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Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Multivalent biopolymers phase separate into membrane-less organelles (MLOs) which exhibit liquid-like behavior. Here, we explore formation of prototypical MOs from multivalent proteins on various time and length scales and show that the kinetically arrested metastable multi-droplet state is a dynamic outcome of the interplay between two competing processes: a diffusion-limited encounter between proteins, and the exhaustion of available valencies within smaller clusters. Clusters with satisfied valencies cannot coalesce readily, resulting in metastable, long-living droplets. In the regime of dense clusters akin to phase-separation, we observe co-existing assemblies, in contrast to the single, large equilibrium-like cluster. A system-spanning network encompassing all multivalent proteins was only observed at high concentrations and large interaction valencies. In the regime favoring large clusters, we observe a slow-down in the dynamics of the condensed phase, potentially resulting in loss of function. Therefore, metastability could be a hallmark of dynamic functional droplets formed by sticker-spacer proteins., Competing Interests: SR, ES No competing interests declared, (© 2020, Ranganathan and Shakhnovich.)

Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation., Competing Interests: The authors declare no conflict of interest.

The arrangement of amino acids in a protein sequence encodes its native folding. However, the same arrangement in aggregation-prone regions may cause misfolding as a result of local environmental stress. Under normal physiological conditions, such regions congregate in the protein's interior to avoid aggregation and attain the native fold. We have used solvent accessibility of aggregation patches (SAAP p ) to determine the packing of aggregation-prone residues. Our results showed that SAAP p has low values for native crystal structures, consistent with protein folding as a mechanism to minimize the solvent accessibility of aggregation-prone residues. SAAP p also shows an average correlation of 0.76 with the global distance test (GDT) score on CASP12 template-based protein models. Using SAAP p scores and five structural features, a random forest machine learning quality assessment tool, SAAP-QA, showed 2.32 average GDT loss between best model predicted and actual best based on GDT score on independent CASP test data, with the ability to discriminate native-like folds having an AUC of 0.94. Overall, the Pearson correlation coefficient (PCC) between true and predicted GDT scores on independent CASP data was 0.86 while on the external CAMEO dataset, comprising high quality protein structures, PCC and average GDT loss were 0.71 and 4.46 respectively. SAAP-QA can be used to detect the quality of models and iteratively improve them to native or near-native structures.

Acquiring a divergent response from homologous protein domains is essential for selective ligand-protein interactions. Stereospecific fluorination of (-)-balanol, an ATP mimic, uncovers a new source of selectivity from integrated chemical and conformational perturbation that differentiates homologous sites by the level of congruency in their response to local and remote fluorine effects.

This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

Background: Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding., Results: Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes., Conclusions: Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

Background: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology., Results: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments., Conclusions: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

The Automated Protein Structure Analysis (APSA) method, which describes the protein backbone as a smooth line in three-dimensional space and characterizes it by curvature kappa and torsion tau as a function of arc length s, was applied on 77 proteins to determine all secondary structural units via specific kappa(s) and tau(s) patterns. A total of 533 alpha-helices and 644 beta-strands were recognized by APSA, whereas DSSP gives 536 and 651 units, respectively. Kinks and distortions were quantified and the boundaries (entry and exit) of secondary structures were classified. Similarity between proteins can be easily quantified using APSA, as was demonstrated for the roll architecture of proteins ubiquitin and spinach ferridoxin. A twenty-by-twenty comparison of all alpha domains showed that the curvature-torsion patterns generated by APSA provide an accurate and meaningful similarity measurement for secondary, super secondary, and tertiary protein structure. APSA is shown to accurately reflect the conformation of the backbone effectively reducing three-dimensional structure information to two-dimensional representations that are easy to interpret and understand., (2008 Wiley-Liss, Inc.)

Small disulfide-bonded proteins (SDPs) are rich sources for therapeutic drugs. Designing drugs from these proteins requires three-dimensional structural information, which is only available for a subset of these proteins. SDPMOD addresses this deficit in structural information by providing a freely available automated comparative modeling service to the research community. For expert users, SDPMOD offers a manual mode that permits the selection of a desired template as well as a semi-automated mode that allows users to select the template from a suggested list. Besides the selection of templates, expert users can edit the target-template alignment, thus allowing further customization of the modeling process. Furthermore, the web service provides model stereochemical quality evaluation using PROCHECK. SDPMOD is freely accessible to academic users via the web interface at http://proline.bic.nus.edu.sg/sdpmod.

Xpro is a relational database that contains all the eukaryotic protein-encoding DNA sequences contained in GenBank with associated data required for the analysis of eukaryotic gene architecture. In addition to the information found in the GenBank records, which includes properties such as sequence, position, length and description about introns, exons and protein-coding regions, Xpro provides annotations on the splice sites and intron phases. Furthermore, Xpro validates intron positions using alignment information between the record's sequence and EST sequences found in dbEST. In the process of validation, alternative splicing information is also obtained and can be found in the database. The intron-containing genes in the Xpro are also classified as experimental or predicted based on the intron position validation and specific keywords in the GenBank records that are present in predicted genes. An Entrez-like query system, which is familiar to most biologists, is provided for accessing the information present in the database system. A non-redundant set of Xpro database contents is also obtained by cross-referencing to the Swiss-Prot/TrEMBL and Pfam databases. The database currently contains information for 493,983 genes--351,918 intron- containing genes and 142,065 intron-less genes. Xpro is updated for each new GenBank release and is freely available via the internet at http://origin.bic. nus.edu.sg/xpro.

Two possibilities exist for the evolution of individual enzymes/proteins from a milieu of amino acids, one based on preference and selectivity and the other on the basis of random events. Logic is overwhelmingly in favour of the former. By protein data base analysis and experiments, we have provided data to show the manifestation of two types of preferences, namely, the choice of the neighbour and its acceptance from the amino end (left) or the carboxyl end (right). The study tends to show that if the 20 proteinous amino acids were made to combine in water, the resulting profile would be nonrandom. Such selectivity could be a factor in protein evolution.

Summary: The relationship between intron distribution in the eukaryotic gene and protein structural elements is essential for understanding the origin and evolution of genes. XdomView is a web-based viewer mapping protein structural domains and intron positions in eukaryotic homologues to its tertiary structure. The association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for eukaryotic gene organization, gene evolution, protein folding and protein structure classification., Availability: Freely available from http://surya.bic.nus.edu.sg/xdom.

The low density lipoprotein receptor-related protein-2/megalin (LRP-2) is an endocytic receptor that is expressed on the apical surfaces of epithelial cells lining specific regions of the male and female reproductive tracts. In the present study, immunohistochemical staining revealed that LRP-2 is also expressed by epithelial cells lining the ductal region and the ampulla of the rat seminal vesicle. To identify LRP-2 ligands in the seminal vesicle, we probed seminal vesicle fluid with 125I-labeled LRP-2 in a gel-blot overlay assay. A 100-kDa protein (under non-reducing conditions) was found to bind the radiolabeled receptor. The protein was isolated and subjected to protease digestion, and the proteolytic fragments were subjected to mass spectroscopic sequence analysis. As a result, the 100-kDa protein was identified as the seminal vesicle secretory protein II (SVS-II), a major constituent of the seminal coagulum. Using purified preparations of SVS-II and LRP-2, solid-phase binding assays were used to show that the SVS-II bound to the receptor with high affinity (Kd = 5.6 nM). The binding of SVS-II to LRP-2 was inhibited using a known antagonist of LRP-2 function, the 39-kDa receptor-associated protein RAP. Using a series of recombinant subfragments of SVS-II, the LRP-2 binding site was mapped to a stretch of repeated 13-residue modules located in the central portion of the SVS-II polypeptide. To evaluate the ability of LRP-2 to mediate 125I-SVS-II endocytosis and lysosomal degradation, ligand clearance assays were performed using differentiated mouse F9 cells, which express high levels of LRP-2. Radiolabeled SVS-II was internalized and degraded by the cells, and both processes were inhibited by antibodies to LRP-2 or by RAP. The results indicate that LRP-2 binds SVS-II and can mediate its endocytosis leading to lysosomal degradation.

The effect of leptin on glucose transport, lipogenesis, and lipoprotein lipase activity was studied in cultured rat adipocytes and 3T3-L1 adipocytes. Leptin had no effect on basal and insulin stimulated glucose transport in isolated adipocytes from the rat and the genetically obese mouse. The incorporation of glucose into lipids was also unaffected. Lipoprotein lipase (LPL) activity remained unchanged in response to leptin in these cells, as well as in minced adipose tissue. Leptin also had no effect on both basal and insulin-stimulated glucose transport in cultured rat and human skeletal muscle cells. These studies showed that leptin had no effect on glucose transport, lipoprotein lipase activity, and insulin action in fat and muscle cells in vitro.

Elevated plasma leptin levels are found in obese humans, suggesting a defect in the function of leptin in regulating body weight and adiposity. In 53 subjects covering a broad range of adiposity, we examined the relationships between plasma leptin, adipose tissue ob mRNA levels, and adipose tissue TNF mRNA. There was a highly significant correlation between plasma leptin levels and every index of adiposity. In contrast, the relationship between ob mRNA levels and adiposity was weak. Adipose tissue from obese subjects demonstrated higher ob mRNA levels than adipose tissue from lean subjects (lean: 0.49+/-0.05; obese 0.87+/-0.09 arbitrary units, P< 0.05). However, there was no significant correlation between body fat and ob mRNA level. In addition, there was no significant relationship between ob mRNA levels and plasma leptin levels, which were measured in the same subjects. In addition to the measure of ob mRNA levels, adipose TNF mRNA levels were measured in 18 subjects. TNF mRNA levels varied with ob mRNA levels (r = 0.44, P = 0.06). These data show that plasma leptin levels are not directly related to adipose tissue ob mRNA levels, suggesting posttranscriptional regulation of leptin expression, either at the level of the adipocyte, or by alteration of plasma leptin degradation or clearance. In addition, the parallel changes in ob and TNF mRNA in adipose tissue suggest that these two important factors in the defense against obesity may be regulated similarly.

Leptin, the gene product of the obese gene, may play an important role in regulating body weight by signalling the size of the adipose tissue mass. Plasma leptin was found to be highly correlated with body mass index (BMI) in rodents and in 87 lean and obese humans. In humans, there was variability in plasma leptin at each BMI suggesting that there are differences in its secretion rate from fat. Weight loss due to food restriction was associated with a decrease in plasma leptin in samples from mice and obese humans.

Earlier studies have revealed a distinct class of regulatory proteins known as trans-activator proteins in diverse biological systems. These proteins have been shown to act on both homologous and heterologous promoter targets. Activation of heterologous targets is speculated to be an integral part of virus-induced pathogenesis. To verify this hypothesis, stable Tat-producing human rhabdomyosarcoma (RD) cell lines were generated. These cell lines produced significant levels of functional Tat, as measured by transfection with the reporter plasmid pLTR-CAT. Tat-producing cells, although morphologically similar to the control, exhibited a slower growth rate. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of the cellular proteins from control (tat-) and tat+ cells revealed increased quantities of 34- and 40-kD proteins along with the appearance of a new 74-kD protein in tat+ cells. Subsequent two-dimensional gel analysis revealed several additional differences. Tat+ cell lines produced two proteins of M(r) 19.5 and 44 kD anew, while proteins with M(r) 14.5, 42, and 52.5 kD were in greater abundance. Interestingly, a 26-kD protein that was originally present in the G418+/tat- (control) sample disappeared in the presence of Tat. These data support a possible modulator role for Tat in cellular gene expression.