The proteins that inhibit peptidases are of great importance in medicine and biotechnology, but there has never been a comprehensive system of classification for them. Some of the terminology currently in use is potentially confusing. In the hope of facilitating the exchange, storage and retrieval of information about this important group of proteins, we now describe a system wherein the inhibitor units of the peptidase inhibitors are assigned to 48 families on the basis of similarities detectable at the level of amino acid sequence. Then, on the basis of three-dimensional structures, 31 of the families are assigned to 26 clans. A simple system of nomenclature is introduced for reference to each clan, family and inhibitor. We briefly discuss the specificities and mechanisms of the interactions of the inhibitors in the various families with their target enzymes. The system of families and clans of inhibitors described has been implemented in the MEROPS peptidase database (http://merops.sanger.ac.uk/), and this will provide a mechanism for updating it as new information becomes available.

Evolutionary families of peptidase inhibitors.

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

/pdf/co-regulatory-networks-of-human-serum-proteins-link-genetics-4ws87mcgc2.pdf

Co-regulatory networks of human serum proteins link genetics to disease

Proteases, though essentially indispensable to the maintenance and survival of their host organisms, can be potentially damaging when overexpressed or present in higher concentrations, and their activities need to be correctly regulated. An important means of regulation involves modulation of their activities through interaction with substances, mostly proteins, called protease inhibitors. Some insects and many of the phytopathogenic microorganisms secrete extracellular enzymes and, in particular, enzymes causing proteolytic digestion of proteins, which play important roles in pathogenesis. Plants, however, have also developed mechanisms to fight these pathogenic organisms. One important line of defense that plants have to fight these pathogens is through various inhibitors that act against these proteolytic enzymes. These inhibitors are thus active in endogenous as well as exogenous defense systems. Protease inhibitors active against different mechanistic classes of proteases have been classified into different families on the basis of significant sequence similarities and structural relationships. Specific protease inhibitors are currently being overexpressed in certain transgenic plants to protect them against invaders. The current knowledge about plant protease inhibitors, their structure and their role in plant defense is briefly reviewed.

	 

	Key words: Proteases, enzymes, protease inhibitors, serpins, cystatins, pathogens, defense.

/pdf/plant-protease-inhibitors-a-defense-strategy-in-plants-43ik3ns4vr.pdf

Plant protease inhibitors: a defense strategy in plants

Kim Worley and colleagues report the whole-genome sequence of the common marmoset, Callithrix jacchus, the first New World monkey to be sequenced.

/pdf/the-common-marmoset-genome-provides-insight-into-primate-57gyfikbt4.pdf

The common marmoset genome provides insight into primate biology and evolution

Serine proteases and their natural protein inhibitors are among the most intensively studied protein complexes. About 20 structurally diverse inhibitor families have been identified, comprising alpha-helical, beta sheet, and alpha/beta proteins, and different folds of small disulfide-rich proteins. Three different types of inhibitors can be distinguished based on their mechanism of action: canonical (standard mechanism) and non-canonical inhibitors, and serpins. The canonical inhibitors bind to the enzyme through an exposed convex binding loop, which is complementary to the active site of the enzyme. The mechanism of inhibition in this group is always very similar and resembles that of an ideal substrate. The non-canonical inhibitors interact through their N-terminal segment. There are also extensive secondary interactions outside the active site, contributing significantly to the strength, speed, and specificity of recognition. Serpins, similarly to the canonical inhibitors, interact with their target proteases in a substrate-like manner; however, cleavage of a single peptide bond in the binding loop leads to dramatic structural changes.

Canonical protein inhibitors of serine proteases

By using sensitive homology-search and gene-finding programs, we have found that a genomic region from the tip of the short arm of human chromosome 16 (16p13.3) encodes a putative secreted protein consisting of a domain related to the whey acidic protein (WAP) domain, a domain homologous with follistatin modules of the Kazal-domain family (FS module), an immunoglobulin-related domain (Ig domain), two tandem domains related to Kunitz-type protease inhibitor modules (KU domains), and a domain belonging to the recently defined NTR-module family (NTR domain). The gene encoding these WAP, FS, Ig, KU, and NTR modules (hereafter referred to as the WFIKKN gene) is intron-depleted--its single 1,157-bp intron splits the WAP module. The validity of our gene prediction was confirmed by sequencing a WFIKKN cDNA cloned from a lung cDNA library. Studies on the tissue-expression pattern of the WFIKKN gene have shown that the gene is expressed primarily in pancreas, kidney, liver, placenta, and lung. As to the function of the WFIKKN protein, it is noteworthy that it contains FS, WAP, and KU modules, i.e., three different module types homologous with domains frequently involved in inhibition of serine proteases. The protein also contains an NTR module, a domain type implicated in inhibition of zinc metalloproteinases of the metzincin family. On the basis of its intriguing homologies, we suggest that the WFIKKN protein is a multivalent protease inhibitor that may control the action of multiple types of serine proteases as well as metalloproteinase(s).

https://www.pnas.org/content/pnas/98/7/3705.full.pdf

A human protein containing multiple types of protease-inhibitory modules

Recently we have described a novel secreted protein (the WFIKKN protein) that consists of multiple types of protease inhibitory modules, including two tandem Kunitz-type protease inhibitor-domains. On the basis of its homologies we have suggested that the WFIKKN protein is a multivalent protease inhibitor that may control the action of different proteases. In the present work we have expressed the second Kunitz-type protease inhibitor domain of the human protein WFIKKN in Escherichia coli, purified it by affinity chromatography on trypsin-Sepharose and its structure was characterized by CD spectroscopy. The recombinant protein was found to inhibit trypsin (Ki = 9.6 nm), but chymotrypsin, elastase, plasmin, pancreatic kallikrein, lung tryptase, plasma kallikrein, thrombin, urokinase or tissue plasminogen activator were not inhibited by the recombinant protein even at 1 μm concentration. In view of the marked trypsin-specificity of the inhibitor it is suggested that its physiological target may be trypsin.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1046/j.1432-1033.2003.03593.x

Expression, purification and characterization of the second Kunitz‐type protease inhibitor domain of the human WFIKKN protein

The DNA in the cells of the human body is usually copied correctly when a cell divides. However, errors (mutations) are sometimes introduced during the copying process. Although the majority of mutations have no major impact on cells, many mutations are harmful: they decrease the ability of cells to survive. There are, however, mutations that can lead to cells dividing more frequently or gaining the ability to spread, which can lead to cancer. These mutations are known as ‘driver mutations’ because they drive the growth of tumors. Since such ‘driver mutations’ provide a growth advantage to tumor cells, they are subject to positive selection, this is, their frequency in the tumor increases over time. Because of their selective advantage, driver mutations accumulate at significantly higher rates than the neutral ‘passenger mutations’ that do not play a role in tumor growth. Genes that carry driver mutations are called driver genes, while genes that carry only passenger mutations are known as passenger genes. Certain genes, however, do not fit into either category. For example, some genes that are essential for tumor growth must get rid of harmful mutations to maintain activity. Mutations of such ‘tumor essential genes’ are thus subject to ‘negative’ or ‘purifying selection’. A major goal of cancer research is to identify genes that play critical roles in tumor growth. Earlier studies have identified numerous driver genes positively selected for driver mutations, exploiting the fact that driver genes show significantly higher mutation rates than passenger genes. Identification of tumor essential genes, however, is inherently more difficult since the paucity of mutations of negatively selected genes hinders the analysis of the mutation data. The failure to provide convincing evidence for negative selection in tumors has led to suggestions that it has no role in cancer evolution. Banyai et al. used a novel approach to address the question of whether negative selection occurs in cancer. Based on characteristic differences in the patterns of mutations in cancer they distinguished clusters of passenger genes, driver genes and tumor essential genes. The group of tumor essential genes includes genes that serve to satisfy the increased demand of rapidly dividing tumor cells for nutrients’ and genes that are essential for cell migration and metastasis (the spread of cancer cells to other areas of the body). The tumor essential genes that Banyai et al. identified may prove to be valuable targets for cancer therapy, illustrating the importance of genome sequencing in cancer research. Identification of additional tumor essential genes is, however, hindered by the fact that they are likely to have low levels of mutations, which can exclude them from meaningful analyses. Progress with genomic sequencing of tumors is expected to overcome this limitation and help identify additional genes that are essential for cancer growth.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes.

Edvards Liepinsh, Alinda Nagy, Maria Trexler, Laszlo Patthy & Gottfried Otting* Department of Medical Biochemistry and Biophysics, Karolinska Institute, S-17177, Stockholm, Sweden; Institute of Organic Synthesis, LV-1006, Riga, Latvia; Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113, Budapest, Hungary; Research School of Chemistry, Australian National University, Canberra, ACT, 0200, Australia

Second Kunitz-type protease inhibitor domain of the human WFIKKN1 protein

Background A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most of the approaches aimed to achieve this goal focused on genes that are positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. Results We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human protein-coding genes. Our analyses have confirmed that the vast majority of human genes do not show detectable signals of selection, whereas tumor suppressor genes are positively selected for inactivating mutations. Oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells. It is worthy of note that the group of negatively selected genes includes several genes that play a central role in the Warburg effect characteristic of cancer cells as well as genes involved in the proliferation, immortalization, invasion and metastasis of tumor cells. Conclusions Our analyses suggest that the approach reported here will promote the identification of numerous novel tumor suppressor genes, oncogenes and pro-oncogenic genes that may serve as targets in cancer therapy.

/pdf/use-of-signals-of-positive-and-negative-selection-to-7597iws2fd.pdf

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

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