identified that may interact with SLiMs in 16 coronavirus proteins. These SLiM-interacting proteins are composed of many intracellular key regulators, such as receptors, transcription factors and kinases, and may have important contributions to virus replication, immune evasion and viral pathogenesis. A total of 209 pathways containing proteins that may interact with SLiMs in coronavirus proteins were identified. This study uncovers potential mechanisms by which coronaviruses hijack the host cell machinery. These results provide potential therapeutic targets for viral infections.

Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.

BACKGROUND: Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. RESULTS: In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. CONCLUSIONS: Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

The Evolution of Duplicated Genes of the Cpi-17/Phi-1 (ppp1r14) Family of Protein Phosphatase 1 Inhibitors in Teleosts.

The Cpi-17 (ppp1r14) gene family is an evolutionarily conserved, vertebrate specific group of protein phosphatase 1 (PP1) inhibitors. When phosphorylated, Cpi-17 is a potent inhibitor of myosin phosphatase (MP), a holoenzyme complex of the regulatory subunit Mypt1 and the catalytic subunit PP1. Myosin phosphatase dephosphorylates the regulatory myosin light chain (Mlc2) and promotes actomyosin relaxation, which in turn, regulates numerous cellular processes including smooth muscle contraction, cytokinesis, cell motility, and tumor cell invasion. We analyzed zebrafish homologs of the Cpi-17 family, to better understand the mechanisms of myosin phosphatase regulation. We found single homologs of both Kepi (ppp1r14c) and Gbpi (ppp1r14d) in silico, but we detected no expression of these genes during early embryonic development. Cpi-17 (ppp1r14a) and Phi-1 (ppp1r14b) each had two duplicate paralogs, (ppp1r14aa and ppp1r14ab) and (ppp1r14ba and ppp1r14bb), which were each expressed during early development. The spatial expression pattern of these genes has diverged, with ppp1r14aa and ppp1r14bb expressed primarily in smooth muscle and skeletal muscle, respectively, while ppp1r14ab and ppp1r14ba are primarily expressed in neural tissue. We observed that, in in vitro and heterologous cellular systems, the Cpi-17 paralogs both acted as potent myosin phosphatase inhibitors, and were indistinguishable from one another. In contrast, the two Phi-1 paralogs displayed weak myosin phosphatase inhibitory activity in vitro, and did not alter myosin phosphorylation in cells. Through deletion and chimeric analysis, we identified that the difference in specificity for myosin phosphatase between Cpi-17 and Phi-1 was encoded by the highly conserved PHIN (phosphatase holoenzyme inhibitory) domain, and not the more divergent N- and C- termini. We also showed that either Cpi-17 paralog can rescue the knockdown phenotype, but neither Phi-1 paralog could do so. Thus, we provide new evidence about the biochemical and developmental distinctions of the zebrafish Cpi-17 protein family.

Immunolocalization of corneous beta proteins of the Epidermal Differentiation Complex in the developing claw of the alligator.

Knowledge on the sharpness, mechanical and hydration resistance of the corneous material of claws requires information on its constituent proteins. The present immunohistochemical study has localized some of the main corneous beta proteins (CBPs, formerly termed beta-keratins) indicated to be present in alligator claws only by genomic data. Using specific antibodies we show the immunolocalization of representative claws CBPs of the Epidermal Differentiation Complex (Beta A1 group) during late stages of claw development in alligator. Intense but asymmetric proliferation, revealed by 5BrdU-immunolabeling, determines the formation of a curved dorsal part (unguis) and a linear ventral part (sub-unguis). The large beta-cells generated in the unguis and their packing into a solid corneous layer occur before thinner beta-cells appear in the sub-unguis. In the latter, CBPs are also immune-detected but with less intensity compared to the unguis, and corneocytes remain separated and desquamate. It is suggested that at the tip of the developing claw beta-corneocytes move downward into the initial part of the sub-unguis. This circular movement contributes to sharpen the claw as these cells fully cornify and are desquamated from the sub-unguis. Corneocytes of the unguis contain 10-16 kDa proline-serine-rich proteins that also possess high percentages of glycine, cysteine, tyrosine, valine and leucine. Cysteines likely give rise to numerous SS bonds in the constituent hard horny material, tyrosine contribute to packing proteins into a dense horny material while glycine, valine and leucine increase the hydrophobic property of claws in these water-adapted predators.

Isolation and characterization of microvesicles from mesenchymal stem cells.

Mesenchymal stem or stromal cells are currently under clinical investigation for multiple diseases. While their mechanism of action is still not fully elucidated, vesicles secreted by MSCs are believed to recapitulate their therapeutic potentials to some extent. Microvesicles (MVs), also called as microparticles or ectosome, are among secreted vesicles that could transfer cytoplasmic cargo, including RNA and proteins, from emitting (source) cells to recipient cells. Given the importance of MVs, we here attempted to establish a method to isolate and characterize MVs secreted from unmodified human bone marrow derived MSCs (referred to as native MSCs, and their microvesicles as Native-MVs) and IFNγ stimulated MSCs (referred to as IFNγ-MSCs, and their microvesicles as IFNγ-MVs). We first describe an ultracentrifugation technique to isolate MVs from the conditioned cell culture media of MSCs. Next, we describe characterization and quality control steps to analyze the protein and RNA content of MVs. Finally, we examined the potential of MVs to exert immunomodulatory effects through induction of regulatory T cells (Tregs). Secretory vesicles from MSCs are promising alternatives for cell therapy with applications in drug delivery, regenerative medicine, and immunotherapy.

An in silico investigation of phytochemicals as potential inhibitors against non-structural protein 1 from dengue virus 4

Dengue fever has emerged as a big threat to human health since the last decade owing to high morbidity with considerable mortalities. The proposed study aims at the in silico investigation of the inhibitory action against DENV4-NS1 of phytochemicals from two local medicinal plants of Pakistan. Non-Structural Protein 1 of Dengue Virus 4 (DENV4-NS1) is known to be involved in the replication and maturation of viron in the host cells. A total of 129 phytochemicals (50 from Tanacetum parthenium and 79 from Silybum marianum) were selected for this study. The tertiary structure of DENV4-NS1 was predicted based on homology modelling using Modeller 9.18 and the structural stability was evaluated using molecular dynamics simulations. Absorption, distribution, metabolism, excretion and toxicity (ADMET) along with the drug-likeness was also predicted for these phytochemicals using SwissADME and PreADMET servers. The results of ADMET and drug-likeness predictions exhibited that 54 phytochemicals i.e. 25 from Tanacetum parthenium and 29 from Silybum marianum showed effective druglikeness. These phytochemicals were docked against DENV4-NS1 using AutoDock Vina and 18 most suitable phytochemicals with binding affinities ≤ -6.0 kcal/mol were selected as potential inhibitors for DENV4-NS1. Proposed study also exploits the novel inhibitory action of Jaceidin, Centaureidin, Artecanin, Secotanaparthenolide, Artematin, Schizolaenone B, Isopomiferin, 6, 8-Diprenyleriodictyol, and Anthraxin against dengue virus. It is concluded that the screened 18 phytochemicals have strong inhibition potential against Dengue Virus 4.

Protein expression profiles in Meishan and Duroc sows during mid-gestation reveal differences affecting uterine capacity, endometrial receptivity, and the maternal-fetal Interface.

BACKGROUND: Embryonic mortality is a major concern in the commercial swine industry and primarily occurs early in gestation, but also during mid-gestation (~ days 50-70). Previous reports demonstrated that the embryonic loss rate was significant lower in Meishan than in commercial breeds (including Duroc). Most studies have focused on embryonic mortality in early gestation, but little is known about embryonic loss during mid-gestation. RESULTS: In this study, protein expression patterns in endometrial tissue from Meishan and Duroc sows were examined during mid-gestation. A total of 2170 proteins were identified in both breeds. After statistical analysis, 70 and 114 differentially expressed proteins (DEPs) were identified in Meishan and Duroc sows, respectively. Between Meishan and Duroc sows, 114 DEPs were detected at day 49, and 98 DEPs were detected at day 72. Functional enrichment analysis revealed differences in protein expression patterns in the two breeds. Around half of DEPs were more highly expressed in Duroc at day 49 (DUD49), relative to DUD72 and Meishan at day 49 (MSD49). Many DEPs appear to be involved in metabolic process such as arginine metabolism. Our results suggest that the differences in expression affect uterine capacity, endometrial matrix remodeling, and maternal-embryo cross-talk, and may be major factors influencing the differences in embryonic loss between Meishan and Duroc sows during mid-gestation. CONCLUSIONS: Our data showed differential protein expression pattern in endometrium between Meishan and Duroc sows and provides insight into the development process of endometrium. These findings could help us further uncover the molecular mechanism involved in prolificacy.

Endotracheal tube mucus as a source of airway mucus for rheological study.

Muco-obstructive lung diseases (MOLDs), like cystic fibrosis and chronic obstructive pulmonary disease, affect a spectrum of subjects globally. In MOLDs, the airway mucus becomes hyperconcentrated, increasing osmotic and viscoelastic moduli and impairing mucus clearance. MOLD research requires relevant sources of healthy airway mucus for experimental manipulation and analysis. Mucus collected from endotracheal tubes (ETT) may represent such a source with benefits, e.g., in vivo production, over canonical sample types such as sputum or human bronchial epithelial (HBE) mucus. Ionic and biochemical compositions of ETT mucus from healthy human subjects were characterized and a stock of pooled ETT samples generated. Pooled ETT mucus exhibited concentration-dependent rheologic properties that agreed across spatial scales with reported individual ETT samples and HBE mucus. We suggest that the practical benefits compared with other sample types make ETT mucus potentially useful for MOLD research.

SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization.

Subcellular localization is a main determinant of protein function; however, a global view of cellular proteome organization remains relatively unexplored. We have developed a robust mass spectrometry-based analysis pipeline to generate a proteome-wide view of subcellular localization for proteins mapping to 12,418 individual genes across five cell lines. Based on more than 83,000 unique classifications and correlation profiling, we investigate the effect of alternative splicing and protein domains on localization, complex member co-localization, cell-type-specific localization, as well as protein relocalization after growth factor inhibition. Our analysis provides information about the cellular architecture and complexity of the spatial organization of the proteome; we show that the majority of proteins have a single main subcellular location, that alternative splicing rarely affects subcellular location, and that cell types are best distinguished by expression of proteins exposed to the surrounding environment. The resource is freely accessible via www.subcellbarcode.org.

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design.

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Revising the PLAC8 gene family: from a central role in differentiation, proliferation, and apoptosis in mammals to a multifunctional role in plants.

PLAC8 is a cysteine-rich protein described as a central mediator of tumor evolution in mammals; as such, it represents a promising candidate for diagnostic and therapeutic targeting. The human PLAC8 gene is also involved in contact hypersensitivity response and presents a role in psoriatic skin. In plants, PLAC8 motif-containing proteins are involved in the determination of organ size and growth, response to infection, Ca2+ influx, Cd resistance, and zinc detoxification. In general, PLAC8 motif-containing proteins present the conserved CCXXXXCPC or CLXXXXCPC region. However, there is no devised nomenclature for the PLAC8 motif-containing proteins. Here, through the analysis of 445 sequences, we show that PLAC8 motif-containing proteins constitute a unique gene family, and we propose a unified nomenclature. This is the first report indicating the existence of different groups of PLAC8 proteins, which we have called types I, II, and III. Type I genes are found in mammals, fungi, plants, and algae, and types II and III are exclusive to plants. Our study describes for the first time PLAC8 type III proteins. Whether these sequences maintain their known functional role or possess distinct functions of types I and II genes remains unclear.

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides.

Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by '"in-search assignment of the monoisotopic peak" in the cross-link database search tool Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available data sets. Xi-MPA always delivered the highest number of identifications with ∼2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

Spectral Library Search Improves Assignment of TMT Labeled MS/MS Spectra.

Tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a proven approach for large-scale multiplexed protein quantification. However, the identification of TMT-labeled peptides is compromised by the labeling during traditional sequence database searches. In this study, we aim to use a spectral library search to increase the sensitivity and specificity of peptide identification for TMT-based MS data. Compared to MS/MS spectra of unlabeled peptides, the spectra of TMT-labeled counterparts usually display intensified b ions, suggesting that TMT labeling can alter product ion patterns during MS/MS fragementation. We compiled a human TMT spectral library of 401,168 unique peptides of high quality from millions of peptide-spectrum matches in tens of profiling projects, matching to 14,048 nonredundant proteins (13,953 genes). A mouse TMT spectral library of similar size was also constructed. The libraries were subsequently appended with decoy spectra to evaluate the false discovery rate, which was validated by a simulated null TMT data set. The performance of the library search was further optimized by removing TMT reporter ions and selecting an appropriate library construction method. Finally, we searched a human TMT data set against the spectral library to demonstrate that the spectral library outperformed the sequence database. Both human and mouse TMT libraries were made publicly available to the research community.

Protein Profile of the Acquired Enamel Pellicle after Rinsing with Whole Milk, Fat-Free Milk, and Water: An in vivo Study.

This study detected changes in the protein profile of the acquired enamel pellicle (AEP) formed in vivo after rinsing with whole milk, fat-free milk, or water. Nine subjects in good oral condition took part in the study. The acquired pellicle was formed in the morning, for 120 min, after prophylaxis with pumice. Following this, the volunteers rinsed with 10 mL of whole milk, fat-free milk, or deionized water for 30 s, following a blinded crossover protocol. After 60 min, the pellicle was collected with filter paper soaked in 3% citric acid and processed for analysis by liquid chromatography-electrospray ionization tandem mass spectrometry. The obtained tandem mass spectrometry spectra were searched against a human protein database (Swiss-Prot). The proteomic data related to protein quantification were analysed using the PLGS software. A total of 260 proteins were successfully identified in the AEP samples collected from all groups. Forty-nine were common to all 3 groups, while 72, 62, and 49 were specific to the groups rinsing with whole milk, fat-free milk, and water, respectively. Some were typical components of the AEP, such as cystatin-B, cystatin-SN, isoforms of α-amylase, IgA and IgG, lysozyme C, protein S100 A78, histatin-1, proline-rich protein 27, statherin, and lactotransferrin. Other proteins are not commonly described as part of the AEP but could act in defence of the organism against pathogens. Distinct proteomic profiles were found in the AEP after rinsing with whole or fat-free milk, which could have an impact on bacterial adhesion and tooth dissolution. The use of fat-free milk could favourably modulate the adhesion of bacteria to the AEP as well as biofilm formation when compared with whole milk.

Leucine-rich repeat receptor-like gene screen reveals that Nicotiana RXEG1 regulates glycoside hydrolase 12 MAMP detection.

Activation of innate immunity by membrane-localized receptors is conserved across eukaryotes. Plant genomes contain hundreds of such receptor-like genes and those encoding proteins with an extracellular leucine-rich repeat (LRR) domain represent the largest family. Here, we develop a high-throughput approach to study LRR receptor-like genes on a genome-wide scale. In total, 257 tobacco rattle virus-based constructs are generated to target 386 of the 403 identified LRR receptor-like genes in Nicotiana benthamiana for silencing. Using this toolkit, we identify the LRR receptor-like protein Response to XEG1 (RXEG1) that specifically recognizes the glycoside hydrolase 12 protein XEG1. RXEG1 associates with XEG1 via the LRR domain in the apoplast and forms a complex with the LRR receptor-like kinases BAK1 and SOBIR1 to transduce the XEG1-induced defense signal. Thus, this genome-wide silencing assay is demonstrated to be an efficient toolkit to pinpoint new immune receptors, which will contribute to developing durable disease resistance.

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection.

The Golgi Apparatus (GA) is a key organelle for protein synthesis within the eukaryotic cell. The main task of GA is to modify and sort proteins for transport throughout the cell. Proteins permeate through the GA on the ER (Endoplasmic Reticulum) facing side (cis side) and depart on the other side (trans side). Based on this phenomenon, we get two types of GA proteins, namely, cis-Golgi protein and trans-Golgi protein. Any dysfunction of GA proteins can result in congenital glycosylation disorders and some other forms of difficulties that may lead to neurodegenerative and inherited diseases like diabetes, cancer and cystic fibrosis. So, the exact classification of GA proteins may contribute to drug development which will further help in medication. In this paper, we focus on building a new computational model that not only introduces easy ways to extract features from protein sequences but also optimizes classification of trans-Golgi and cis-Golgi proteins. After feature extraction, we have employed Random Forest (RF) model to rank the features based on the importance score obtained from it. After selecting the top ranked features, we have applied Support Vector Machine (SVM) to classify the sub-Golgi proteins. We have trained regression model as well as classification model and found the former to be superior. The model shows improved performance over all previous methods. As the benchmark dataset is significantly imbalanced, we have applied Synthetic Minority Over-sampling Technique (SMOTE) to the dataset to make it balanced and have conducted experiments on both versions. Our method, namely, identification of sub-Golgi Protein Types (isGPT), achieves accuracy values of 95.4%, 95.9% and 95.3% for 10-fold cross-validation test, jackknife test and independent test respectively. According to different performance metrics, isGPT performs better than state-of-the-art techniques. The source code of isGPT, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/isGPT.

Classification of ß-hairpin repeat proteins.

In recent years, a number of new protein structures that possess tandem repeats have emerged. Many of these proteins are comprised of tandem arrays of ß-hairpins. Today, the amount and variety of the data on these ß-hairpin repeat (BHR) structures have reached a level that requires detailed analysis and further classification. In this paper, we classified the BHR proteins, compared structures, sequences of repeat motifs, functions and distribution across the major taxonomic kingdoms of life and within organisms. As a result, we identified six different BHR folds in tandem repeat proteins of Class III (elongated structures) and one BHR fold (up-and-down ß-barrel) in Class IV ("closed" structures). Our survey reveals the high incidence of the BHR proteins among bacteria and viruses and their possible relationship to the structures of amyloid fibrils. It indicates that BHR folds will be an attractive target for future structural studies, especially in the context of age-related amyloidosis and emerging infectious diseases. This work allowed us to update the RepeatsDB database, which contains annotated tandem repeat protein structures and to construct sequence profiles based on BHR structural alignments.

Exigência de proteína bruta para juvenis de curimatã-pacu / Crude protein requeriment to curimatã-pacu juveniles

O objetivo deste estudo foi determinar a exigência de proteína bruta (PB) de juvenis de curimatã-pacu (Prochilodus argenteus). O experimento foi conduzido por um período de 60 dias, utilizando um delineamento inteiramente ao acaso, com quatro tratamentos (dieta contendo 24,0; 28,0; 32,0 e 36,0% de PB) e cinco repetições cada, em que 100 juvenis (peso inicial de 0,33 ± 0,01g) foram distribuídos em 20 aquários experimentais com volume de 130L, sendo considerada como unidade experimental uma caixa com cinco peixes, sob recirculação de água. Avaliaram-se o desempenho produtivo, o crescimento heterogêneo, os parâmetros morfométricos, fisiológicos e a viabilidade econômica das rações em função do nível de PB. Pela análise de regressão, foi verificado um aumento linear nos parâmetros de desempenho, morfométricos e fisiológicos, com o aumento do nível proteico nas rações. Quando comparados pelo teste de Tukey, o melhor resultado (P>0,05) foi obtido com 36% de PB. Verificou-se menor heterogeneidade de crescimento dos peixes à medida que se aumentavam os níveis de PB na ração. Assim, considerando também a viabilidade econômica, recomenda-se a utilização de rações com 36% de proteína bruta para juvenis de curimatã-pacu.(AU)

The aim of this study was to determinate crude protein (CP) requirement to curimatã-pacu juveniles (Prochilodus argenteus). The experiment was conducted during 60 days in a completely randomized design, with four treatments (24.0, 28.0, 32.0 and 36.0% CP of diet) and five replications, where a hundred juvenile fishes (initial weight de 0.33 ± 0.01g), were distributed in twenty tanks (130L each). Each tank with five juveniles was considered an experimental unit, with water recirculation. Performance, heterogeneous growth, morphometric and physiological parameters, and economic viability of the rations according to the CP levels were evaluated. For the regression analysis, a linear increase in performance, heterogeneous growth and morphometric and physiological parameters with increased of the protein in rations were observed. When compared by the Tukey test (P>0.05), the best results were obtained with 36% CP. There was lower fish growth heterogeneity as the levels of CP in the diet increased. Thus, taking into consideration the economic viability it is recommended to use feed containing 36% of crude protein to curimatã-pacu juveniles.(AU)

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group.

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it difficult to discuss the group as a whole and to place new sequences into the appropriate context. Developing a reference that prioritizes important sequence features related to structure and/or activity can be used in addition to established names to add some coherency to a diverse group of proteins. This paper uses the cysteine-stabilized alpha-helix (CS-αß) superfamily as an example to show how a reference generated in spreadsheet software can clarify relationships between existing proteins in the superfamily, as well as facilitate the addition of new sequences. It also shows how the reference can help to refine sequence alignments generated in commonly used software, which impacts the validity of phylogenetic analyses. The use of a reference will likely be most helpful for protein groups that include highly divergent sequences from a broad spectrum of taxa, with features that are not adequately captured by molecular analyses.